No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Flat-tailed Horned Lizard (Phrynosoma mcallii) Occupancy Monitoring within the Yuma Desert Management Area, Arizona: 2021 Final Report
Abstract
The Arizona Game and Fish Department completed the eleventh consecutive season of Flat-tailed Horned Lizard (Phrynosoma mcallii) occupancy monitoring within the Yuma Desert Management Area (YDMA) in 2021. Occupancy and demographic monitoring are included within the larger range-wide effort outlined by the Flat-tailed Horned Lizard Interagency Coordinating Committee (FTHL ICC 2013). Occupancy and demographic monitoring support management and conservation strategies developed by the Flat-tailed Horned Lizard Interagency Coordinating Committee, whose primary objective is to "maintain self-sustaining populations" (FTHL ICC 2003). In order to meet this objective, reduce threats, and eliminate the potential need for federal listing under the Endangered Species Act, a strategy was devised to provide a standardized assessment of Flat-tailed Horned Lizard populations throughout the species’ range
to determine whether the population is stable, increasing, or decreasing (FTHL ICC 2003, 2008, 2009, 2013). Occupancy modeling within five management areas and one research area is part of that strategy. This report summarizes the 2021 occupancy season for the YDMA and provides an analysis of occupancy monitoring efforts from 2011 through 2021 to provide the most current information for trends in the YDMA of Arizona.
ResearchGate has not been able to resolve any citations for this publication.
Range-wide analysis for long-term monitoring of the Flat-tailed Horned Lizard.
A part of a larger scale long-term monitoring plan by the FTHL Interagency Coordinating Committee for the conservation and management of Flat-tailed Horned Lizards across their range. This specific report shows the long-term occupancy monitoring results from 2011 to 2019.
Demography and occupancy monitoring in the Yuma Management Area up to 2014.
Range-wide PVA on Flat-tailed Horned Lizard populations
Monitoring programs for species of conservation concern are notoriously flawed. Notably, many monitoring programs do not establish trigger points or a level of decline in population size that will result in management action. Here we report on the monitoring program for the Flat-tailed Horned Lizard (Phrynosoma mcallii) that has been established across its range throughout the United States by the Flat-tailed Horned Lizard Interagency Coordinating Committee (FTHL ICC). Importantly, we examine whether a trigger point of 30% decline was detected in these populations. Between 2007 and 2013, we detected 955 P. mcallii on 2,714 occupancy surveys and captured 715 individuals on 1,861 demographic surveys. Occupancy surveys have demonstrated that the species occurs throughout the management areas and occupancy estimates range from 0.25–0.89. Demographic surveys have demonstrated that population trends over time are correlated across all management areas; however, they are probably driven by factors at smaller geographic scales. During the study no population decline > 30% was detected after accounting for natural and stochastic fluctuations. Continued monitoring is called for to gain a greater understanding of what is driving the trends in populations both range-wide and at the scale of management areas.
Few species are likely to be so evident that they will always be detected when present. Failing to allow for the possibility that a target species was present, but undetected, at a site will lead to biased estimates of site occupancy, colonization, and local extinction probabilities. These population vital rates are often of interest in long-term monitoring programs and metapopulation studies. We present a model that enables direct estimation of these parameters when the probability of detecting the species is less than 1. The model does not require any assumptions of process stationarity, as do some previous methods, but does require detection/nondetection data to be collected in a manner similar to Pollock's robust design as used in mark–recapture studies. Via simulation, we show that the model provides good estimates of parameters for most scenarios considered. We illustrate the method with data from monitoring programs of Northern Spotted Owls (Strix occiden-talis caurina) in northern California and tiger salamanders (Ambystoma tigrinum) in Min-nesota, USA.
MARK provides parameter estimates from marked animals when they are re-encountered at a later time as dead recoveries, or live recaptures or re-sightings. The time intervals between re-encounters do not have to be equal. More than one attribute group of animals can be modelled. The basic input to MARK is the encounter history for each animal. MARK can also estimate the size of closed populations. Parameters can be constrained to be the same across re-encounter occasions, or by age, or group, using the parameter index matrix. A set of common models for initial screening of data are provided. Time effects, group effects, time x group effects and a null model of none of the above, are provided for each parameter. Besides the logit function to link the design matrix to the parameters of the model, other link functions include the log—log, complimentary log—log, sine, log, and identity. The estimates of model parameters are computed via numerical maximum likelihood techniques. The number of parameters that are estimable in the model are determined numerically and used to compute the quasi-likelihood AIC value for the model. Both the input data, and outputs for various models that the user has built, are stored in the Results database which contains a complete description of the model building process. It is viewed and manipulated in a Results Browser window. Summaries available from this window include viewing and printing model output, deviance residuals from the model, likelihood ratio and analysis of deviance between models, and adjustments for over dispersion. Models can also be retrieved and modified to create additional models. These capabilities are implemented in a Microsoft Windows 95 interface. The online help system has been developed to provide all necessary program documentation.
We argue that model selection uncertainty should be fully incorporated into statistical inference whenever estimation is sensitive to model choice and that choice is made with reference to the data. We consider different philosophies for achieving this goal and suggest strategies for data analysis. We illustrate our methods through three examples. The first is a Poisson regression of bird counts in which a choice is to be made between inclusion of one or both of two covariates. The second is a line transect data set for which different models yield substantially different estimates of abundance. The third is a simulated example in which truth is known.
Difficulty in monitoring the flat-tailed horned lizard (Phrynosoma mcallii) has led to controversy over its conservation status. The difficulty in detecting this species has discouraged large-scale estimates of abundance and led to uncertainty over whether the species exists in population sizes of sufficient size for long-term persistence. We incorporated detection probability into monitoring of this species using closed mark—recapture and distance-sampling methods. Density estimation from mark—recapture abundance estimates was improved using an estimate of the proportion of time lizards were on the plot. We estimated the probability of detection on the line for distance sampling and adjusted density estimates accordingly. We estimated the populations of the Yuha Basin Management Area in 2002 and the East Mesa Management Area, Imperial County, California, USA, in 2003 to be 25,514 (95% CI 14,444-38,970) and 42,619 (95% CI 23,161-67,639), respectively. Two estimates of detection probability on the line in distance sampling by different methods were 0.45 and 0.65. Density estimates derived from distance analyses for 3 East Mesa Management Area plots and the Yuha Basin Management Area were 1.55 per ha (95% CI 0.64-3.76) and 0.41 per ha (95% CI 0.22-0.7), respectively. These are the first large-scale estimates of abundance and density for P. mcallii.
Information theory and an extension of the maximum likelihood principle
- H Akaike
Akaike, H. 1973. Information theory and an extension of the maximum likelihood principle. In:
Petrove, B.N and F. Csaki (Eds.); Proceedings of the 2 nd International Symposium on
Information Theory, Akademiai Kiado, Budapest, 267 -281.
Flat-tailed Horned Lizard occupancy survey protocol: Update. Arizona Game and Fish Department, Wildlife Contracts Branch
- D J Leavitt
- W L Crumbo
- A A Grimsley
Leavitt, D. J., W. L. Crumbo, and A. A. Grimsley. 2015b. Flat-tailed Horned Lizard occupancy
survey protocol: Update. Arizona Game and Fish Department, Wildlife Contracts Branch,
Phoenix, Arizona.
Flat-tailed Horned Lizard (Phrynosoma mcallii) occupancy surveys in the Yuma Desert Management Area
- D J Leavitt
- D P Sturla
- M F Ingraldi
Leavitt, D. J., D. P. Sturla, and M. F. Ingraldi. 2013. Flat-tailed Horned Lizard (Phrynosoma
mcallii) occupancy surveys in the Yuma Desert Management Area, Arizona. Final
Report. Arizona Game and Fish Department, Phoenix, Arizona.
Digital Soil Subgroup map of selected areas of the Barry M
- T Nauman
Nauman, T., 2009. Digital Soil Subgroup map of selected areas of the Barry M. Goldwater
Range for Arizona Game and Fish: Unpublished Report, AZGF PO #E0075088, 7 p.
National Oceanic and Atmospheric Administration
NOAA. National Oceanic and Atmospheric Administration. 2019. Data from MCAS Yuma,
Arizona Weather Station. Subset used: January 1, 2011 to November 9, 2019. Accessed
November 13, 2019. https://www.noaa.gov/
Disturbance mapping of the Flat-tailed Horned Lizard Yuma Management Area. Unpublished Report. U.S. Geological Survey Open File Report Cooperation with the University of Arizona
- M L Villarreal
Villarreal, M. L. 2014. Disturbance mapping of the Flat-tailed Horned Lizard Yuma
Management Area. Unpublished Report. U.S. Geological Survey Open File Report
Cooperation with the University of Arizona, School of Natural Resources and the
Environment.