Analysis and Management of Animal
... To understand population responses to environmental variation such as climate change at migration areas, and to develop conservation and management actions to mitigate negative impacts of such variation, requires reliable monitoring of stopover population size and phenology. Where individuals can be marked, or otherwise individually identified, capture–recapture methods provide a variety of approaches to estimate population size and demographic rate parameters (Williams, Nichols, and Conroy, 2002 ). Traditional capture–recapture studies involve a series of sampling occasions during which newly captured animals are given individually recognizable marks, identities of previously captured animals are recorded, and typically, all animals are released. ...
... Assumptions for the CMSA superpopulation formulation are similar to those of the Cormack–Jolly–Seber (CJS) model (see Williams et al., 2002 ) and include: (i) homogeneity of entry , persistence, and resighting probabilities for marked and unmarked individuals; (ii) marks are not lost or overlooked; (iii) sampling is instantaneous; (iv) no temporary emigration from the study area; and (v) independence of fates with respect to entry, persistence, and resighting probability for all individuals (Williams et al., 2002). Assumption 1 is critical for abundance estimation with all parameterizations of the JS model (Williams et al., 2002), and is unlikely violated in our case because individuals are " captured " via a resighting process. ...
... Assumptions for the CMSA superpopulation formulation are similar to those of the Cormack–Jolly–Seber (CJS) model (see Williams et al., 2002 ) and include: (i) homogeneity of entry , persistence, and resighting probabilities for marked and unmarked individuals; (ii) marks are not lost or overlooked; (iii) sampling is instantaneous; (iv) no temporary emigration from the study area; and (v) independence of fates with respect to entry, persistence, and resighting probability for all individuals (Williams et al., 2002). Assumption 1 is critical for abundance estimation with all parameterizations of the JS model (Williams et al., 2002), and is unlikely violated in our case because individuals are " captured " via a resighting process. ...
We present a novel formulation of a mark-recapture-resight model that allows estimation of population size, stopover duration, and arrival and departure schedules at migration areas. Estimation is based on encounter histories of uniquely marked individuals and relative counts of marked and unmarked animals. We use a Bayesian analysis of a state-space formulation of the Jolly-Seber mark-recapture model, integrated with a binomial model for counts of unmarked animals, to derive estimates of population size and arrival and departure probabilities. We also provide a novel estimator for stopover duration that is derived from the latent state variable representing the interim between arrival and departure in the state-space model. We conduct a simulation study of field sampling protocols to understand the impact of superpopulation size, proportion marked, and number of animals sampled on bias and precision of estimates. Simulation results indicate that relative bias of estimates of the proportion of the population with marks was low for all sampling scenarios and never exceeded 2%. Our approach does not require enumeration of all unmarked animals detected or direct knowledge of the number of marked animals in the population at the time of the study. This provides flexibility and potential application in a variety of sampling situations (e.g., migratory birds, breeding seabirds, sea turtles, fish, pinnipeds, etc.). Application of the methods is demonstrated with data from a study of migratory sandpipers.
... Both spatial survey effort and desired sample size must be selected by the researcher, but may be informed by previous research, power analyses and/or simulations (Williams et al. 2002). We selected a survey effort of 150 km, a length of transect which we felt provided reasonable coverage of our study site and encompasses 1350 km 2 within 2.5 km of transects, the radius of the average fox home range at DPG (). ...
... Wildlife managers often require estimates of animal abundance (í µí± ̂ ) to evaluate management practices intended to maintain harvested populations or control nuisance species, or to determine the status of imperiled species and assess conservation efforts (Williams et al. 2002, Solberg et al. 2006). Capture-recapture techniques can provide reliable estimates of abundance (Williams et al. 2002, Royle et al. 2014), but conventional methods of capture and recapture (e.g., live-capture) are often expensive and challenging to implement, particularly when working with rare or elusive species (). ...
... Wildlife managers often require estimates of animal abundance (í µí± ̂ ) to evaluate management practices intended to maintain harvested populations or control nuisance species, or to determine the status of imperiled species and assess conservation efforts (Williams et al. 2002, Solberg et al. 2006). Capture-recapture techniques can provide reliable estimates of abundance (Williams et al. 2002, Royle et al. 2014), but conventional methods of capture and recapture (e.g., live-capture) are often expensive and challenging to implement, particularly when working with rare or elusive species (). While these constraints do not preclude the use of traditional capture-recapture techniques, they can limit their practicality over large spatial extents, for extended periods, or when monitoring multiple species concurrently. ...
Resource managers worldwide are challenged to protect sensitive species. The status of many species remains ambiguous, in part due to the difficulty in developing cost-efficient monitoring programs. We used noninvasive genetic sampling (NGS) to investigate two sympatric carnivores in the Great Basin Desert: kit foxes ( Vulpes macrotis ) and coyotes ( Canis latrans ). We developed a conceptual model to optimize NGS design for capture-recapture analyses. We compared statistical classification approaches to field identification (ID) of carnivore scats, and evaluated rates of scat removal to inform noninvasive surveys. To improve efficiency, we developed the ConGenR script to facilitate the determination of consensus genotypes, amplification and genotyping error rates, and genotype matching. We combined NGS with capture-recapture (NGS-CR) analyses to compare likelihood-based abundance estimators. Finally, we combined NGS and occupancy modeling to evaluate coyote and kit fox spatial dynamics. Our results suggested that temporal NGS-CR designs that balanced DNA degradation and sample accumulation reduced costs. Field based scat ID was misleading, but statistical classification provided high accuracy in the absence of molecular ID. Scat removal rates were significantly inflated and influenced survey results at even low levels of disturbance. The choice of estimator and sampling design significantly influenced abundance estimates, and the relationship between estimators varied by species. Occupancy of coyotes and kit foxes were positively and negatively associated with shrubland and woodland cover, respectively. Kit fox probability of local extinction was positively related to coyote activity, yet within an occupied unit, kit foxes were more likely to use areas with greater coyote activity. Collectively, our results demonstrate that NGS can be used to inform conservation and management and explore the relationships between elusive species. iv Acknowledgements
... Ces pollutions souvent associées à l'agriculture ou à l'industrie, peuvent avoir un impact important sur les populations d'amphibiens, direct (mortalité) ou indirect par l'apparition de malformations ou de disfonctionnements biologiques (Relyea et al., 2005Blaustein & Wake, 1990; Blaustein et al., 1994; Beebee, 1996; Beebee & Griffiths, 2005, 1766; Cuvier, 1817), la science qui étudie les populations en tant que telles ou biologie des populations est une science plutôt récente (Conroy & Carroll, 2009). L'herpétologie notamment, ou science qui étudie les reptiles et les amphibiens, l'est encore plus et n'a réellement Mazerolle et al., 2007) comme l'abondance, le recrutement, ou la survie par exemple (Yoccoz et al., 2001; Williams et al., 2002; MacKenzy et al., 2006). En effet, sans connaitre la probabilité de détection d'un individu (notée p), il est impossible d'estimer l'effectif de la population présente (N = C/p ou C= le nombre total d'individus observés ou capturés) (Williams et al., 2002). ...
... L'herpétologie notamment, ou science qui étudie les reptiles et les amphibiens, l'est encore plus et n'a réellement Mazerolle et al., 2007) comme l'abondance, le recrutement, ou la survie par exemple (Yoccoz et al., 2001; Williams et al., 2002; MacKenzy et al., 2006). En effet, sans connaitre la probabilité de détection d'un individu (notée p), il est impossible d'estimer l'effectif de la population présente (N = C/p ou C= le nombre total d'individus observés ou capturés) (Williams et al., 2002). De même les estimations de survie sont biaisées négativement lorsque l'on néglige ces problèmes de détection (Williams et al., 2002). ...
... En effet, sans connaitre la probabilité de détection d'un individu (notée p), il est impossible d'estimer l'effectif de la population présente (N = C/p ou C= le nombre total d'individus observés ou capturés) (Williams et al., 2002). De même les estimations de survie sont biaisées négativement lorsque l'on néglige ces problèmes de détection (Williams et al., 2002). Ainsi, l'évaluation de l'effectif et de la viabilité d'une population au sein d'un site d'étude, requiert des approches statistiques plus sophistiquées que des indices basés sur de simples comptages, des approches visant notamment à estimer les probabilités de détection (Mazerolle et al., 2007Lebreton et al., 1992 ; Kendall et al., 1997; Kendall & Nichols, 2002; Lebreton & Pradel, 2002) et lorsque les individus observés ne sont pas capturés (MacKenzie et al., 2002MacKenzie et al., , 2003MacKenzie et al., , 2005MacKenzie et al., , 2006Blanco‐Moreno et al., 1998 ; Tejedo & Reques, 2002); En France : (Castanet & Guyétant, 1989 ; Cheylan et Poitevin, 1998 ; Thirion, 2002 Thirion, , 2006), sur sa biologie de reproduction (Alvarez et al., 1990 ; Lizana et al., 1994 ; Maglia, 2003 ; Martínez‐Solano, 2000) ou sur son écologie (Thirion, 2006 ; Diaz-Paniagua et al., 2005; Leclair et al., 2005, 1876; Boulenger, 1897; Angel, 1946). ...
MÉMOIRE présenté par PRIOL Pauline pour l'obtention du Diplôme de l'École Pratique des Hautes Études Suivi d'une espèce rare en vue de sa conservation: dynamique spatiale et temporelle de populations de Pélobate cultripède (Pelobates cultripes) en Aquitaine soutenu le 27/11/2015 devant le jury suivant : M. DELESALLE Bruno – Président M. BESNARD Aurélien – Tuteur scientifique et pédagogique M. EGGERT Christophe – Rapporteur M. CROCHET Pierre-André – Examinateur Mme JOURDAN Hélène – Examinateur Mémoire préparé sous la direction de : M. BESNARD Aurélien Laboratoire de Biogéographie et Ecologie des Vertébrés, directeur: M. MIAUD Claude Ecole Pratique des Hautes Etudes UMR 5175-Centre d'Ecologie Fonctionnelle et Evolutive 1919, Route de Mende 34293 Montpellier cedex 5-France
... In model Scenario 3, movement 229 was included in the operating model but ignored in the estimation model in order to investigate 235 In the next phase, the operating model utilized a continuous time framework and the 236 impact of various temporal adjustments in the estimation model were assessed. In each scenario, 237 different temporal adjustments were made to the estimation model mortality calculations to 238 better account for the underlying continuous time dynamics of the spawning, fishing, and tagging 239 processes. The operating model assumed a uniform distribution for the spawning and tagging 240 seasons where spawning occurred May 1 to July 31 and tagging occurred June 1 to August 31 241 (see Figure 1). ...
... Operating Model 4 A traditional approach for tagging simulation studies has been to define the capture history 5 probability statements over discrete time intervals (Williams et al. 2002), and generate the 6 expected number of observations per outcome assuming a multinomial distribution. However, 7 when the number of states (e.g., age and spatial transitions) or sampling events is large, the 8 ability to define each capture history becomes difficult, particularly for a generalized model. ...
The development of a reliable tagging program requires simulation testing the experimental design. However, the potential for model misspecification, particularly in the underlying spatiotemporal dynamics, is often ignored. A continuous time, spatially-explicit, age-structured, capture-recapture operating model was developed to better emulate real-world population dynamics typically overlooked in spatially-aggregated or discrete time tagging models. Various spatiotemporal model parametrizations, including case studies with Atlantic bluefin and yellowfin tunas, were explored to evaluate the bias associated with Brownie tag return estimation models. Simulations demonstrated that accounting for connectivity was essential for obtaining unbiased parameter estimates, and that migration rates could be reliably estimated without the correlation associated with other parameters (e.g., between tag reporting and mortality). Mortality parameter estimates were particularly sensitive to the temporal dynamics of the tagging and fishing seasons, but accounting for the seasonality in tag releases and fishery recaptures allowed for relatively unbiased estimation. Our results indicate that parameter bias and uncertainty can be severely underestimated when discrete time or spatially-aggregated operating models are used to determine optimal experimental design of tagging studies.
... Biological management of species and population has to begin with accurate estimates of population parameters[1]. The knowledge of species-specific life-history traits and reliable estimates of population parameters, size and structure are not only instrumental in the understanding of the dynamics of natural populations[2], but a must be for the design and implementation of effective management strategies[1,3]. ...
... Biological management of species and population has to begin with accurate estimates of population parameters[1]. The knowledge of species-specific life-history traits and reliable estimates of population parameters, size and structure are not only instrumental in the understanding of the dynamics of natural populations[2], but a must be for the design and implementation of effective management strategies[1,3]. Policymakers depend on such data for their management decisions and, inevitably, the effectiveness of management policies depends on the robustness of the scientific evidence from the field. ...
Indo-Pacific humpback dolphins (Sousa chinensis) inhabiting Hong Kong waters are thought to be among the world's most anthropogenically impacted coastal delphinids. We have conducted a 5-year (2010–2014) photo-ID study and performed the first in this region comprehensive mark-recapture analysis applying a suite of open population models and robust design models. Cormack-Jolly-Seber (CJS) models suggested a significant transient effect and seasonal variation in apparent survival probabilities as result of a fluid movement beyond the study area. Given the spatial restrictions of our study, limited by an administrative border, if emigration was to be considered negligible the estimated survival rate of adults was 0.980. Super-population estimates indicated that at least 368 dolphins used Hong Kong waters as part of their range. Closed robust design models suggested an influx of dolphins from winter to summer and increased site fidelity in summer; and outflux, although less prominent, during summer-winter intervals. Abundance estimates in summer (N = 144–231) were higher than that in winter (N = 87–111), corresponding to the availability of prey resources which in Hong Kong waters peaks during summer months. We point out that the current population monitoring strategy used by the Hong Kong authorities is ill-suited for a timely detection of a population change and should be revised.
... as noted above, we also emphasise the need to adequately characterise detection uncertainty, for example, using shadings associated with probabilities of occupancy, in the preparation of distribution maps. finally, as Martin et al. (2010) note, ideally one should account for detection probabilities when estimating transition probabilities among occupancy or abundance states [Yoccoz et al. 2001; williams et al. 2002; Chapter 1]. however, the basic design of many historical large-scale elephant monitoring programs does not allow for the estimation of detectability. the types of models presented by Martin et al. (2010) can be used to reduce errors associated with detectability by at least assigning individuals to broad abundance classes rather than modelling uncorrected count data directly. ...
... Chapter 13 provides further discussion about the use of modern methods to revisit old problematic data sets. nevertheless, we again strongly encourage biologists and wildlife managers to design monitoring programs that will explicitly consider both detection and sampling variation to avoid errors associated with these two sources of variations, which can result in unreliable inference [Yoccoz et al. 2001; williams et al. 2002]. while there are as yet no published applications of occupancy methods to the study of detailed elephant–habitat relationships or management questions in asia, the large-scale study of Karanth et al. (2009) based on historical records and expert opinion surveys of current distribution concluded that the elephant had a relatively restricted range, occupying about 25% of the indian subcontinent. ...
... Thus, some type of calibration for models of detection–nondetection data would be necessary to make predictions reliable for setting spatially explicit harvest quotas. In the absence of monitoring that can provide information on fisher density, a conservative approach to harvest quotas would be necessary until an adequate time series of harvest data is collected (Williams, Nichols & Conroy 2002). ...
... Thus, some type of calibration for models of detection–non-detection data would be necessary to make predictions reliable for setting spatially explicit harvest quotas. In the absence of monitoring that can provide information on fisher density, a conservative approach to harvest quotas would be necessary until an adequate time series of harvest data is collected (Williams, Nichols & Conroy 2002). ...
The challenges associated with monitoring low‐density carnivores across large landscapes have limited the ability to implement and evaluate conservation and management strategies for such species. Non‐invasive sampling techniques and advanced statistical approaches have alleviated some of these challenges and can even allow for spatially explicit estimates of density, one of the most valuable wildlife monitoring tools.
For some species, individual identification comes at no cost when unique attributes (e.g. pelage patterns) can be discerned with remote cameras, while other species require viable genetic material and expensive laboratory processing for individual assignment. Prohibitive costs may still force monitoring efforts to use species distribution or occupancy as a surrogate for density, which may not be appropriate under many conditions.
Here, we used a large‐scale monitoring study of fisher Pekania pennanti to evaluate the effectiveness of occupancy as an approximation to density, particularly for informing harvest management decisions. We combined remote cameras with baited hair snares during 2013–2015 to sample across a 70 096‐km ² region of western New York, USA . We fit occupancy and Royle–Nichols models to species detection–non‐detection data collected by cameras, and spatial capture–recapture (SCR) models to individual encounter data obtained by genotyped hair samples. Variation in the state variables within 15‐km ² grid cells was modelled as a function of landscape attributes known to influence fisher distribution.
We found a close relationship between grid cell estimates of fisher state variables from the models using detection–non‐detection data and those from the SCR model, likely due to informative spatial covariates across a large landscape extent and a grid cell resolution that worked well with the movement ecology of the species. Fisher occupancy and density were both positively associated with the proportion of coniferous‐mixed forest and negatively associated with road density. As a result, spatially explicit management recommendations for fisher were similar across models, though relative variation was dampened for the detection–non‐detection data.
Synthesis and applications . Our work provides empirical evidence that models using detection–non‐detection data can make similar inferences regarding relative spatial variation of the focal population to models using more expensive individual encounters when the selected spatial grain approximates or is marginally smaller than home range size. When occupancy alone is chosen as a cost‐effective state variable for monitoring, simulation and sensitivity analyses should be used to understand how inferences from detection–non‐detection data will be affected by aspects of study design and species ecology.
... Using MARK open and closed capture-recapture models were then applied to these data. All capture-recapture models make the following assumptions (Williams et al., 2002a) ...
... Where is the estimated abundance of all individuals (distinctive and nondistinctive ) identified during the study period, is the abundance estimate of the highly distinctive individuals, and ˆ is the estimated proportion of distinctive individuals (Burnham et al., 1987). The variance for the total stock size estimate was derived as follows (Williams et al., 2002a): ...
Coastal dolphin populations are exposed to non-consumptive human activities that can pose conservation challenges. Consequently, effective management strategies, using rigorous scientific assessments of exposed populations, are needed to mitigate any potential negative impacts of these activities. To inform management decisions for the conservation of the Hawaii Island spinner dolphin (Stenella longirostris) stock, I: (i) estimated abundance and survival rates; (ii) measured the effectiveness of various sampling scenarios to detect changes in abundance; (iii) identified important spinner dolphin resting habitats; and (iv) measured cumulative exposure to human activities. Between September 2010 and March 2013, boat-based and land-based sampling was undertaken to collect dolphin photo-identification, group behaviour and acoustic data from both inside and outside four important spinner dolphin resting bays on the Kona Coast of Hawaii Island. Between years, independent survival rate estimates were similar (0.97 ± 0.05 SE), and abundance estimates of 631 (95% CI 524-761) and 668 (95% CI 556-801; CV =0.09) were very consistent. At this precision, and with 95% power and a monitoring interval of three years, a 5% change in abundance would not be detected for 12 years. I documented that should resting spinner dolphins be displaced from resting bays, they are unlikely to engage in rest behaviour elsewhere. When resting inside bays, dolphins were most likely to rest between 10:00-14:00, and over sandy substrates. Individual spinner dolphins spent between 49.5% and 69.4% of daytime resting (mean = 61.7%). Dolphins were chronically and repeatedly exposed to human activities during daytime hours (> 82% of time), with a median duration of only ten min between interactions. The short interval between interactions may prevent recovery from disturbance and deprive individuals of rest and change their sleep state from “deep” to “light”. Rest deprivation and the disruption of sleep can lead to impaired cognitive abilities and ultimately effect population viability. These data provide a firm baseline for urgent consideration by managers to evaluate the risks to the spinner dolphins of Hawaii Island, potential pathways for mitigating human interactions and ways to measure the success of management interventions.
... Quantifying abundance is fundamental for wildlife conservation and management. Reliable information on wildlife population size is key to support management decisions, harvest regulations, define conservation status, and further our understanding of ecosystems functioning (Williams et al., 2002). However, population abundance continues to be challenging to estimate in nature (Waples & Feutry, 2021), especially when species are elusive, vagile, and distributed over large areas (Doak et al., 2005). ...
The wolf (Canis lupus) is among the most controversial of wildlife species. Abundance estimates are required to inform public debate and policy decisions, but obtaining them at biologically relevant scales is challenging. We developed a system for comprehensive population estimation across the Italian alpine region (100,000 km²), involving 1513 trained operators representing 160 institutions. This extensive network allowed for coordinated genetic sample collection and landscape‐level spatial capture–recapture analyses that transcended administrative boundaries to produce the first estimates of key parameters for wolf population status assessment. Wolf abundance was estimated at 952 individuals (95% credible interval 816–1120) and 135 reproductive units (i.e., packs) (95% credible interval 112–165). We also estimated that mature individuals accounted for 33–45% of the entire population. The monitoring effort was spatially estimated thereby overcoming an important limitation of citizen science data. This is an important approach for promoting wolf–human coexistence based on wolf abundance monitoring and an endorsement of large‐scale harmonized conservation practices.
... Simulations have shown that a situation of high variance in encounter rate with around 20-25 camera traps is expected to yield a coefficient of variation around 0.40 (Fig 4, Rowcliffe et al., 2008), which is consistent with the results of our study (mean CV of 0.36 in REM estimates). Accordingly, to obtain a CV lower than 0.20, required for effective wildlife management (Williams et al., 2002), the effort with REM will be around 100 camera traps. Regarding CT-DS, Bessone et al. (2020) and in consequence precision. ...
A better understanding of population density (i.e. the number of individuals per unit area) is essential for wildlife conservation and management. Despite the fact that a wide variety of methods with which to estimate population density have already been described and broadly used, there are still relevant gaps. In the last few decades, the use of remotely activated cameras (camera traps) has been established as an effective sampling tool when compared with alternative methods. Camera trapping could, therefore, be considered a reliable tool with which to monitor those situations in which classical methods have relevant limitations. It could, for example, be used with species whose behaviour is elusive and which have low detectability (as is the case of most mammals), or populations in which the animals can be identified individually by the spot patterns on their bodies. However, there is lack of information regarding those species for which it is not possible to identify individual animals (i.e. unmarked species). Some authors that have applied camera trapping originally considered relative abundance indexes in order to monitor unmarked populations. These indices were based on encounter rates (i.e. the number of animals detected per sampling unit) observed in camera trapping studies. Methods with which to estimate the population density of unmarked populations were later described, the first of which was the random encounter model (REM). The REM models the random encounters between moving animals and static cameras in order to estimate population density. The REM does this by employing three basic parameters: i) encounter rate, ii) detection zone (area in which the cameras effectively detect animals), and iii) day range (average daily distance travelled by each individual in the population). When this thesis was first started, it was broadly discussed that the application of the REM was limited by the difficulties involved in estimating the parameters required, especially the day range. In this context, the aim of this thesis was to develop and harmonise camera trapping methodologies so as to estimate the population density and movement parameters of unmarked populations, working principally in the REM framework.
The first research carried out for this thesis comprised a review of published studies concerning REM, which found that i) wrong practices in the estimation of REM parameters were frequent, and ii) the REM has rarely been compared with reference densities in empirical studies. We, therefore, then went on to evaluate the main factors that affect the probability of detection and the trigger speed of camera traps, which are relevant for encounter rate and detection zone estimation. This is shown in Chapter 1. We subsequently evaluated and described new methodologies that use camera traps to estimate the movement parameters of unmarked populations. We also evaluated the seasonal and spatial variation in these parameters. The information regarding this is provided in Chapter 2. Finally, we assessed the performance of the REM in a wide range of scenarios, and we compared it with other recently described camera trapping methods used to estimate the population density of unmarked species, as detailed in Chapter 3.
The results reported in Chapter 1 show that camera trap performance as regards trigger speed and detection probability are highly influenced by different factors, such as the period of the day, the camera trap model, deployment height or sensitivity, among others. We monitored the community of birds and mammals in the study area, and we discovered that a relevant proportion of the animals that entered the theoretical detection zone were not usually recorded. These missed detections introduce bias into the encounter rate, and consequently into density. However, several camera trapping methods with which to estimate effective detection zone have been described, and they should be applied to all the populations monitored. With regard to the day range, we considered the wild boar as a model species and showed that assuming straight-line distances between consecutive locations obtained by telemetry devices underestimates this parameter, while movement behaviours should be accounted when using camera traps to estimate day range, as shown in Chapter 2.1. We then explored the use of camera traps to monitor movement parameters in greater depth, and showed that they are a reliable method. We described a new procedure with which to estimate the day range that accounts for movement behaviour, and for the ratio between fast and slow speeds. The new procedure performed well in the wide range of scenarios that we simulated, and was also tested with populations of mammals around the world. In this respect, we also described a machine learning protocol with which to identify movement behaviour obtained from camera trap records. All of this is described in Chapter 2.2. We subsequently showed that geographical (e.g. altitude), environmental (e.g. habitat fragmentation), biological (e.g. species) and management (e.g. hunting) factors affect the day range, and we reported variable day ranges in ungulates and carnivores across Europe, as shown in Chapter 2.3. We use the combination of a literature review and an empirical study to compare REM densities with those obtained using reference methods. The results showed a strong correspondence between the REM and reference densities, especially when REM parameters are estimated accurately for the target population. We also showed that the precision of the REM is lower than that of the reference methods, and provided further insights into the survey design in order to increase precision. This information is provided in Chapter 3.1. Finally, and as shown in Chapter 3.2, we used ungulates and carnivores as a target in order to compare the REM, random encounter and staying time (REST), and camera trap distance sampling (CT-DS). The REST and CTDS are two recently described methods with which to estimate the population density of unmarked species using camera traps. The results showed that the performance of the three methods is similar in terms of accuracy and precision. We recommend a survey design that will make it possible to apply all the methods, as the final selection of one of them will be mediated by the number of animals recorded and the camera trap performance.
In conclusion, the results of this thesis show the usefulness of camera trapping to monitor the movement parameters and population density of wildlife and contributes with a methodological practical step forwards. In summary, the REM approach, which was tuned in this thesis, proved to be a reliable method in a wide range of environmental scenarios. The REM can be firmly established as a reference method to be implemented in multispecies monitoring programmes in the coming years, considering that it does not need to identify individual animals or spatial autocorrelation in captures. However, future developments of the REM in particular, and camera trapping unmarked methods in general, should be focused on optimising surveys designs in order to increase precision. Before this thesis was begun, the main limitations of applying the REM were the estimation of REM parameters, along with its reliability. This has, however, already been dealt with, and the main gap now concerns the low precisions obtained.
... Several methods have been employed for detecting space use by elephants, such as field methods (telemetry data, direct sighting, and scat observation) [31,34,35,[37][38][39][40] and analytical methods (spatial regressions, habitat suitability, resource-selection functions, and presence-only modelling) [41,42]. However, these methods do not consider observation errors, such as imperfect detectability (sometimes animals might pass unobserved) and sampling biasness [31,[43][44][45][46]. In addition, presence-absence surveys can underestimate species distribution [31,46,47] as they barely differentiate between the true absence of species and the non-detection of signs. ...
Land development has impacted natural landforms extensively, causing a decline in resources and negative consequences to elephant populations, habitats, and gene flow. Often, elephants seek to fulfill basic needs by wandering into nearby human communities, which leads to human–elephant conflict (HEC), a serious threat to conserving this endangered species. Understanding elephant space use and connectivity among their habitats can offset barriers to ecological flow among fragmented populations. We focused on the Keonjhar Forest Division in Eastern India, where HEC has resulted in the deaths of ~300 people and several hundred elephants, and damaged ~4100 houses and ~12,700 acres of cropland between 2001 and 2018. Our objectives were to (1) analyze elephant space use based on their occupancy; (2) map connectivity by considering the land structure and HEC occurrences; (3) assess the quality of mapped connectivity and identify potential bottlenecks. We found that (1) the study area has the potential to sustain a significant elephant population by providing safe connectivity; (2) variables like forests, precipitation, rural built-up areas, cropland, and transportation networks were responsible for predicting elephant presence (0.407, SE = 0.098); (3) five habitat cores, interconnected by seven corridors were identified, of which three habitat cores were vital for maintaining connectivity; (4) landscape features, such as cropland, rural built-up, mining, and transportation networks created bottlenecks that could funnel elephant movement. Our findings also indicate that overlooking HEC in connectivity assessments could lead to overestimation of functionality. The study outcomes can be utilized as a preliminary tool for decision making and early planning during development projects.
... 1 Introduction 24 Understanding the processes that influence species abundance, density, demographic rates, spa-25 tial distribution, and habitat selection are central goals in ecology and fundamental to biodiversity 26 conservation (e.g., Williams et al., 2002;Manly et al., 2007;MacKenzie et al., 2018). Driven by 27 a need to quantify these processes, both spatial capture-recapture (SCR; e.g., Royle ...
Ecologists and conservation biologists increasingly rely on spatial capture–recapture (SCR) and movement modeling to study animal populations. Historically, SCR has focused on population‐level processes (e.g., vital rates, abundance, density, and distribution), whereas animal movement modeling has focused on the behavior of individuals (e.g., activity budgets, resource selection, migration). Even though animal movement is clearly a driver of population‐level patterns and dynamics, technical and conceptual developments to date have not forged a firm link between the two fields. Instead, movement modeling has typically focused on the individual level without providing a coherent scaling from individual‐ to population‐level processes, whereas SCR has typically focused on the population level while greatly simplifying the movement processes that give rise to the observations underlying these models. In our view, the integration of SCR and animal movement modeling has tremendous potential for allowing ecologists to scale up from individuals to populations and advancing the types of inferences that can be made at the intersection of population, movement, and landscape ecology. Properly accounting for complex animal movement processes can also potentially reduce bias in estimators of population‐level parameters, thereby improving inferences that are critical for species conservation and management. This introductory article to the Special Feature reviews recent advances in SCR and animal movement modeling, establishes a common notation, highlights potential advantages of linking individual‐level (Lagrangian) movements to population‐level (Eulerian) processes, and outlines a general conceptual framework for the integration of movement and SCR models. We then identify important avenues for future research, including key challenges and potential pitfalls in the developments and applications that lie ahead.
... Simulations have shown that a situation of high variance in encounter rate with around 20-25 camera traps is expected to yield a coefficient of variation around 0.40 (figure 4, Rowcliffe et al., 2008), which is consistent with the results of our study (mean CV of 0.36 in REM estimates). Accordingly, to obtain a CV lower than 0.20, required for effective wildlife management (Williams et al., 2002), the effort with REM will be around 100 camera traps. Regarding CT-DS, Bessone et al. (2020) precision. ...
Population density estimations are essential for wildlife management and conservation. Camera traps have become a promising cost‐effective tool, for which several methods have been described to estimate population density when individuals are unrecognizable (i.e. unmarked populations). However, comparative tests of their applicability and performance are scarce.
Here, we have compared three methods based on camera traps to estimate population density without individual recognition: Random Encounter Model (REM), Random Encounter and Staying Time (REST) and Distance Sampling with camera traps (CT‐DS). Comparisons were carried out in terms of consistency with one another, precision and cost‐effectiveness. We considered six natural populations with a wide range of densities, and three species with different behavioural traits (red deer Cervus elaphus, wild boar Sus scrofa and red fox Vulpes vulpes). In three of these populations, we obtained independent density estimates as a reference.
The densities estimated ranged from 0.23 individuals/km² (fox) to 34.87 individuals/km² (red deer). We did not find significant differences in terms of density values estimated by the three methods in five out of six populations, but REM has a tendency to generate higher average density values than REST and CT‐DS. Regarding the independents’ densities, REM results were not significantly different in any population, and REST and CT‐DS were significantly different in one population. The precision obtained was not significantly different between methods, with average coefficients of variation of 0.28 (REST), 0.36 (REM) and 0.42 (CT‐DS). The REST method required the lowest human effort.
Synthesis and applications. Our results show that all of the methods examined can work well, with each having particular strengths and weaknesses. Broadly, Random Encounter and Staying Time (REST) could be recommended in scenarios of high abundance, Distance Sampling with camera traps (CT‐DS) in those of low abundance while Random Encounter Model (REM) can be recommended when camera trap performance is not optimal, as it can be applied with less risk of bias. This broadens the applicability of camera trapping for estimating densities of unmarked populations using information exclusively obtained from camera traps. This strengthens the case for scientifically based camera trapping as a cost‐effective method to provide reference estimates for wildlife managers, including within multi‐species monitoring programmes.
... Understanding movement, reproduction, and survival is essential in metapopulation studies (Hanski, 1998;Ovaskainen & Saastamoinen, 2018). Inference of movement and vital rates, however, often relies on data of captured/ marked animals that are relatively difficult to obtain (Grand et al., 2003;Neil Arnason, 1973;Pollock, 1991). The development of the original dynamic N-mixture model allows the inference of recruitment and apparent survival while accounting for imperfect detection in count data of unmarked animals (Dail & Madsen, 2011), providing potentials to understand metapopulation dynamics with limited financial resources. ...
Knowledge of age-specific movement and vital rates is important for understanding metapopulation dynamics yet difficult to obtain without capturing/marking individual animals. The development of dynamic N-mixture models allows for the inference of recruitment and apparent survival while accounting for imperfect detection in count data of unmarked populations. Recent studies have further developed dynamic N-mixture models to account for age structures or movement among local populations; however, there has yet to be a dynamic N-mixture model that simultaneously accounts for both age structure and movement despite the fact that natural populations are composed of individuals of different ages with different movement and vital rates. In this study, I developed a dynamic N-mixture model that allows different movement and vital rates between age classes while accounting for imperfect detection in age-structured count data. I then conducted a simulation study to evaluate the inferential performance of the model while considering different local abundances, number of sites, and detection probabilities. The simulation study showed that the model could provide unbiased estimates of adult-related parameters under a high detection probability, but bias was found for young-related parameters regardless of detection probability. The bias in young-related parameters also tended to be lower when local abundance was lower, probably due to more frequent extinction-recolonization events in these populations. Overall, the results indicated that cautions should be taken when using dynamic N-mixture models alone. However, these models may be useful sub-models under integrated modeling frameworks, and thus improve our understanding of metapopulation dynamics.
... To estimate the abundance of free-ranging dogs in the study area, capture-recapture (CR) methods were chosen (Williams et al. 2002;O'Connell et al. 2011;Paschoal et al. 2012;Belsare and Gompper 2013). Closed population models use requires that three assumptions must be fulfilled (O'Connell et al. 2011): (1) the population is closed to processes of birth, death, immigration and emigration; ...
Domestic dogs (Canis familiaris) have turned out to be one of the most common carnivoran species in natural ecosystems worldwide, becoming a major concern for wildlife conservation, particularly on islands. Here, we assessed the effect of infrastructure and the environment on the occupancy probability of free-ranging dogs on Navarino Island, Cape Horn Biosphere Reserve, southern Chile. We collected camera-trap data from 200 sites in a grid around the only major settlement of Navarino Island. Single-season, single species occupancy modeling was used to assess the impact of five infrastructure variables and two environmental variables on the occupancy of free-ranging dog and of six variables on the probability of detection. A total of 4,000 camera-trap days yielded 67 independent photo sequences of free-ranging dogs. Our results provided support for the hypothesis that environmental variables had the most influence on occupancy, when compared to infrastructure variables, while Julian date, survey and animal trail density were the most important predictor variables for detection probability. Free-ranging dogs preferred open habitats instead of forests and habitats at lower elevations. The photographic records further showed interaction between owned/unowned and feral free-ranging dogs as well as reproduction in feral dogs. Dogs were slightly more active at day than at night. Results of the present study demonstrated that there is an urgent need to implement management measurements in order to reduce the numbers of free-ranging dogs in the Cape Horn Biosphere Reserve.
... Detection errors can be roughly categorized as either false negatives or false positives. False-negative errors (i.e., not detecting an individual present in the area) are commonly addressed in wildlife studies, including aerial surveys, and there is a vast literature about how to deal with them (e.g., Kéry & Royle, 2016;MacKenzie et al., 2006;Royle & Dorazio, 2008;Williams, Nichols, & Conroy, 2002). False positives (i.e., counting an individual twice or recognizing another target as an individual) are typically assumed insignificant; and the development of methods to deal with this error type is still nascent (Dénes et al., 2015). ...
Unmanned aerial systems ( UAS ) are emerging as an accessible and versatile tool for ecologists, promising to revolutionize the way abundance and distribution data are obtained in wildlife studies. Establishment of UAS as an efficient and reliable tool demands understanding how detection errors influence UAS ‐derived counts and possible solutions to address them.
We describe two types of false‐negative errors (availability and perception errors) and two types of false‐positive errors (misidentification and double count) that may bias abundance estimates from UAS surveys. Then, we discuss available methods to address detection errors in UAS surveys and point out challenges for future developments. We present hierarchical models as an integrative framework to account for multiple detection errors and datasets in UAS abundance modelling.
Methods to address detection errors in UAS surveys depend on how data are collected (flight plan, images processing, and reviewing procedure). Conventional aerial surveys literature offers a set of solutions, especially to deal with false‐negative errors. Available auxiliary information (such as ground counts and telemetry data) facilitates estimating detection errors, although the versatility of UAS permits exploring novel approaches. Solutions involve planning separated strip transects, temporally replicating flights, carrying out counts in orthomosaics, and multiple observer protocol. When automatic image review is used, subsample manual reviewing, trial experiments, and semiautomated procedures might deal with algorithm errors.
UAS surveys need to be consciously planned, thinking on what kind of errors can significantly affect counts and the use of raw counts and indices should be avoided. Approaches that formally account for false positives are needed, particularly for double counts. Hierarchical modelling (especially N ‐mixture models) offers a fruitful framework to explore and combine solutions, integrating multiple datasets and accommodating different detection errors.
... Hence, according to our data, the expected number of different individuals with the same genotype was very low. (Williams et al. 2002, Royle & Dorazio 2008. Invasive methods are often inappropriate in the case of endangered species and require multisession sampling, for example during subsequent seasons (Capture-Mark-Recapture) -the strategy difficult to achieve in the case of elusive and rare species. ...
Knowledge about population size is of high importance for conservationists. We used non-invasively collected samples and microsatellite genotyping to estimate the size of the Tatra National Park population of the endangered capercaillie Tetrao urogallus. This population is one of the most important strongholds of the species in Poland. In 2016 over 150 samples (faeces and feathers) of the capercaillie were collected throughout area of the Tatra National Park. Then, DNA was extracted and genetic profiles were evaluated, using nine microsatellite markers. We obtained 81 reliable genotypes. Among them, 34 unique genotypes were found, corresponding to Minimum Number of individuals Alive in the investigated population. Application of capture-recapture models in the R package Capwire indicated, that the area was inhabited by approx. 54 birds, whereas regression model suggested presence of 36-64 individuals. Previous field surveys suggested that the number of birds in the Tatra National Park is about 50. Hence, we assumed that genetic tagging of non-invasive samples performs well in estimating the abundance of the capercaillie in the investigated population.
... Several statistical methods exist to describe and explain demographic processes in populations (the demographic paradigm) and to quantify the impact of environmental factors on demographics (the ecological paradigm). Pardo et al. (6) used the more processoriented approach based on estimation and analysis of demographic parameters, such as survival or breeding probabilities, assessment of the effect of environmental factors on these parameters, and joint modeling of these parameters using matrix population models (7,8). Using high-quality data from ringed individuals collected over five decades as part of a remarkable long-term monitoring program of albatrosses on Bird Island, South Georgia, Southern Ocean, Pardo et al. (6) are able to disentangle the relative and additive influences of fisheries and climate on the population dynamics of albatrosses, including their effects on juvenile and immature classes. ...
... However, a key premise for the use of rarefaction curves is that the sample effort is applied consistently within the area of interest and that each data value is independent. In general, data independence can only be assured by random sampling, but if the starting position of a regular grid is chosen randomly, the resulting sampling array can effectively be considered random in most cases (Williams et al. 2002). In general, during fieldwork for environmental assessments, individuals are sampled collectively in samples and are not encountered randomly or individually in systematic sampling. ...
Rarefaction Curves are frequently used in Environmental Impact Assessments to evaluate sampling sufficiency, but without clear guidelines of how to ensure that the assumptions of the methods are met. Infrastructure projects in the Brazilian Amazon and elsewhere often occupy extensive areas in remote locations with difficult access, and random sampling under such conditions is impractical. We tested the influence of sampling unit (sample or individual), and geographic distance between samples on rarefaction curve s, and evaluated the magnitude of errors resulting from the misuse of rarefaction curve in decision making, using frogs from four Amazonian sampling sites. Individual-based rarefaction curve were steeper than those generated by sample-based rarefaction curve. Geographic distance influenced the number of exclusive species in a predictable fashion only in one area, and not in the Environmental Impact Assessment site. Misuse of rarefaction curve generated large errors in the identification of vulnerable taxa. Because the rarefaction curve model is sensitive to the assumption of randomness and geographic distance can influence it unpredictably, we suggest that rarefaction curve should generally not be used to estimate sample completeness when making management decisions for environmental licensing purposes.
... However, the most appropriate methods vary greatly according to circumstances. General methodologies are available (see, for example, Buckland et al. 2001;Legg and Nagy 2006;MacKenzie et al. 2005;Shrader-Frechette and McCoy 1993;Williams, Nichols and Conroy 2002), but further modelling may be valuable for tailoring methods for particular species or circumstances (see, for instance, Barnes 2002;Karanth and Nichols 2002;Plumptre 2000;Sims et al. 2006;Taylor and Gerrodette 1993). ...
... However, the study has highlighted the fact that, within closed brown hyaena populations, there is limited to zero ongoing long term monitoring. Animal population biology ultimately must be understood in a broader context of the habitats and communities of which the populations are a part of as every animal is influenced by both biotic and abiotic pressures (Williams et al., 2002). Therefore, to ensure the long term survival of the highly threaten and endangered species it is critical to understand how each of the elements within system are interconnected especially if they are working within a closed population. ...
Global biodiversity is declining at an unprecedented rate, principally as a consequence of increasing human population. Effects of this expansion are exemplified by the extent to which many carnivores are now in conflict with humans, particularly in unprotected rangelands. One such species is the brown hyaena (Parahyaena brunnea), classified as ‘Near Threatened’ (Wiesel et al., 2008). The IUCN SSC hyaenid specialist group identified that brown hyaena are under threat due to human persecution and noted that greater understanding of their distribution and abundance is needed. With the principal aim of assessing the distribution and abundance of brown hyaena in South Africa, this study responds to that challenge. Five specific objectives were established: to utilise local knowledge to map the distribution of carnivores across South Africa; to determine the factors driving attitudes and perceptions of South African farmers to carnivores; to determine differences in relative abundance of carnivores in protected areas compared to unprotected in the North West and Limpopo Provinces; to compare home range estimates and movement patterns of free living brown hyaena inside and outside protected areas in the same provinces; to determine what variables influence brown hyaena home range size.
Distribution of brown hyaena and other carnivores, and attitudes to them, were determined using a web-based questionnaire involving multiple stakeholder groups. The results confirmed current knowledge on carnivore distributions but, critically, revealed wider distribution of brown hyaena and other key species than are currently known by IUCN (2013). Responses demonstrated that cultural group and land use type significantly affected attitudes towards all carnivores, with Afrikaans livestock farmers demonstrating the most overtly negative attitudes to all carnivore species. An encouraging finding was that 25% of land owner respondents had positive attitudes to brown hyaenas and were therefore likely to be well disposed to engaging in conservation activities. Further information on the abundance and movement ecology of brown hyaena was gained through an intensive field study in the North West and Limpopo Provinces, which are under-researched. The study was conducted in protected and unprotected areas since brown hyaenas are found in both but are subject to different pressures. The use of remote camera traps demonstrated that the relative abundance of brown hyaena was four times lower in unprotected areas than in the protected areas. A significant finding was that mesopredators showed higher relative abundances in the unprotected areas. This suggests probable further human-wildlife conflict if mesopredator release continues to occur. Low levels of abundance in the unprotected areas, in conjunction with persecution, led to the conclusion that conservation efforts should be focused here.
GPS collars were used to determine differences between brown hyaena home range across the protected and unprotected areas, to gain insights into their habitat use, and to establish their movement patterns through the fragmented landscape. The study demonstrated that home range sizes in the unprotected areas were not only significantly smaller than in the protected areas but also substantially smaller than those found across the entire hyaena’s range. Reasons for the variation are suspected to be higher levels of persecution and greater biomass availability outside the protected areas in conjunction with the relatively high density of apex predators inside the protected areas. In conclusion, large carnivore research is critically required outside protected areas where carnivores are currently involved in the most conflict and are at the greatest risk.
... Neste sentido, formulam-se modelos matemáticos que tentam estimar a abundância e densidades populacionais. Os modelos mais utilizados na estimação do tamanho de uma população animal baseiam-se em amostragem por captura-recaptura (Williams et al., 2002) e amostragem por distâncias (Buckland et al., 2001). O caso mais simples do modelo de transectos lineares é quando se considera que existe apenas um observador. ...
Abstract: This work is intended to estimate population size using the combined line transect and capture-recapture model (Alpizar-Jara e Pollock, 1999) through Bayesian Inference. The probability of sighting an animal (or an object) on the transect line, g 0 , is estimated assuming that is less or equal to 1. The posterior distributions of the key parameters of the detection function were obtained using Gibbs sampling as implemented in WinBUGS 1.4. The performance of the Bayesian estimator has been evaluated and compared to maximum likelihood estimators of the combined model. This work is illustrated with a practical example.
... Logistic regression relies on the logit link function, based on the binomial distribution, so that predictions of survival will be mapped to the range 0-1. Additionally, logit link functions can be used to evaluate proportional odds for ranked data (Agresti 1996) and underpin a host of the current capture-mark-recapture modeling approaches used wild wildlife science ( Williams et al. 2002). ...
There is a fear of statistics among the public, state and federal officials, and even among numerous scientists. The general feeling appears to be based on the convoluted manner in which “statistics” is presented in the media and by the cursory introduction to statistics that most people receive in college. Among the media, we often hear that “statistics can be used to support anything you want”; thus, statistics (and perhaps statisticians by implication) become untrustworthy. Of course, nothing could be further from the truth. It is not statistics per se that is the culprit. Rather, it is usually the way in which the data were selected for analysis that results in skepticism among the public.
Additionally, and as we have emphasized throughout this book, “statistics” and “study design” are interrelated yet separate topics. No statistical analysis can repair data gathered from a fundamentally flawed design, yet improperly conducted statistical analyses can easily be corrected if the design was appropriate. In this chapter we outline the knowledge base we think all natural resource professionals should possess, categorized by the primary role one plays in the professional field. Students, scientists, managers, and yes, even administrators, must possess a fundamental understanding of study design and statistics if they are to make informed decisions. We hope that the guidance provided below will help steer many of you toward an enhanced understanding and appreciation of study design and statistics.
... The modeling and sustainability of biodiversity and ecosystem services require interdisciplinary exchanges between ecology, economics, mathematics, and computer sciences. In that respect, bioeconomics (Clark, 1990), ecological economics (Costanza et al., 2015), and conservation biology (Byron, Nichols, & Conroy, 2002) are well-suited to provide important multidisciplinary insights. Applications also contribute to point out the need to reconcile the various scientific disciplines engaged in the management of biodiversity and ecosystem services. ...
... The latter can be sub-divided into within-season breeding dispersal, during which individuals move following nesting failure or success , and between-season breeding dispersal, in which individuals change the breeding site from one season to another (Greenwood & Harvey 1982). The survival probabilities of small birds at a particular site are frequently analysed by using capture-recapture models, the Cormack-Jolly- Seber (CJS) formulation being the most often used one (Lebreton et al. 1992, Naef-Daenzer et al. 2001, Williams et al. 2002, Greño et al. 2008). In the CJS, the probability of encounter (p) is explicitly modelled in order to correct for possible biases in survival estimates. ...
Studies dealing with the individual survival of birds in open populations usually estimate survival according to capture-recapture models like the Cormack-Jolly-Seber (CJS). In fact, these models estimate local apparent survival (ϕ), which is a combination of the probabilities of true survival (S) and site-fidelity (F), i.e. death and emigration are confounded. These S and F parameters can be estimated by using ‘robust’ models (e.g. Barker’s model), which use additional resighting and dead reports data. We aim to compare the results (and associated biological implications) obtained by analysing juvenile and adult survival in a Polish urban population of Blackbirds Turdus merula using both the CJS and Barker models. Our CJS models estimated high ϕ values for both juvenile and adult birds (0.48 and 0.62, respectively). The lower scores for juveniles could be interpreted as low juvenile overwintering survival. By fitting Barker models to the same dataset we determined that juvenile site fidelity was lower than that of adults (0.91 and 0.93, respectively), so natal dispersal was slightly greater than breeding dispersal. The high fidelity causes similarity between apparent survival and true survival parameters (S: 0.51 for juveniles, 0.64 for adults). The results are comparable with data from other urban populations. Thus, using robust models certainly allows one to reduce the noise of movements confounding and/or masking survival probabilities, but one can also determine the individual or environmental variables affecting any of them separately.
... I recommend that future studies increase the sample size of primary (landowner) sample units. However, Although I was unable to estimate precisely (CV ≤15%) for both time periods and multiple management practices across regions in 2010, continued collection of the same number of soil cores for two additional years should enhance the precision of the overall regional and composite estimates (Williams et al. 2002:44-45, Stafford et al. 2006a). ...
Rice not collected by harvesters and natural seeds are important foods for waterfowl. Estimation of abundance of these seeds is necessary for calculating waterfowl habitat conservation needs in the Louisiana Chenier Plain (LCP) and Texas Mid-Coast (TMC). My objectives were to quantify dry mass of rice and other seeds from August-November 2010, and estimate waterbird abundances on farmed and idle ricelands in these regions from December 2010-March 2011. Rice abundance in farmed ricelands ranged from 159.7 kg/ha (CV = 66.6%) to 1,014.0 kg/ha (CV = 8.3%). Natural seed abundance in idle ricelands ranged from 99.7 kg/ha (CV = 32.9%) to 957.4 kg/ha (CV = 17.2%). Greatest waterbird densities occurred in shallowly flooded disked ricelands (mean = 7.35 waterbirds/ha, 90%; CI = 2.37-19.70). Ratoon, disked, and shallowly flooded ricelands are important habitat for non-breeding waterbirds but variable estimates of seed and waterbird abundances warrant continuation of this study.
... Knowledge regarding neonate survival and cause−specific mortality rates are essential parameters to model populations (Lebreton et al., 1992). Understanding neonate survival rates allows for estimation of recruitment; crucial information for management decisions allocating harvest or cull rates, depending if the population is to remain stable, increase, or decline (Williams et al., 2002). Neonate ungulate survival rates can be low because of their vulnerability to predation (Barrett, 1984). ...
Large herbivores typically have consistently high prime‐aged adult survival and lower, more variable, juvenile, and senescent survival. Many kangaroo populations undergo greater fluctuations in density compared with other large herbivores, but age‐ and sex‐specific survival of kangaroos and their response to environmental variation remain poorly estimated. We used long‐term capture–mark–recapture data on 920 individuals to investigate the survival component of eastern grey kangaroo (Macropus giganteus) population dynamics. Forage availability and population density were monitored quarterly and included as predictors of survival in Bayesian Cormack–Jolly–Seber models. Annual survival probabilities were estimated for five age classes: 0 years (juveniles), 1–2 years (subadults), 3–6 years (prime‐aged adults), 7–9 years (presenescent adults), and ≥10 years (senescent adults). Survival of juveniles varied widely during our 12‐year study, ranging from 0.07 to 0.90 for females and 0.05–0.92 for males. Subadult survival was 0.80–0.93 for females and 0.75–0.85 for males, while that of prime‐aged adults was ≥0.94 for females and ≥0.83 for males, despite large fluctuations in forage and density. The survival of presenescent adults spanned 0.86–0.93 for females and 0.60–0.86 for males. Senescent survival was variable, at 0.49–0.90 for females and 0.49–0.80 for males. Male survival was significantly lower than female survival in prime‐aged and presenescent adults, but not in other age classes. Although most of the models supported by Watanabe–Akaike Information Criterion selection included at least one environmental covariate, none of these covariates individually had a discernible effect on survival. Temporal variability in overall survival appeared mostly due to changes in the survival of juvenile and senescent kangaroos. Kangaroo survival patterns are similar to those of ungulates, suggesting a strong role of sex–age structure on population dynamics.
The development of methods for individual identification in wild species and the refinement of Capture-Mark-Recapture (CMR) models during the past decades has greatly improved the assessment of population demographic rates to answer ecological and conservation questions. In particular, multistate models, with their flexibility for the analysis of complex study systems, have become popular in the ecological community. However, despite the extensive use of these models, little attention has been paid to the effect of common violations of the CMR model assumptions, such as mark loss and the often-associated recycling of remarked individuals.
To explore this knowledge gap we used a wide range of simulation scenarios reflecting frequently encountered real case studies inspired from the survival rates of 700 vertebrates’ species. We estimated the effects of mark loss and recycled individuals on parameter estimates using a multi-state Cormack-Jolly-Seber (MSCJS) framework. We explored parameter bias through simulations of a metapopulation system with different capture and survival rates. We also illustrated how mark loss can be easily estimated and accounted for using an empirical long-term (10 years) CMR dataset of bats, individually identified using both PIT tag technology as marks that can be lost, and multi-locus genotypes as ‘permanent marks’.
The results from our simulated scenarios demonstrated that the occurrence of bias and the parameters concerned were highly dependent on the study system, and no general rules on parameter behaviour can be established a priori . The model structure and the interdependency among parameters make it challenging to predict how bias could affect estimates.
Our results highlight the need to assess the effect of mark loss when using MSCJS models. Ignoring such violations of model assumptions can have important implications for ecological inferences and conservation policies. In general, the use of permanent marks (e.g. genotype), should always be preferred when modelling of population dynamics and if not possible, combining two types of temporary marks (e.g. PIT tags, bands) should be considered.
Understanding the relative strengths of intrinsic and extrinsic factors regulating populations is a long‐standing focus of ecology and critical to advancing conservation programs for imperiled species. Conservation could benefit from an increased understanding of factors influencing vital rates (somatic growth, recruitment, survival) in small, translocated populations, which is lacking owing to difficulties in long‐term monitoring of rare species. Translocations, here defined as the transfer of wild‐captured individuals from source populations to new habitats, are widely used for species conservation, but outcomes are often minimally monitored, and translocations that are monitored often fail. To improve our understanding of how translocated populations respond to environmental variation, we developed and tested hypotheses related to intrinsic (density dependent) and extrinsic (introduced rainbow trout Oncorhynchus mykiss, stream flow and temperature regime) causes of vital rate variation in endangered humpback chub (Gila cypha) populations translocated to Colorado River tributaries in the Grand Canyon (GC), USA. Using biannual recapture data from translocated populations over 10 years, we tested hypotheses related to seasonal somatic growth, and recruitment and population growth rates with linear mixed‐effects models and temporal symmetry mark–recapture models. We combined data from recaptures and resights of dispersed fish (both physical captures and continuously recorded antenna detections) from throughout GC to test survival hypotheses, while accounting for site fidelity, using joint live‐recapture/live‐resight models. While recruitment only occurred in one site, which also drove population growth (relative to survival), evidence supported hypotheses related to density dependence in growth, survival, and recruitment, and somatic growth and recruitment were further limited by introduced trout. Mixed‐effects models explained between 67% and 86% of the variation in somatic growth, which showed increased growth rates with greater flood‐pulse frequency during monsoon season. Monthly survival was 0.56–0.99 and 0.80–0.99 in the two populations, with lower survival during periods of higher intraspecific abundance and low flood frequency. Our results suggest translocations can contribute toward the recovery of large‐river fishes, but continued suppression of invasive fishes to enhance recruitment may be required to ensure population resilience. Furthermore, we demonstrate the importance of flooding to population demographics in food‐depauperate, dynamic, invaded systems.
Given the rapid population decline and recent petition for listing of the monarch butterfly ( Danaus plexippus L.) under the Endangered Species Act, an accurate estimate of the Eastern, migratory population size is needed. Because of difficulty in counting individual monarchs, the number of hectares occupied by monarchs in the overwintering area is commonly used as a proxy for population size, which is then multiplied by the density of individuals per hectare to estimate population size. There is, however, considerable variation in published estimates of overwintering density, ranging from 6.9–60.9 million ha ⁻¹ . We develop a probability distribution for overwinter density of monarch butterflies from six published density estimates. The mean density among the mixture of the six published estimates was ~27.9 million butterflies ha ⁻¹ (95% CI: 2.4–80.7 million ha ⁻¹ ); the mixture distribution is approximately log-normal, and as such is better represented by the median (21.1 million butterflies ha ⁻¹ ). Based upon assumptions regarding the number of milkweed needed to support monarchs, the amount of milkweed ( Asclepias spp.) lost (0.86 billion stems) in the northern U.S. plus the amount of milkweed remaining (1.34 billion stems), we estimate >1.8 billion stems is needed to return monarchs to an average population size of 6 ha. Considerable uncertainty exists in this required amount of milkweed because of the considerable uncertainty occurring in overwinter density estimates. Nevertheless, the estimate is on the same order as other published estimates. The studies included in our synthesis differ substantially by year, location, method, and measures of precision. A better understanding of the factors influencing overwintering density across space and time would be valuable for increasing the precision of conservation recommendations.
Given the rapid population decline and recent petition for listing of the monarch butterfly ( Danaus plexippus L.) under the Endangered Species Act, an accurate estimate of the Eastern, migratory population size is needed. Because of difficulty in counting individual monarchs, the number of hectares occupied by monarchs in the overwintering area is commonly used as a proxy for population size, which is then multiplied by the density of individuals per hectare to estimate population size. There is, however, considerable variation in published estimates of overwintering density, ranging from 6.9–60.9 million ha ⁻¹ . We develop a probability distribution for overwinter density of monarch butterflies from six published density estimates. The mean density among the mixture of the six published estimates was ~27.9 million butterflies ha ⁻¹ (95% CI: 2.4–80.7 million ha ⁻¹ ); the mixture distribution is approximately log-normal, and as such is better represented by the median (21.1 million butterflies ha ⁻¹ ). Based upon assumptions regarding the number of milkweed needed to support monarchs, the amount of milkweed ( Asclepias spp.) lost (0.86 billion stems) in the northern U.S. plus the amount of milkweed remaining (1.34 billion stems), we estimate >1.8 billion stems is needed to return monarchs to an average population size of 6 ha. Considerable uncertainty exists in this required amount of milkweed because of the considerable uncertainty occurring in overwinter density estimates. Nevertheless, the estimate is on the same order as other published estimates. The studies included in our synthesis differ substantially by year, location, method, and measures of precision. A better understanding of the factors influencing overwintering density across space and time would be valuable for increasing the precision of conservation recommendations.
Estimating abundance is one of the most fundamental and important aspects of population biology, with major implications on how the status of a population is perceived and thus on conservation and management efforts. Although typically based on one of two methods (distance sampling or mark-recapture), there are many individual identification methods that can be used for mark-recapture purposes. In recent years, the use of genetic data for individual identification and abundance estimation through mark-recapture analyses have increased, and in some situations such genetic identifications are more efficient than their field-based counterparts for population monitoring. One issue with mark-recapture analyses, regardless of which method of individual identification is used, is that the study area must provide adequate opportunities for “capturing” all individuals within a population. However, many populations are unevenly and widely distributed, making it unfeasible to adequately sample all necessary areas. Here we develop an analytical technique that accounts for unsampled locations, and provides a means to infer “missing” individuals from unsampled locations, and therefore obtain more accurate abundance estimates when it is not possible to sample all sites. This method is validated using simulations, and is used to estimate abundance of the Eastern Canada-West Greenland (EC-WG) bowhead whale population. Based on these analyses, the estimated size of this population is 9,089 individuals, with a 95% highest density interval of 5,107–17,079.
Although important to guide conservation management, detailed demographic studies on rare or elusive species inhabiting fragmented, human-dominated landscapes are often hampered by the species' low densities, and the logistic and ethical constraints in obtaining reliable information covering large areas. Genetic non-invasive sampling (gNIS) provides cost-effective access to demographic information, though its application to small mammals is still scarce. We used gNIS to infer on the demography of an endemic small mammal, the Cabrera vole (Microtus cabrerae), occurring as a spatially-structured population in a 462-ha Mediterranean farmland landscape. We intensively sampled fresh vole feces in four seasons, extracted the DNA, and performed individual identification based on genotypes built using nine microsatellites. We then estimated population size and individual survival relative to environmental variables, controlling for heterogeneity in capture probabilities using capture-mark-recapture modelling. Population size increased during the wet season and decreased during the dry season, while survival remained constant across the study period. Individuals captured along road-verges and around water-bodies survived longer than those captured near agricultural fields. The use of gNIS on a heterogeneous landscape such as our study area allowed us to demonstrate that human land-use activities affect Cabrera vole demographic parameters in Mediterranean farmland, with implications for conservation planning towards its long-term persistence. Our approach can be widely applied to other elusive small mammals of conservation concern, but for which informative demographic data are still scarce.
Publications in reputed peer reviewed journals are often looked upon as tenets on which future scientific thought is built. Sometimes published information can be flawed and errors in published research should be expediently reported, preferably by a peer review process. We review a recent publication by Gopalaswamy et al (2015) that challenges the use of double sampling in large scale wildlife surveys. Double sampling is often resorted to as an established economical approach for large scale surveys since it calibrates abundance indices against absolute abundance, thereby elegantly addressing the statistical shortfalls of indices. Empirical data used by Goplaswamy et al. (2015) to validate their theoretical model, relate to tiger sign and tiger abundance referred to as an Index Calibration experiment (IC-Karanth). These data on tiger abundance and signs should be paired in time and space to qualify as a calibration experiment for double sampling, but original data of IC-Karanth show lags of several years. One crucial data point used in the paper does not match the original source. We show that by use of inappropriate and incorrect data collected through a faulty experimental design, wrong parameterisation of their theoretical model, and cherry-picked estimates from literature on detection probability, the inferences of this paper are highly suspect. We highlight how the results of Goplaswamy et al. (2015) were further distorted in popular media. If left unaddressed, Gopalaswamy et al. paper could have serious implications on design of large scale studies by propagating unscientific inferences.
This study describes two longitudinal serological surveys of European Bat Lyssavirus type 1 (EBLV-1) antibodies in serotine bat (Eptesicus serotinus) maternity colonies located in the North-East of France. This species is currently considered as the main EBLV-1 reservoir. Multievent capture-recapture models were used to determine the factors influencing bat rabies transmission as this method accounts for imperfect detection and uncertainty in disease states. Considering the period of study, analyses revealed that survival and recapture probabilities were not affected by the serological status of individuals, confirming the capacity of bats to be exposed to lyssaviruses without dying. Five bats have been found with EBLV-1 RNA in the saliva at the start of the study, suggesting they were caught during virus excretion period. Among these bats, one was interestingly recaptured one year later and harbored a seropositive status. Along the survey, some others bats have been observed to both seroconvert (i.e. move from a negative to a positive serological status) and serorevert (i.e. move from a positive to a negative serological status). Peak of seroprevalence reached 34% and 70% in site A and B respectively. On one of the 2 sites, global decrease of seroprevalence was observed all along the study period nuanced by oscillation intervals of approximately 2–3 years supporting the oscillation infection dynamics hypothesized during a previous EBLV-1 study in a Myotis myotis colony. Seroprevalence were affected by significantly higher seroprevalence in summer than in spring. The maximum time observed between successive positive serological statuses of a bat demonstrated the potential persistence of neutralizing antibodies for at least 4 years. At last, EBLV-1 serological status transitions have been shown driven by age category with higher seroreversion frequencies in adults than in juvenile. Juveniles and female adults seemed indeed acting as distinct drivers of the rabies virus dynamics, hypothesis have been addressed but their exact role in the EBLV-1 transmission still need to be specified.
This chapter focuses on how to tackle uncertainty in natural resources decision making. First, it explains two important categories of uncertainty, namely irreducible and reducible uncertainty. Describing the effects of uncertainty on decision making, the chapter introduces approaches for identifying the key or important uncertainties. Next, the chapter talks about the traditional approaches to reduce uncertainty. It introduces adaptive resource management (ARM) as a special case of structured decision making that reduces uncertainty through time and improves decision making. All decision models should be evaluated using sensitivity analysis prior to choosing and implementing the optimal or satisficing decision. The chapter discusses four types of sensitivity analysis that are commonly used in natural resource decision modeling. These are one-way sensitivity analysis, two-way sensitivity analysis, response profile sensitivity analysis, and indifference curves.
This chapter presents the general motivation for a structured approach to decision making in natural resource management. It discusses the role of structured decision making (SDM) in natural resource management and common problems made when making natural resource decisions. The chapter talks about the advantages and limitations of this structured approach to decision making. It also defines the terms objective, management, decision, model, and adaptive management, each of which will be a key element in the development of a structured decision approach. Adaptive resource management (ARM) extends SDM to the case where outcomes following decisions are uncertain, which is common in natural resource management. The chapter provides a wide array of examples that are currently or potentially amenable to SDM and ARM.
This chapter focuses on the application and implementation of different approaches used to obtain optimal decisions in natural resource conservation/management scenarios when complexity is high. It classifies these approaches based on the type of decision to be made (static or dynamic/sequential), whether the resource system is treated as deterministic or stochastic, and the complexity of objectives and constraints. The chapter first presents deterministic, static decisions that can be solved using constrained or unconstrained optimization methods, including classical, linear, and nonlinear programming. Building on this, it discusses sequential decision making and adaptive resource management wherein decisions are revisited through time and/or space. It also discusses the advantages and disadvantages of these approaches and provides guidelines and suggestions to implement each.
This chapter discusses the key elements of structured decision making (SDM). These include clear development of a problem statement, elucidation of objectives, specification of decision alternatives, and establishment of boundaries (temporal, spatial) for the decision problem. The chapter explains optimal decision making and general principles for evaluating and selecting among alternative decisions. It introduces the use of predictive modeling in decision making, and discusses the issue of uncertainty. The basic ideas presented here are not only relevant to natural resource management, but are also applicable to decision making in other fields.
The chapter aims to merge the two seemingly disparate topics of statistics and decision making in a practical manner and equip researchers, managers and conservationists with necessary tools. It explains how to effectively use statistics in decision making. First, it provides an overview of statistics terms and concepts, including expectation, independence, and probability distributions. Linear models that contribute to the toolbox needed to make decisions in the face of uncertainty are presented next. The chapter describes the use of statistics to quantify uncertainty and to use data to inform views of uncertainty. Examples include using models and data to describe biological/ecological relationships in management and conservation and to develop and parameterize probability models. The chapter concludes with a discussion on advanced modeling/statistical techniques, including hierarchical modeling and Bayesian inference, which provide flexibility in creating a robust decision-making framework.
In this chapter, we turn our attention to the concept of basic study design. We begin by discussing variable classification, focusing on the types of variables: explanatory, disturbing, controlling, and randomized. We then discuss how each of these variable types is integral to wildlife study design. We then detail the necessity of randomization and replication in wildlife study design, and relate these topics to variable selection.
We outline the three major types of designs in decreasing order of rigor (i.e., manipulative experiments, quasi-experiments, and observational studies) with respect to controls, replication, and randomization, which we further elaborate in Chap. 3. We provide a general summary on adaptive management and we briefly touch on survey sampling designs for ecological studies, with a discussion on accounting for detectability, but leave detailed discussion of sampling design until Chap. 4.
We discuss the place of statistical inference in wildlife study design, focusing on parameter estimation, hypothesis testing, and model selection. We do not delve into specific aspects and applications of statistical models (e.g., generalized linear models or correlation analysis) as these are inferential, rather than design techniques. We discuss the relationships between statistical inference and sampling distributions, covering the topics of statistical accuracy, precision, and bias. We provide an outline for evaluating Type I and II errors as well as sample size determination. We end this chapter with a discussion on integrating project goals with study design and those factors influencing the design type used, and conclude with data storage techniques and methods, programs for statistical data analysis, and approaches for presenting results from research studies.
This article introduces the different types of ornithological data, obtained either at the population level (count data, occupancy data) or at the individual level (capture–mark–recapture data, radiotracking data, and geolocation data), as well as references to the modeling/analysis techniques appropriate for each type of data. It also gives a list of organizations and web sites through which such data can be made available, as well as an extensive list of references to up-to-date articles and books.
Counts of birds can inform studies with different goals, such as estimating population size, monitoring populations over time and in response to environmental change, and estimating vital rates to model population dynamics. Because estimates need to be reasonably accurate and precise, considerable thought has gone into developing counting techniques that enable robust estimation of abundance, taking into account probability of detection, which can vary between species, land cover types and over time. In recent years these have been applied to over 60 % of studies estimating bird abundance conducted in non-urban landscapes. However, robust estimation techniques are not being similarly applied to studies in urban areas. We reviewed 162 articles in which birds had been counted and abundance and/or occupancy reported in urban areas, spanning the years 1991 to 2015, and found that only 11 % attempted to account for variable detectability; few of these had modelled detectability satisfactorily. There was no indication of increasing methodological rigour over time. Counting birds in urban areas poses significant challenges; robust techniques are constrained by limitations imposed by built structures, social factors and a mosaic of many small private parcels of land. We present a framework for estimating bird abundance and discuss the strengths and weaknesses of the different approaches, relating each to the urban context. Citizen science initiatives are considered as a good fit in urban areas and are increasing in number: sampling designed for all landscapes might be inappropriate in urban areas, but counting protocols should allow the modelling of detection probability.
While the positive relationship between avian diversity and habitat heterogeneity is widely accepted, it is primarily based on observed species richness without accounting for imperfect detection. Other facets of diversity such as functional diversity are also rarely explored. We investigated the avian diversity-landscape heterogeneity relationship in agricultural landscapes by considering two aspects of diversity: taxonomic diversity (species richness) estimated from a multi-species dynamic occupancy model, and functional diversity (functional evenness [FEve] and divergence [FDiv]) based on traits of occurring species. We also assessed how agricultural lands enrolled in a conservation program managed on behalf of declining early successional bird species (hereafter CP38 fields, an agri-environment scheme) influenced avian diversity. We analyzed breeding bird data collected at CP38 fields in Mississippi, USA, during 2010–2012, and two principal components of environmental variables: a gradient of heterogeneity (Shannon’s landscape diversity index) and of the amount of CP38 fields (percent cover of CP38 fields; CP38). FEve did not show significant responses to environmental variables, whereas FDiv responded positively to heterogeneity and negatively to CP38. However, most FDiv values did not significantly differ from random expectations along an environmental gradient. When there was a significant difference, FDiv was lower than that expected. Unlike functional diversity, species richness showed a clear pattern. Species richness increased with increasing landscape heterogeneity but decreased with increasing amounts of CP38 fields. Only one species responded negatively to heterogeneity and positively to CP38. Our results suggest that the relationships between avian diversity and landscape heterogeneity may vary depending on the aspect of diversity considered: strong positive effects of heterogeneity on taxonomic diversity, but weakly positive or non-significant effects on functional diversity. Our results also indicate that effectiveness of CP38 in conserving avian diversity, particularly, taxonomic diversity, could be limited without the consideration of landscape heterogeneity.
There have been relatively few studies on the population structure of species from countries having high levels of biodiversity, such as Brazil, even though most species are at some risk of extinction. Precise estimates of population parameters, such as abundance and survival probability, are necessary for evaluating the status of populations and developing management policies for species and their associated habitats. Here, we used mark-recapture methods to model the demography of the sand lizard, Liolaemus arambarensis, an endemic species of southern Brazil. Specifically, we estimated population size, survival probability, and sex ratio of three populations, while accounting for imperfect individual detectability. Our goal was to evaluate how the population structure of this species reflects its conservation status according to the International Union for Conservation of Nature (IUCN) Red List. The estimated population size of mature individuals was higher than 250 individuals but lower than 2500 individuals; therefore, the species should be categorized as Endangered by IUCN population criteria. Monthly survival probability of adult females, adult males, and juveniles was relatively high (ranging from 0.74 to 0.85), whereas capture and recapture probabilities were low (ranging from 0.01 to 0.22). The accelerated degradation and alteration of sand lizard habitat, restinga areas of the Southern Coastal Plain, highlight the importance of long-term monitoring to detect future patterns of fluctuation and possible population declines of its few known populations.
The interaction of local populations has been the focus of an increasing number of studies in the past 30 years. The study of source‐sink dynamics has especially generated much interest. Many of the criteria used to distinguish sources and sinks incorporate the process of apparent survival (i.e., the combined probability of true survival and site fidelity) but not emigration. These criteria implicitly treat emigration as mortality, thus biasing the classification of sources and sinks in a manner that could lead to flawed habitat management. Some of the same criteria require rather restrictive assumptions about population equilibrium that, when violated, can also generate misleading inference. Here, we expand on a criterion (denoted “contribution” or \documentclass{aastex} \usepackage{amsbsy} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{bm} \usepackage{mathrsfs} \usepackage{pifont} \usepackage{stmaryrd} \usepackage{textcomp} \usepackage{portland,xspace} \usepackage{amsmath,amsxtra} \usepackage[OT2,OT1]{fontenc} \newcommand\cyr{ \renewcommand\rmdefault{wncyr} \renewcommand\sfdefault{wncyss} \renewcommand\encodingdefault{OT2} \normalfont \selectfont} \DeclareTextFontCommand{\textcyr}{\cyr} \pagestyle{empty} \DeclareMathSizes{10}{9}{7}{6} \begin{document} \landscape $C^{r}$ \end{document} ) that incorporates successful emigration in differentiating sources and sinks and that makes no restrictive assumptions about dispersal or equilibrium processes in populations of interest. The metric \documentclass{aastex} \usepackage{amsbsy} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{bm} \usepackage{mathrsfs} \usepackage{pifont} \usepackage{stmaryrd} \usepackage{textcomp} \usepackage{portland,xspace} \usepackage{amsmath,amsxtra} \usepackage[OT2,OT1]{fontenc} \newcommand\cyr{ \renewcommand\rmdefault{wncyr} \renewcommand\sfdefault{wncyss} \renewcommand\encodingdefault{OT2} \normalfont \selectfont} \DeclareTextFontCommand{\textcyr}{\cyr} \pagestyle{empty} \DeclareMathSizes{10}{9}{7}{6} \begin{document} \landscape $C^{r}$ \end{document} is rooted in the theory of matrix population models, yet it also contains clearly specified parameters that have been estimated in previous empirical research. We suggest that estimates of emigration are important for delineating sources and sinks and, more generally, for evaluating how local populations interact to generate overall system dynamics. This suggestion has direct implications for issues such as species conservation and habitat management.
Biodiversity in tropical regions is particularly high and may be highly sensitive to climate change. Unfortunately, a lack of long-term data hampers understanding of how tropical species, especially animals, may react to projected environmental changes. The amount and timing of rainfall is key to the function of tropical ecosystems and, although specific model predictions differ, there is general agreement that rainfall regimes will change over large areas of the tropics. Here, we estimate associations between dry season length (DSL) and the population biology of 20 bird species sampled in central Panama over a 33-year period. Longer dry seasons decreased the population growth rates and viability of nearly one-third of the species sampled. Simulations with modest increases in DSL suggest that consistently longer dry seasons will change the structure of tropical bird communities. Such change may occur even without direct loss of habitat - a finding with fundamental implications for conservation planning. Systematic changes in rainfall regime may threaten some populations and communities of tropical animals even in large tracts of protected habitat. These findings suggest the need for collaboration between climate scientists and conservation biologists to identify areas where rainfall regimes will be able to plausibly maintain wildlife populations. © 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
Avian habitat selection occurs at multiple spatial scales to incorporate life history requirements. Breeding habitat of Golden-winged Warblers (Vermivora chrysoptera) is characterized by largely forested landscapes containing natural or anthropogenic disturbance elements that maintain forest patches in early stages of succession. Breeding habitat occurs in a variety of settings, including shrub and forest swamps, regenerating forests following timber harvest, grazed pastures, and reclaimed mined lands. We identified structural components of nest sites for Golden-winged Warblers by measuring habitat characteristics across five states (North Carolina, New York,
Pennsylvania, Tennessee, and West Virginia) in the Appalachian breeding-distribution segment and two states (Minnesota and Wisconsin) in the Great Lakes breeding-distribution segment. We
measured habitat characteristics at the nest-site scale with a series of nested plots characterizing herbaceous vegetation (grasses and forbs), woody shrubs and saplings, and overstory trees. We measured
similar variables at paired random plots located 25–50 m from the nest within the same territory to evaluate selection. We used conditional logistical regression to identify which parameters were important in habitat selection and Simple Saddlepoint Approximation (SSA) to aid in management interpretation of identified parameters for each study site. Study site was an important determinant for which parameters were significant in nest-site selection, although selection for some parameters was consistent across sites. The amount of woody cover at the nest-site scale was consistently present in the top nest-site selection models across sites, although the direction of the relationship was not the same across all sites. We also identified grass, forb, woody cover, and vegetation density as important components of Golden-winged Warbler nest-site selection. Based on SSA, we identified vegetation thresholds to aid in designing habitat management prescriptions to promote creation or restoration of Golden-winged Warbler nesting habitat across the eastern portion of their breeding distribution.
We used southeastern fox squirrels (Sciurus niger) in the southeastern United States as an example of how modern approaches to estimate density coupled with a reevaluation of previous estimates can provide important new insights into the management and conservation of mammals. There are few rigorous density estimates of southeastern fox squirrels, which hinders our ability to manage and conserve their populations. Based on an initial estimate from 1957 of 38 squirrels/km 2 and subsequent decreases in estimates of population densities, noted decreases in hunter harvest reports, and anecdotal observations, southeastern fox squirrels are believed to be declining. To assess the extent of this decline, we first estimated the density of a subspecies of southeastern fox squirrel, Sherman's fox squirrel (S. n. shermani), using live trapping and camera trapping and modern analytical approaches for mark–recapture analysis. Then, to compare our densities to previous work, we calculated a standardized effective survey area correction factor for past studies and recalculated their population densities. Once standardized, we found little temporal or geographic variation in densities of southeastern fox squirrels (2.4–8.5 squirrels/km 2) spanning nearly 70 years of research. Past densities were substantially lower than initially reported with corrected survey areas, suggesting that densities may have always been naturally low but were incorrectly inflated due to study designs and statistical approaches. Moreover, corrected densities from all studies were correlated with the bounded survey area, suggesting that researches aiming to estimate population densities of southeastern fox squirrels were frequently conducted at scales too small relative to the size of their home ranges. The use of methodological and analytical approaches such as those used in this study may help to avoid misdirected conservation designations or management actions and misuse of conservation funding.
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