Species‐specific differences in detection and occupancy probabilities help drive ability to detect trends in occupancy

Steenweg, Robin; Hebblewhite, Mark; Whittington, Jesse; McKelvey, Kevin S.

doi:10.1002/ecs2.2639

Cited by 17 publications

(24 citation statements)

References 31 publications

(87 reference statements)

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“…Conversely, estimates of occupancy had higher variability when w equaled 0.5. This was due to the variance properties of the binomial distribution where variance is highest at 0.5 and lowest near zero and one (Steenweg et al 2019). We, like other studies, found that Fig.…”

Section: Discussionsupporting

confidence: 77%

“…Thus, we had high power to detect changes in occupancy for most species in our analysis (Steenweg et al 2019). The mean time to first detection for individual species ranged from 0.9 min (95% BCI = 0.8 to 1.0, P cum = 1.0) for house wrens (Troglodytes aedon) to 7.2 min (95% BCI = 4.6 to 14.1, P cum = 0.75) for downy woodpeckers (Dryobates pubescens).…”

Section: Landbird Studymentioning

confidence: 99%

“…The mean time to first detection for individual species ranged from 0.9 min (95% BCI = 0.8 to 1.0, P cum = 1.0) for house wrens (Troglodytes aedon) to 7.2 min (95% BCI = 4.6 to 14.1, P cum = 0.75) for downy woodpeckers (Dryobates pubescens). Thus, we had high power to detect changes in occupancy for most species in our analysis (Steenweg et al 2019).…”

Section: Landbird Studymentioning

confidence: 99%

“…Long-term monitoring programs across large spatial scales are required to understand how the direct and indirect effects of human activity affect wildlife populations. Occupancy models are frequently used to monitor changes in distribution (K ery et al 2013), relative abundance (Steenweg et al 2019), and species diversity (Sutherland et al 2016, Tenan et al 2017. They are also used to understand how factors such as climate change (Mor an-Ord oñez et al 2017), inter-specific competition (Wisz et al 2013, Staniczenko et al 2017, and industrial development affect wildlife populations (Johnson et al 2015, Rich et al 2017.…”

Section: Introductionmentioning

confidence: 99%

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Landbird trends in protected areas using time‐to‐event occupancy models

et al. 2019

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Global populations of wildlife are affected by human activity, land cover change, and climate change. Long-term monitoring programs across large spatial scales are required to understand how these and other factors affect wildlife populations. Occupancy models are frequently used to monitor changes in species distribution while accounting for imperfect detection. Occupancy surveys can be expensive because they typically require multiple surveys to estimate the probability of detection. Time-to-detection models provide a promising approach for estimating occupancy because they require just one visit; however, few studies have tested or applied these models to wildlife data. We ran a simulation study to assess biases of time-to-event occupancy models for standardized avian point-count surveys and then applied the models to 10 yr of data. Time to first detection occupancy models had minimal bias and almost nominal coverage for species with a mean time to first detection <8 min on surveys with 10 min of sampling. Biases and root mean squared error increased with increasing time to first detection. We applied a single species, multiyear occupancy model to 34,665 detections of 77 landbird species collected across 500 km of latitude in five protected areas along the Rocky Mountains. Models from 64 species converged and had mean times to first detection <8 min. Average time to first detections was 3.2 min, which reflected a cumulative probability of detection of 0.96. Occupancy rates increased, decreased, and remained unchanged for 53%, 9%, and 38% of species, respectively. Overall, occupancy rates increased in 2015 and 2016 for short-and long-distance migrants and decreased slightly for winter residents. Average decadal temperature and precipitation were important predictors for almost half of the species, while annual changes in spring temperature and precipitation affected 23% of species. Our studies demonstrate that time to first event occupancy models provide an efficient method for monitoring changes in distribution so long as encounter rates are much shorter than the survey duration. Our stable to increasing trends and strong responses to spring temperature and precipitation highlight the value of long-term monitoring for understanding how changing climatic conditions affect wildlife.

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Section: Discussionsupporting

confidence: 77%

Section: Landbird Studymentioning

confidence: 99%

Section: Landbird Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Landbird trends in protected areas using time‐to‐event occupancy models

et al. 2019

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“…The burgeoning interest in making inferences about communities has led to analytical approaches that utilize data on all observed species, including bycatch species that are obtained incidentally during the course of a study that was designed with one or more focal species in mind (Iknayan et al 2014, Guillera‐Arroita 2017, Ovaskainen et al 2017, Steenweg et al 2019). While the design is optimized for the focal species, there is often information about other species readily or potentially available for extrapolation.…”

Section: Introductionmentioning

confidence: 99%

Multi‐species occupancy models: review, roadmap, and recommendations

2020

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Recent technological and methodological advances have revolutionized wildlife monitoring. Although most biodiversity monitoring initiatives are geared towards focal species of conservation concern, researchers are increasingly studying entire communities, specifically the spatiotemporal drivers of community size and structure and interactions among species. This has resulted in the emergence of multi‐species occupancy models (MSOMs) as a promising and efficient approach for the study of community ecology. Given the potential of MSOMs for conservation and management action, it is critical to know whether study design and model assumptions are consistent with inference objectives. This is especially true for studies that are designed for a focal species but can give insights about a community. Here, we review the recent literature on MSOMs, identify areas of improvement in the multi‐species study workflow, and provide a reference model for best practices for focal species and community monitoring study design. We reviewed 92 studies published between 2009 and early 2018, spanning 27 countries and a variety of taxa. There is a consistent under‐reporting of details that are central to determining the adequacy of designs for generating data that can be used to make inferences about community‐level patterns of occupancy, including the spatial and temporal extent, types of detectors used, covariates considered, and choice of field methods and statistical tools. This reporting bias could consequently result in skewed estimates, affecting conservation actions and management plans. On the other hand, comprehensive reporting is likely to help researchers working on MSOMs assess the robustness of inferences, in addition to making strides in terms of reproducibility and reusability of data. We use our literature review to inform a roadmap with best practices for MSOM studies, from simulations to design considerations and reporting, for the collection of new data as well as those involving existing datasets.

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When can model‐based estimates replace surveys of wildlife populations that span many discrete management units?

Priadka

Brown

Fedy

et al. 2022

Ecol Sol and Evidence

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Monitoring widely distributed species on a budget presents challenges for the spatio‐temporal allocation of survey effort. When there are multiple discrete units to monitor, survey alternatives such as model‐based estimates can be useful to fill information gaps but may not reliably reflect biological complexity and change. The spatio‐temporal allocation of survey effort that minimizes uncertainty for the greatest number of units within a budget can help to ensure monitoring is optimized. We used aerial survey‐based population estimates of moose (Alces alces) across 30 Wildlife Management Units (WMUs) in Ontario, Canada to parameterize simulated populations and test the performance of different monitoring scenarios in capturing WMU‐specific annual variation and trends. Firstly, we tested scenarios that prioritized conducting a survey for a unit based on one of three management criteria: population state, population uncertainty or number of years between surveys. Also incorporated in the decision framework were WMU‐specific costs and annual budget constraints. Secondly, we tested how using model‐based estimates to fill information gaps improved population and trend estimates. Lastly, we assessed how the utility (based on minimizing population uncertainty) of using a model‐based estimate rather than conducting a survey was impacted by population density, severity of environmental stressors and years since the last survey. Interval‐based monitoring that minimized the number of years between surveys captured accurate trends for the highest number of WMUs, but annual variation was poorly captured regardless of management criteria prioritized. Using model‐based estimates to fill information gaps improved trend estimation. Further, the utility of conducting a survey increased with time since the last survey and was greater for populations with low densities when the severity of environmental stressors was high, while being greater for populations with high densities when environmental severity was low. Overall, the utility of aerial survey monitoring was strongly associated with WMU‐specific monitoring precision and the predictive power of model‐based estimates. If long‐term trends are evident, then there is greater value in using alternatives such as model‐based predictions to replace surveys, but model‐based estimates may be a poor substitute when there is strong annual variation and when using a simple model.

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Species‐specific differences in detection and occupancy probabilities help drive ability to detect trends in occupancy

Cited by 17 publications

References 31 publications

Landbird trends in protected areas using time‐to‐event occupancy models

Landbird trends in protected areas using time‐to‐event occupancy models

Multi‐species occupancy models: review, roadmap, and recommendations

When can model‐based estimates replace surveys of wildlife populations that span many discrete management units?

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