WILDPOP: AN INTERACTIVE TOOL FOR ESTIMATING OCCUPANCY AND ABUNDANCE OF WILDLIFE POPULATIONS

Context Determining the most efficient detection method for a target species is key for successful wildlife monitoring and management. Driven transects and sign surveys are commonly used to monitor populations of the endangered numbat (Myrmecobius fasciatus). Camera trapping is being explored as a new method. These methods were unevaluated for efficacy and cost for numbat detection. Aims To compare efficacy and costing of driven transects, sign surveys and camera trapping for detecting numbats in the Upper Warren region, Western Australia. Methods Seven repeat sign surveys and driven transects, as well as 4 months of camera trapping, were conducted concurrently at 50 sites along three transects. Numbat detection rates and costing of the three techniques were compared, and detection probabilities were compared between sign surveys and camera trapping. Key results Numbat signs were detected during 88 surveys at 39 sites, exceeding camera trapping (26 detections at 13 sites) and driven transects (seven detections near five sites). The estimated probability for detecting a numbat or a sign thereof (at a site where numbats were present) ranged from 0.21 to 0.35 for a sign survey, and 0.02 to 0.06 for 7 days of camera trapping. Total survey costs were lowest for driven transects, followed by camera trapping and sign surveys. When expressed as cost per numbat detection, sign surveys were cheapest. Conclusions Comparative studies of survey methods are essential for optimal, cost-effective wildlife monitoring. Sign surveys were more successful and cost effective than camera trapping or driven transects for detecting numbats in the Upper Warren region. Together with occupancy modelling, sign surveys are appropriate to investigate changes in occupancy rates over time, which could serve as a metric for long-term numbat monitoring. Implications There is no ‘best’ method for wildlife surveys. Case-specific comparison of animal detection methods is recommended to ensure optimal methods. For the numbat population in the Upper Warren region, further studies are needed to improve numbat detection rates from camera trapping, and to test sign surveys in autumn (March to May), when surviving juvenile numbats have established their own territory and assumptions regarding population closure are less likely to be violated.

A fifth of reptiles are Data Deficient; many due to unknown population status. Monitoring snake populations can be demanding due to crypsis and low population densities, with insufficient recaptures for abundance estimation via Capture-Mark-Recapture. Alternatively, binomial N-mixture models enable abundance estimation from count data without individual identification, but have rarely been successfully applied to snake populations. We evaluated the suitability of occupancy and N-mixture methods for monitoring an insular population of grass snakes (Natrix helvetica) and considered covariates influencing detection, occupancy and abundance within remaining habitat. Snakes were elusive, with detectability increasing with survey effort (mean: 0.33 ± 0.06 s.e.m.). The probability of a transect being occupied was moderate (mean per kilometre: 0.44 ± 0.19 s.e.m.) and increased with transect length. Abundance estimates indicate a small threatened population associated to our transects (mean: 39, 95% CI: 20–169). Power analysis indicated that the survey effort required to detect occupancy declines would be prohibitive. Occupancy models fitted well, whereas N-mixture models showed poor fit, provided little extra information over occupancy models and were at greater risk of closure violation. Therefore we suggest occupancy models are more appropriate for monitoring snakes and other elusive species, but that population trends may go undetected.

The rapid decline in global biodiversity underscores the critical need for comprehensive monitoring of wildlife distribution and abundance. This study explores the trends in applied hierarchical modeling, which is an important tool in addressing these conservation challenges. By analyzing a dataset of 697 peer-reviewed articles published between 2002 and 2022, we examine the taxonomic focus, detection procedures, study designs, and modeling choices within the field of population ecology. Our findings revealed that most studies concentrated on single taxonomic groups, particularly mammals and birds. Data collection methods included visual surveys, acoustic surveys, camera traps, and traps, with some studies combining multiple techniques. Notably, the United States dominated the geographical focus, accounting for 46% of published papers. In terms of modeling approaches, single-season occupancy was the most prevalent, followed by various other models, including multi-species occupancy and N-mixture models. While hierarchical modeling has gained popularity, citations for these articles remained relatively modest, with only a few achieving over 100 citations. Authorship analysis revealed a highly collaborative network of researchers, with key authors contributing significantly to the field’s development and dissemination. Co-authorship and co-citation networks highlighted the importance of authors who can bridge differing scientific groups and those that have made substantial contributions to hierarchical modeling methods. Despite its growth, the field faces challenges related to standardization in modeling and reporting practices. While efforts to address these issues are currently underway, a cohesive framework for occupancy modeling in ecology is still in an emerging stage.

Wildlife managers often encounter stakeholder groups with differing beliefs about ideal population levels of wildlife and appropriate management actions toward wildlife. For example, hunters, farmers, foresters, and suburban homeowners often express different acceptance capacities for white‐tailed deer. Similarly, stakeholder groups often differ over managing Canada geese, black‐tailed prairie dogs, beaver, and other species. Understanding and responding to these different preferences is essential to the successful management of publicly owned wildlife. Researchers have examined beliefs about wildlife populations from perspectives including cultural carrying capacity, overabundance, risk perception, wildlife acceptance capacity, and normative beliefs. Each approach has contributed to our understanding of how beliefs about ideal wildlife population levels are based on a complex interaction among internal, psychological variables (values, beliefs); behavioral variables (occupation, past experience with wildlife); and situational specifics (wildlife species, abundance, management actions). A normative approach, based on social psychology's hierarchical model of human thought, can help explain and predict the determinants and consequences of stakeholder acceptance capacity. Research using the normative approach demonstrates how stakeholder acceptance capacity for wildlife populations and management actions can be influenced by psychological, behavioral, and situational variables. Additional investigation of stakeholder acceptance capacity and its determinants will allow for more confident generalization about stakeholder responses to different wildlife population levels and management actions, and will help identify conditions that are likely to generate intense conflict among stakeholder groups.

Accurate assessment of abundance forms a central challenge in population ecology and wildlife management. Many statistical techniques have been developed to estimate population sizes because populations change over time and space and to correct for the bias resulting from animals that are present in a study area but not observed. The mobility of individuals makes it difficult to design sampling procedures that account for movement into and out of areas with fixed jurisdictional boundaries. Aerial surveys are the gold standard used to obtain data of large mobile species in geographic regions with harsh terrain, but these surveys can be prohibitively expensive and dangerous. Estimating abundance with ground-based census methods have practical advantages, but it can be difficult to simultaneously account for temporary emigration and observer error to avoid biased results. Contemporary research in population ecology increasingly relies on telemetry observations of the states and locations of individuals to gain insight on vital rates, animal movements, and population abundance. Analytical models that use observations of movements to improve estimates of abundance have not been developed. Here we build upon existing multi-state mark-recapture methods using a hierarchical N-mixture model with multiple sources of data, including telemetry data on locations of individuals, to improve estimates of population sizes. We used a state-space approach to model animal movements to approximate the number of marked animals present within the study area at any observation period, thereby accounting for a frequently changing number of marked individuals. We illustrate the approach using data on a population of elk (Cervus elaphus nelsoni) in Northern Colorado, USA. We demonstrate substantial improvement compared to existing abundance estimation methods and corroborate our results from the ground based surveys with estimates from aerial surveys during the same seasons. We develop a hierarchical Bayesian N-mixture model using multiple sources of data on abundance, movement and survival to estimate the population size of a mobile species that uses remote conservation areas. The model improves accuracy of inference relative to previous methods for estimating abundance of open populations.

Science, statistics and surveys: a herpetological perspective

The ability to accurately estimate abundance is crucial to ecologists, conservationists, and managers to provide insight on species status, population trends, and viability. Acoustic detection and occupancy modeling can provide an understanding of resource use for bats, but these methods do not estimate how many bats are in an area, or how these numbers change over time. In North America, there is a heightened need to estimate bat abundance and trends in response to white-nose syndrome (WNS) and other threats to bat populations. We assessed the performance of the N-mixture model for repeated count data and the general multinomial-Poisson model for removal sampling to estimate bat abundance from simulated mist-net capture data. We evaluated performance under varying numbers of sites and visits, detection probabilities (P), and population sizes. We simulated four scenarios with a total of 85 combinations of parameter values each containing 1,000 replications. We used the UNMARKED package in R to fit the N-mixture and removal models. We calculated relative bias (RB), mean absolute error (MAE), and mean absolute percent error (MA%E) from model estimates to evaluate model performance. Relative bias, MAE, and MA%E decreased as p and bat abundance increased for all models. The removal model outperformed the N-mixture model in all scenarios except when P = 0.05. The N-mixture model had low RB, MAE, and MA%E when bat abundance was ≥ 70 and P > 0.5, but in other scenarios, errors were large. The mean of estimates from the removal model were unbiased and RB, MAE, and MA%E were very low for most scenarios. Use of the removal model with data from repeated mist-net surveys may allow resource managers and conservationists to better quantify how resource management and landscape composition affect bat species abundance and overall populations.

Citizen science data facilitate monitoring of rare large carnivores in remote montane landscapes

The estimation of abundance and distribution and factors governing patterns in these parameters is central to the field of ecology. The continued development of hierarchical models that best utilize available information to inform these processes is a key goal of quantitative ecologists. However, much remains to be learned about simultaneously modeling true abundance, presence, and trajectories of ecological communities.Simultaneous modeling of the population dynamics of multiple species provides an interesting mechanism to examine patterns in community processes and, as we emphasize herein, to improve species‐specific estimates by leveraging detection information among species. Here, we demonstrate a simple but effective approach to share information about observation parameters among species in hierarchical community abundance and occupancy models, where we use shared random effects among species to account for spatiotemporal heterogeneity in detection probability.We demonstrate the efficacy of our modeling approach using simulated abundance data, where we recover well our simulated parameters using N‐mixture models. Our approach substantially increases precision in estimates of abundance compared with models that do not share detection information among species. We then expand this model and apply it to repeated detection/non‐detection data collected on six species of tits (Paridae) breeding at 119 1 km2 sampling sites across a P. montanus hybrid zone in northern Switzerland (2004–2020). We find strong impacts of forest cover and elevation on population persistence and colonization in all species. We also demonstrate evidence for interspecific competition on population persistence and colonization probabilities, where the presence of marsh tits reduces population persistence and colonization probability of sympatric willow tits, potentially decreasing gene flow among willow tit subspecies.While conceptually simple, our results have important implications for the future modeling of population abundance, colonization, persistence, and trajectories in community frameworks. We suggest potential extensions of our modeling in this paper and discuss how leveraging data from multiple species can improve model performance and sharpen ecological inference.

Rapid change in wildlife populations can challenge managers to promote species conservation while maintaining public support for wildlife. Wolf management during recolonization in Wisconsin, United States demonstrates the complexities of inconsistent management authority, public attitudes, and illegal killing of wolves. State management authority to control depredating wolves oscillated during a period of intense sociopolitical conflict over wolf status under the federal Endangered Species Act. We demonstrate that swings in wolf status led to inconsistent management authority, declining local public support for wolves, and possibly the unintended backlash of more illegal kills and a legislatively mandated public wolf hunt. A new Wildlife Management Matrix illustrates an idealized relationship between lethal control options and perceptions of wildlife. Moderating the sociopolitical drivers of swings in policy over short periods is essential to allow wildlife managers greater flexibility in achieving species‐specific goals. To our knowledge, this research provides the first demonstrated link between illegal wildlife killing and management authority under the Endangered Species Act, and suggests that illegal behavior may be moderated with responsible and effective wildlife management programs. We recommend states avoid prescriptive harvest legislation, and we suggest a more incremental shift from federal to state management authority.

The debate about commercialization of deer and other wildlife by hunting or captive rearing is confused with the unrelated history of market hunting during the 1800s and undermined by false presumptions and fears about wildlife and public lands being taken over by private interests. Opponents underestimate the private role in landscape and wildlife management and the control that landowners have on wildlife populations and recreational hunting. Assertions are incorrect that harvested animals and sport hunting are not integral to the success of wildlife conservation. Fallacious arguments about hunting may have entered the commercialization issue into an antihunting debate. The issue should not be whether the public or private sectors should benefit from wildlife, but rather how providing wildlife values to landowners can increase their stewardship. I argue that it is right and proper that private individuals should be entitled to manage and benefit from wildlife and recreation on their properties. It is pragmatic for governments to enable landowners to do so in conjunction with rules established and enforced on behalf of all the people. Public wildlife ownership and management have been imposed upon the private sector even though land is controlled privately on which wildlife are maintained and where recreationists seek access. Private lands (which encompass 66% of the United States) will continually grow in value to sustain biotic diversity, wildlife populations, and to provide recreation. To deny landowners a role in the management of their own lands is a mistake. Pressures are mounting for private landowners to account for costs and benefits and to operate effectively. Wildlife must be treated as a benefit, because as a cost, wildlife will be treated negatively either directly or indirectly. Lands not economically competitive for wildlife enterprises will likely be put to agricultural, industrial, or residential uses that diminish natural environmental values.

The N-mixture model proposed by Royle in 2004 may be used to approximate the abundance and detection probability of animal species in a given region. In 2006, Royle and Dorazio discussed the advantages of using a Bayesian approach in modelling animal abundance and occurrence using a hierarchical N-mixture model. N-mixture models assume replication on sampling sites, an assumption that may be violated when the site is not closed to changes in abundance during the survey period or when nominal replicates are defined spatially. In this paper, we studied the robustness of a Bayesian approach to fitting the N-mixture model for pseudo-replicated count data. Our simulation results showed that the Bayesian estimates for abundance and detection probability are slightly biased when the actual detection probability is small and are sensitive to the presence of extra variability within local sites.

In this study we propose an extension of the N-mixture family of models that targets an improvement of the statistical properties of rare species abundance estimators when sample sizes are low, yet typical for tropical studies. The proposed method harnesses information from other species in an ecological community to correct each species' estimator. We provide guidance to determine the sample size required to estimate accurately the abundance of rare tropical species when attempting to estimate the abundance of single species.We evaluate the proposed methods using an assumption of 50 m radius plots and perform simulations comprising a broad range of sample sizes, true abundances and detectability values and a complex data generating process. The extension of the N-mixture model is achieved by assuming that the detection probabilities are drawn at random from a beta distribution in a multi-species fashion. This hierarchical model avoids having to specify a single detection probability parameter per species in the targeted community. Parameter estimation is done via Maximum Likelihood.We compared our multi-species approach with previously proposed multi-species N-mixture models, which we show are biased when the true densities of species in the community are less than seven individuals per 100 hectares. The beta N-mixture model proposed here outperforms the traditional Multi-species N-mixture model by allowing the estimation of organisms at lower densities and controlling the bias in the estimation.We illustrate how our methodology can be used to suggest sample sizes required to estimate the abundance of organisms, when these are either rare, common or abundant. When the interest is full communities, we show how the multi-species approaches, and in particular our beta model and estimation methodology, can be used as a practical solution to estimate organism densities from rapid inventory datasets. The statistical inferences done with our model via Maximum Likelihood can also be used to group species in a community according to their detectabilities.

Determining presence or absence of collared peccaries (Pecari tajacu) from surveys of sign (tracks and feces) requires information on whether animals in sample units are detected. We estimated detection probabilities of collared peccary from sign surveys using occupancy models. Because it was unlikely that residence status of collared peccary in sampling units remained the same over a survey season, which is a primary assumption of occupancy models, we first determined the time interval for which to pool data. We then examined the influence of rainfall and peccary abundance on detection probabilities. We placed 90 sign stations (25‐m‐diam circular plots) throughout Chaparral Wildlife Management Area, south Texas, USA. We surveyed plots weekly for the presence or non‐presence of collared peccary during 2 11‐week sampling seasons in spring and fall 2003. We examined sign data weekly and we pooled the data in intervals from 2 weeks to 5 weeks. Estimates of detection probabilities increased from 1 week to 3 weeks of pooled data and leveled off thereafter. We needed a 3‐week time interval to meet the assumption of unchanging residence status. Using sign data pooled in 3‐week increments, detection probabilities were influenced by areas that differed in peccary abundance, but they were not influenced by rainfall. Estimates of detection probabilities ranged from 0.43 to 0.77 for 3‐week time intervals. Sign surveys and occupancy modeling of data can be used to measure spatial patterns of collared peccary in south Texas as long as multiple 3‐week time intervals are sampled.

Integrating complex geospatial data into research and applications for wildlife ecology remains a challenge. For example, animations of wildlife tracking data can be useful for developing hypotheses, communicating with stakeholders and infrastructure planning. Conveying an effective message often requires visualizing movements in relation to custom background layers, such as dynamic weather conditions or local transportation features. However, animations are commonly made using software that is easy to use but offers few options for input layers, thus limiting their impact. Alternatively, bespoke solutions require advanced programming skills that are not readily available for many ecologists. We developed ECODATA, a suite of open‐source tools to support exploration, analysis and visualization of animal movements and dynamic geospatial data layers. The tools do not require programming skills and guide users through the process of manipulating vector, raster and tabular data files to prepare inputs to custom animations or further analyses. The software was developed by a team of remote sensing experts, quantitative ecologists, wildlife managers and conservation practitioners. We demonstrate the use of ECODATA through two examples. The first describes the use of the software to animate movements of elk (Cervus elaphus) and wolves (Canis lupus) in relation to roads, wildlife crossing structures and seasonal vegetation green‐up near Banff National Park in Canada. The second illustrates the impact of the software on wildlife management, with an animation of caribou (Rangifer tarandus) movements and parturitions during the calving season. Both examples include processed remote sensing data and feature layers that provide relevant local context. ECODATA offers a novel resource to explore and communicate animals' interactions with their environment, informing management decisions and conservation strategies. The flexible tools for geospatial data manipulation can be used for data visualization, as described here, or to create predictor variables for inclusion in habitat selection or other ecological models.