An integrated modeling approach for assessing management objectives for mule deer in central British Columbia

Hatter, Ian W.; Dielman, Patrick; Kuzyk, Gerald W.

doi:10.1002/wsb.781

Cited by 5 publications

(5 citation statements)

References 27 publications

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“…To maximize the generality of our results, we included in our simulations two different life-histories and simulated data for 15 years, corresponding to a typical duration of IPM studies, which is often between 10 and years (20 years: Tenan et al 2017, 16 years: Plard et al 2020, 15 years: Lieury et al 2015Hatter et al 2017;Fay et al 2019, 14 years: Duarte et al 2016, 12 years: Brommer et al 2017, 11 years: Cleasby et al 2017, even if some studies last longer (22 years: Tempel et al 2014, 30 years: Margalida et al 2020 or shorter (7 years, Duarte et al 2017). We chose to simulate a relative large number of individuals (300 individuals) compared to population sizes of empirical IPMs which was often between 20 and 300 individuals (in the articles cited above).…”

Section: Generality and Limits Of Our Resultsmentioning

confidence: 99%

Consequences of violating assumptions of integrated population models on parameter estimates

2021

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While ecologists know that models require assumptions, the consequences of their violation become vague as model complexity increases. Integrated population models (IPMs) combine several datasets to inform a population model and to estimate survival and reproduction parameters jointly with higher precision than is possible using independent models. However, accuracy actually depends on an adequate fit of the model to datasets. We first investigated bias of parameters obtained from integrated population models when specific assumptions are violated. For instance, a model may assume that all females reproduce although there are non-breeding females in the population. Our second goal was to identify which diagnostic tests are sensitive to detect violations of the assumptions of IPMs. We simulated data mimicking a short- and a long-lived species under five scenarios in which a specific assumption is violated. For each simulated scenario, we fitted an IPM that violates the assumption (simple IPM) and an IPM that does not violate each specific assumption. We estimated bias and uncertainty of parameters and performed seven diagnostic tests to assess the fit of the models to the data. Our results show that the simple IPM was quite robust to violation of many assumptions and only resulted in small bias of the parameter estimates. Yet, the applied diagnostic tests were not sensitive to detect such small bias. The violation of some assumptions such as the absence of immigrants resulted in larger bias to which diagnostic tests were more sensitive. The parameters informed by the least amount of data were the most biased in all scenarios. We provide guidelines to identify misspecified models and to diagnose the assumption being violated. Simple models should often be sufficient to describe simple population dynamics, and when data are abundant, complex models accounting for specific processes will be able to shed light on specific biological questions.

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Section: Generality and Limits Of Our Resultsmentioning

confidence: 99%

Consequences of violating assumptions of integrated population models on parameter estimates

2021

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“…In general, Bayesian population analysis allows for a flexible framework to estimate demographic parameters and can be easily parameterized for the R/M equation (Hatter and Bergerud , Hatter et al ). Bayesian estimation methods are also advantageous over frequentist methods used to estimate woodland caribou demographic rates because they can easily share information across years to provide parameter estimates in years with missing data or provide shrinkage estimates when accurate estimates are difficult to obtain (Kéry and Schaub ).…”

Section: Methodsmentioning

confidence: 99%

Web‐based application for threatened woodland caribou population modeling

et al. 2019

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Woodland caribou (Rangifer tarandus caribou) are threatened in Canada, with population and distribution declines evident in most regions of the country. Causes of declines are linked to landscape change from forest fires and human development, notably forestry oil and gas activities, which result in caribou habitat loss and affect ecosystem food webs. The Federal Species at Risk Act requires effective protection and restoration of caribou habitat, with actions to increase caribou survival. These requirements call for effective monitoring of caribou population trends to gauge success. Many woodland caribou populations are nearly impossible to count using traditional aerial survey methods, but demographic‐based monitoring approaches can be used to estimate population trends based on population modeling of vital rates from marked animals. Monitoring programs have used a well‐known simple population model (the Recruitment‐Mortality [R/M] equation) to estimate demographic rates for woodland caribou, but have faced challenges in managing large data streams and providing transparency in the demographic estimation process. We present a stand‐alone statistical software application using open‐source software to permit efficient, transparent, and replicable demographic estimation for woodland caribou populations. We developed an easy‐to‐use, interactive web‐based application for the R/M population model that uses a Bayesian estimation approach and provides the user flexibility in choice of prior distributions and other output features. We illustrate the web‐application to the A la Pêche Southern Mountain (Central Group) woodland caribou population in west‐central Alberta, Canada, during 1998–2017. Our estimates of population demographics are consistent with previous research on this population and highlight the utility of the application in assessing caribou population responses to species recovery actions. We provide example data, computer code, the web‐based application package, and a user manual to guide installation and use. We also review underlying assumptions and challenges of population monitoring in this case study. We expect our software will contribute to efficient monitoring of woodland caribou and help in the assessment of recovery actions for this species. © 2019 The Authors. Wildlife Society Bulletin Published by Wiley Periodicals, Inc.

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“…This precision is necessary for the application of the bootstrap resampling analysis method to replicate an instantaneous time-lapse design. Reference density estimates on these sites were obtained either through the application of different statistical methods to the same camera datasets: camera-trap distance sampling (CT-DS) in Ivory Coast (Howe et al, 2017) and Spain (Palencia et al, 2021), random encounter model (REM) in Malaysia (Wearn et al, 2022) and Spain (Palencia et al, 2021); or using independent datasets and methods: line transect distance sampling (LT-DS) in Kenya (Ford et al, 2015), the Formozov-Malyushev-Pereleshin (FMP) snow tracking in Russia (Waller, 2022), and integrated population modeling (IPM) in Canada (Hatter et al, 2017).…”

Section: Datasetsmentioning

confidence: 99%

Estimating animal density using the Space‐to‐Event model and bootstrap resampling with motion‐triggered camera‐trap data

Lyet

Waller

Chambert

et al. 2023

Remote Sens Ecol Conserv

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Over the past few decades, the use of camera‐traps has revolutionized our ability to monitor populations of wild terrestrial mammals. While methods to estimate abundance from individually‐identifiable animals are well‐established, they are mostly restricted to species with clear natural markings or else necessitate invasive and often costly animal tagging campaigns. Estimating abundance or density from unmarked animals remains challenging. Several models recently developed to deal with this issue are promising, but are not widely used by field ecologists. Here, we developed a framework for applying the Space‐To‐Event (STE) model—originally designed to be used with time‐lapse images—on motion‐triggered camera‐trap data. Our approach involves performing bootstrap resampling on the photographic dataset to generate multiple datasets that are then used as input to the STE model. We tested our approach on 29 datasets, including 17 ungulate species from eight sites, in six different countries and various ecosystems. Then, we conducted a regression analysis to evaluate how variations in ecological and sampling conditions across studies affected the bias and precision of our STE density estimates. Our study shows that with a bootstrap resampling approach and information on animal activity and effective detection distances to animals, the STE model can be used to analyze motion‐trigger datasets and provide population density estimates that are similar to those from other methods. We found that measuring the camera viewshed was critical to prevent major negative biases in density estimates. Moreover, using a 1‐s sampling window was important to avoid the positive bias that results from violating the instantaneous‐sampling assumption. We found that precision increased with greater sampling effort and higher density populations. Based on these results, we highlight several issues from past studies that have applied the original timelapse‐based STE to motion‐trigger datasets, issues that our bootstrap resampling approach addresses. We caution that the STE model, whether applied to timelapse or motion‐triggered datasets, relies on strict assumptions. Any violations of these assumptions, such as non‐instantaneous sampling or the application of angle and distance of detection provided by the camera manufacturer, can cause biases in multiple directions that may be difficult to differentiate.

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An integrated modeling approach for assessing management objectives for mule deer in central British Columbia

Cited by 5 publications

References 27 publications

Consequences of violating assumptions of integrated population models on parameter estimates

Consequences of violating assumptions of integrated population models on parameter estimates

Web‐based application for threatened woodland caribou population modeling

Estimating animal density using the Space‐to‐Event model and bootstrap resampling with motion‐triggered camera‐trap data

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