Table 2 - uploaded by Brock D Hoenes
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Mean number (SE) of desert mule deer and oryx fecal-pellet groups per transect for each habitat types in the greater San Andres Mountains, New Mexico, 2004–2006.
Source publication
Introduced oryx (Oryx gazella gazella) have expanded into the San Andres Mountains of south-central New Mexico, but little is known of concurrent habitat used by oryx and desert mule deer (Odocoileus hemionus crooki); the latter in New Mexico is a species of special concern that has declined signifi cantly since the introduction of oryx. We used fe...
Citations
... Ten replicates were run for both models and logistic outputs were selected using Baggenstoss 2018 [2] ; Presse et al. 2013 [39] ; Phillips and Dudik 2008. Jackknife approach was adopted to determine the importance of the variables used in the model (Hoenes and Bender 2010; Yost et al. 2009) [20,48] . Receiver operating characteristic (ROC) analyses was used to evaluate the reliability and predictive performance of the models (Pearce and Ferrier 2000) [33] . ...
Himalayan Bulbul Pycnonotus leucogenys is widely spread in the Himalayan Siwalik range but information is available on its migratory behavioural patterns. Hence, a two-year study was undertaken to investigate its distribution range of the species in the summer and winter seasons in Punjab and Himachal Pradesh. A total of twenty variables including 19 bioclimatic variables and elevation were selected for the development of Species distribution Modelling (SDM). Occurrence records of P. leucogenys in summer and winter were processed at the Maximum entropy model (MaxEnt) using the ENMeval data package. The results suggested a downward movement from the upper Siwalik Himalayan ranges to the lower Siwalik in winter, and with return migration movement in summer. However, a large fraction of the distribution range was found overlapping in both the models, which suggests partial altitudinal migration of P. leucogenys. The data recorded on the ground subordinate the finding of the model. A wide range of tolerance for bioclimatic variables was observed in P. leucogenys, however temperature-related factors played a vital role in the variation in the species distribution range in the annual cycle of partial migration. The finding of this study would be a valuable reference for future studies on ecological and behavioral aspects of partial-migration of bird species in the Himalaya.
... The selection process is often based on one of these two approaches: the AUC approach and the Jackknife test. The AUC approach used by Hoenes and Baldwin [122,123] is based on the AUC score of listed models built from the most general to the parsimonious one. The best model selected is the one with the least variables with the best AUC score. ...
Ticks and tick-borne diseases (TTBD) are constraints to the development of livestock and induce potential human health problems. The worldwide distribution of ticks is not homogenous. Some places are ecologically suitable for ticks but they are not introduced in these areas yet. The absence or low density of hosts is a factor affecting the dissemination of the parasite. To understand the process of introduction and spread of TTBD in different areas, and forecast their presence, scientists developed different models (e.g., predictive models and explicative models). This study aimed to identify models developed by researchers to analyze the TTBD distribution and to assess the performance of these various models with a meta-analysis. A literature search was implemented with PRISMA protocol in two online databases (Scopus and PubMed). The selected articles were classified according to country, type of models and the objective of the modeling. Sensitivity, specificity and accuracy available data of these models were used to evaluate their performance using a meta-analysis. One hundred studies were identified in which seven tick genera were modeled, with Ixodes the most frequently modeled. Additionally, 13 genera of tick-borne pathogens were also modeled, with Borrelia the most frequently modeled. Twenty-three different models were identified and the most frequently used are the generalized linear model representing 26.67% and the maximum entropy model representing 24.17%. A focus on TTBD modeling in Africa showed that, respectively, genus Rhipicephalus and Theileria parva were the most modeled. A meta-analysis on the quality of 20 models revealed that maximum entropy, linear discriminant analysis, and the ecological niche factor analysis models had, respectively, the highest sensitivity, specificity, and area under the curve effect size among all the selected models. Modeling TTBD is highly relevant for predicting their distribution and preventing their adverse effect on animal and human health and the economy. Related results of such analyses are useful to build prevention and/or control programs by veterinary and public health authorities.
... 6,000 years is unlikely. Nonnative South African oryx (Oryx gazella gazella) also occur in the Tularosa Basin but are unlikely to graze the Malpais Spring ciénega habitat (Smith et al., 1998;Hoenes and Bender, 2010), and we have not observed any noticeable herbivory effect of that species on wetland vegetation in the ciénega. Alternatively, hydrodynamics may have been an important factor limiting emergent wetland plant cover, considering that lotic habitat likely predominated in the natural spring outflow channel (Fig. 1) before anthropogenic alteration. ...
... Moreover, declines in native desert mule deer (Odocoileus hemionus eremicus) coincided with increases in oryx numbers (Edgington 2009, Bender et al. 2017. While direct competition was not considered to be an issue (Hoenes and Bender 2010), apparent competition via disease may have been. Oryx introduced exotic diseases that could potentially affect native ungulates , and oryx can serve as an amplifying host for native diseases , Bender et al. 2017. ...
Introductions of exotic species can benefit certain publics but can also have many unanticipated consequences. South African oryx (Oryx gazella gazella) were introduced into the Chihuahuan Desert on White Sands Missile Range (WSMR), New Mexico, USA to alleviate a perceived lack of large mammal hunting opportunities. Because of conflicts with oryx as the population increased, we modeled population growth and determined survival of radio-collared oryx to identify rates of population increase, limiting factors to population growth, and levels of harvest necessary to control population growth. Following introductions in 1969-1977, oryx significantly increased their range and showed a rate of increase of approximately λ = 1.22 through 2000, reaching approximately 3,500 individuals. This rate was marginally positively influenced by total precipitation received the previous year and near the species maximum based on fecundity (approx. λ = 1.26-1.29). In response to concerns over conflicts with oryx on WSMR, intensive studies of oryx limiting factors, 2001-2003, found annual survival of oryx excluding recovered harvest was ≥ 0.95 for adults and 1.00 for subadults. Most of the mortality was unrecovered hunting loss, further indicating that oryx had few nonhuman limiting factors. Modeling of the oryx population indicated that adult female harvest must be approximately 0.22-0.25 to control population growth. Intensive harvesting aimed at controlling oryx numbers removed an average of 30 (SE = 2.0)% of the estimated population, 2001-2013, which decreased the population to approximately 1,700 by 2013. Decreased harvest intensity from 2014-2017 to approximately 16 (SE = 1.6)% of the population subsequently allowed oryx to increase again at approximately λ = 1.14, rebounding to around 2,900 by 2017. Introduction of oryx succeeded in increasing recreational opportunities and revenue for management agencies. However, negative impacts on military missions, vehicle-oryx collisions, possible disease impacts on native ungulates, impacts on protected areas, the logistics of managing hunting programs on a closed military reservation, and optimizing oryx-related revenues continue to be significant management challenges.
... The Caballo and San Andres ranges share a similar suite of ungulates including mule deer (Odocoileus hemionus) and javelina (Pecari tajacu), and predators including coyote (Canis latrans), mountain lion (Puma concolor), and bobcat (Lynx rufus). Oryx (Oryx gazella), an African antelope species, is present on WSMR on low elevation footslopes of the San Andres range (Hoenes and Bender 2010). Mule deer densities are similarly low in both ranges. ...
Foraging behavior affects animal fitness and is largely dictated by the resources available to an animal. Understanding factors that affect forage resources is important for conservation and management of wildlife. Cattle sympatry is proposed to limit desert bighorn population performance, but few studies have quantified the effect of cattle foraging on bighorn forage resources or foraging behavior by desert bighorn. We estimated forage biomass for desert bighorn sheep in 2 mountain ranges: the cattle-grazed Caballo Mountains and the ungrazed San Andres Mountains, New Mexico. We recorded foraging bout efficiency of adult females by recording feeding time/step while foraging, and activity budgets of 3 age-sex classes (i.e., adult males, adult females, yearlings). We also estimated forage biomass at sites where bighorn were observed foraging. We expected lower forage biomass in the cattle-grazed Caballo range than in the ungrazed San Andres range and lower biomass at cattle-accessible versus inaccessible areas within the Caballo range. We predicted bighorn would be less efficient foragers in the Caballo range. Groundcover forage biomass was low in both ranges throughout the study (Jun 2012-Nov 2013). Browse biomass, however, was 4.7 times lower in the Caballo range versus the San Andres range. Bighorn in the Caballo range exhibited greater overall daily travel time, presumably to locate areas of higher forage abundance. By selecting areas with greater forage abundance, adult females in the Caballo range exhibited foraging bout efficiency similar to their San Andres counterparts but lower overall daily browsing time. We did not find a significant reduction in forage biomass at cattle-accessible areas in the Caballo range. Only the most rugged areas in the Caballo range had abundant forage, potentially a result of intensive historical livestock use in less rugged areas. Forage conditions in the Caballo range apparently force bighorn to increase foraging effort by feeding only in areas where adequate forage remains.
... En la literatura existen diversas vías que se han propuesto para la selección de los mejores modelos (ej. Baldwin 2009, Hoenes & Bender 2010, Fitzpatrick et al. 2013, las cuales son producto de la constante evolución debido a las críticas y a la rapidez con que se desarrollan las nuevas tendencias en el ámbito de la modelación de nicho ecológico y los modelos de distribución geográfica potencial (Warren & Seifert 2011, Royle et al. 2012, Yackulic et al. 2013. Pero a la fecha, es aún difícil justificar cuál de las metodologías propuestas es la más adecuada para seleccionar los mejores modelos con MaxEnt, ni bajo qué condiciones es más propicio usar una u otra, ya que en primera instancia depende de los objetivos particulares de cada investigación (Peterson et al. 2011). ...
Topographic and climatic variables affect demographic rates and habitat selection of
many species, so these variables are useful for generating potential distribution models. In this research,
the most important topographic and climatic variables were identified for the generation of Yucatan
parrot (Amazona xantholora) and Yellow-headed Parrot (Amazona oratrix) potential geographical distribution
model in the Yucatan Peninsula. Models were generated using MaxEnt (algorithm based on
Maximum Entropy). The presence records used were obtained from different available data bases from
The National Commission of Natural Protected Areas (CONANP) and El Colegio de la Frontera Sur.
One hundred replicas of models per species were developed and the best model selected is representing
the median value. Different thresholds were applied to the best model in order to obtain a presence/
absence map. Presence and absence records taken from field work were used to validate the model. To
know which of the variables contributed the most in the models generation, the ones with greater contribution
in the variables Jackknife with the AUC data and because of the contribution percentage of each
one, were selected. Amazona oratrix is potentially distributed in the southwestern portion of the Yucatan
Peninsula. Amazona xantholora is present around much of the Peninsula, including the Cozumel Island,
but it is absent within the areas where A. oratrix is located. The variable with greater contribution to A.
oratrix model was the average temperature of the driest quarter, while for A. xantholora were the digital
elevation model and the precipitation of the wettest and coldest quarters. For none of the two species it is
observed a direct relationship between vegetation gradient established within the northern and southern
of the Peninsula, and the potential geographical distribution area. The particular knowledge about the
environmental factors that influence the potential geographical distribution of these species can be useful
for conservation actions and for future evaluation of the changes that such distribution might suffer.
... En la literatura existen diversas vías que se han propuesto para la selección de los mejores modelos (ej. Baldwin 2009, Hoenes & Bender 2010, Fitzpatrick et al. 2013, las cuales son producto de la constante evolución debido a las críticas y a la rapidez con que se desarrollan las nuevas tendencias en el ámbito de la modelación de nicho ecológico y los modelos de distribución geográfica potencial (Warren & Seifert 2011, Royle et al. 2012, Yackulic et al. 2013. Pero a la fecha, es aún difícil justificar cuál de las metodologías propuestas es la más adecuada para seleccionar los mejores modelos con MaxEnt, ni bajo qué condiciones es más propicio usar una u otra, ya que en primera instancia depende de los objetivos particulares de cada investigación (Peterson et al. 2011). ...
Topographic and climatic variables affect demographic rates and habitat selection of many species, so these variables are useful for generating potential distribution models. In this research, the most important topographic and climatic variables were identified for the generation of Yucatan parrot (Amazona xantholora) and Yellow-headed Parrot (Amazona oratrix) potential geographical distribution model in the Yucatan Peninsula. Models were generated using MaxEnt (algorithm based on Maximum Entropy). The presence records used were obtained from different available data bases from The National Commission of Natural Protected Areas (CONANP) and El Colegio de la Frontera Sur. One hundred replicas of models per species were developed and the best model selected is representing the median value. Different thresholds were applied to the best model in order to obtain a presence/ absence map. Presence and absence records taken from field work were used to validate the model. To know which of the variables contributed the most in the models generation, the ones with greater contribution in the variables Jackknife with the AUC data and because of the contribution percentage of each one, were selected. Amazona oratrix is potentially distributed in the southwestern portion of the Yucatan Peninsula. Amazona xantholora is present around much of the Peninsula, including the Cozumel Island, but it is absent within the areas where A. oratrix is located. The variable with greater contribution to A. oratrix model was the average temperature of the driest quarter, while for A. xantholora were the digital elevation model and the precipitation of the wettest and coldest quarters. For none of the two species it is observed a direct relationship between vegetation gradient established within the northern and southern of the Peninsula, and the potential geographical distribution area. The particular knowledge about the environmental factors that influence the potential geographical distribution of these species can be useful for conservation actions and for future evaluation of the changes that such distribution might suffer.
... Roads may positively or negatively influence deer distribution through either avoidance [43], or possible attraction because some plants eaten by white-tailed deer might be abundant near roads [44]. Similarly, water sources have been regarded as a key habitat element for the species [18]. ...
We analyzed potential distribution patterns in the northernmost distribution of American tropical forests in Eastern Mexico of three ungulate species including Odocoileus virginianus, Mazama temama and Pecari tajacu, in response to several physical, climatic, biological, and anthropogenic variables in order to identify environmental factors related to potential distribution and potential key areas for ungulate conservation. Current presence records for these species were gathered, and potential distribution models were built using Maximum Entropy niche modeling (MaxEnt). Model suitability surfaces were used to calculate remaining potential habitat areas in the region, as well as the potential sympatric area and representation of these areas in Natural Protected Areas. Biological and anthropogenic variables were the best species distribution predictors. Landscape composition (the proportion of different land-use and land-cover classes: forest, agriculture, and pasture) within approximately 120 ha, was the most important variable for all models, influencing each species differently with respect to their tolerance to altered habitats. Remaining potential area of all three species is fragmented and has apparently been nearly lost in plains (<14% remaining). Distribution models allowed us to detect an important surface at the western portion of our study area which may function as a large biological corridor that promotes connectivity at the Sierra Madre Oriental mastogeographic province in a region heavily transformed by land use change. In this context of habitat transformation, management focused on promoting quality matrix at the landscape level, promises to be a viable alternative for ungulates conservation in tropical regions of Mexico.
... Similarly, management actions aimed at mitigating any single mortality factor are unlikely to aff ect deer survival in the SAM because of low cause-specifi c mortality rates associated with any single mortality factor and the predisposition associated with poor condition. Because densities of other large herbivores are low in the SAM, competition is also likely having litt le eff ect on deer condition and, thus, survival (Hoenes and Bender 2010). ...
Mule deer numbers have declined precipitously in the San Andres Mountains of southcentral New Mexico. To assess reasons for population declines, we monitored condition, survival, and causes of mortality for a range of 37 to 64 radio-collared, >1.5-year-old female mule deer annually, and a range of 14 to 31 radio-collared, >1.5-year-old male mule deer annually from 2003 to 2009, and modeled environmental factors affecting survival. We found annual survival rates of 0.74 to 0.86 for females and 0.74 to 0.92 for males, rates that were similar among years within sexes. Causes of mortality for 50 radio-collared females and 22 radio-collared males included predation (13 females, 2 males), accidents (4 females, 1 male), malnutrition (13 females, 7 males), disease (6 females, 2 males), unknown-not-predation (3 females, 6 males), unknown (11 females, 3 males), and harvest (0 females, 1 male). Condition of females varied among years and was poor in most years (i.e., lactating females had1.0] > 0.937). Potential rates of increase of mule deer in the greater San Andres Mountains were limited by production and survival of fawns, rather than adult mortality.
... For example, is a species more likely to be found closer to water, at sites with greater annual precipitation, or at lower elevations? Because Maxent is an exponential model, the probability assigned to a location is proportional to the exponential of the selected combination of variables, thus allowing construction of response curves to illustrate the effect of selected variables on probability of use [10][11][12][13]. These response curves consist of a chart with specified metrics for the variable in question represented on the x-axis and the predicted probability of suitable conditions as defined by the logistic output when all other variables are set to their average values over all other presence locations along the y-axis [11]. ...
... There are only two published attempts that I am familiar with that addressed this topic. One involved the use of a critical ratio test that compared the most general model (i.e., containing all variables) to more parsimonious models [10,13]. This approach used AUC scores and associated SE's to derive a Z-score to determine if competing models were different [30]. ...
... Developing methods for model selection would also have great utility for wildlife research, particularly when trying to discern which variable or combination of variables has the greatest influence on the distribution of the species of interest. Many variables that are included in full Maxent models often have little known influence on distributional patterns [10,12,13]. Their elimination from distributional models would simplify the management of the species of interest and may increase the generality of the model over a broader area by decreasing the potential for overfitting the model. ...
Maximum entropy (Maxent) modeling has great potential for identifying distributions and habitat selection of wildlife given its reliance on only presence locations. Recent studies indicate Maxent is relatively insensitive to spatial errors associated with location data, requires few locations to construct useful models, and performs better than other presence-only modeling approaches. Further advances are needed to better define model thresholds, to test model significance, and to address model selection. Additionally, development of modeling approaches is needed when using repeated sampling of known individuals to assess habitat selection. These advancements would strengthen the utility of Maxent for wildlife research and management.
