Integrating harvest and camera trap data in species distribution models

Gilbert, Neil A.; Pease, Brent S.; Anhalt‐Depies, Christine; Clare, John; Stenglein, Jennifer L.; Townsend, Philip A.; Deelen, Timothy R. Van; Zuckerberg, Benjamin

doi:10.1016/j.biocon.2021.109147

Cited by 12 publications

(13 citation statements)

References 33 publications

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“…Wisconsin; Gilbert et al 2021b longevity has improved and battery life has been extended to many months, increasing this tool's utility for wildlife monitoring.…”

Section: Discussionmentioning

confidence: 99%

Monitoring mesocarnivores with tracks and technology using multi‐method modeling

et al. 2023

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Mesocarnivores play important ecological roles and are valued by diverse stakeholders. These species are often the focus of conservation efforts or are managed for sustainable harvest. Management actions require accurate population monitoring, but such monitoring is challenging because mesocarnivores are elusive and persist at low densities. We addressed this challenge by evaluating 2 monitoring methods (scent stations and motion-sensitive cameras) using multi-method modeling.We estimated occurrence probabilities and habitat relationships for 8 mesocarnivore species by fitting occupancy models to data collected at 75 sites from October to December 2021 across a 3,200-km 2 system in New Hampshire, USA. We assessed the relative estimated precision of the methodological approaches and their costs. We also evaluated tradeoffs in occurrence estimation and uncertainty among study designs by analyzing simulations run across various numbers of study sites and 2 study durations. Cameras cost roughly 10 times more than scent stations but strongly outperformed them in terms of species' detectability and parameter estimate precision. Multi-method models yielded the most precise estimates of occurrence probability and habitat relationships. Parameter estimates were on average twice as precise for camera and multi-method models compared to scent stations. Additionally, the estimated precision and direction (positive or negative) of habitat relationships varied with the method employed. Longer camera deployments, additional study sites, and multi-method approaches nearly always reduced uncertainty, but these

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“…Wisconsin; Gilbert et al 2021b longevity has improved and battery life has been extended to many months, increasing this tool's utility for wildlife monitoring.…”

Section: Discussionmentioning

confidence: 99%

Monitoring mesocarnivores with tracks and technology using multi‐method modeling

et al. 2023

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“…We must calibrate the parameters (capture efficiency in our study) by absolute abundance estimated from the REST model in such cases. Recent developments in combining multiple data sources, including camera data (Gilbert et al, 2021; Zipkin & Saunders, 2018) for species distribution modeling, have improved estimates of species distributions and population sizes (Bowler et al, 2019; Chandler & Clark, 2014; Fletcher Jr. et al, 2019; Miller et al, 2019). The multiple data in these models compensate for the estimated information of each dataset.…”

Section: Discussionmentioning

confidence: 99%

State‐space model combining local camera data and regional administration data reveals population dynamics of wild boar

et al. 2022

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Recent increases in wildlife cause negative impacts on humans through both economic and ecological damage, as well as the spread of pathogens. Understanding the population dynamics of wildlife is crucial to develop effective management strategies. However, it is difficult to estimate accurate and precise population size over large spatial and temporal scales because of the limited data availability. We addressed these issues by first fitting a random encounter and staying time (REST) model based on camera trap data to construct an informative prior distribution for a capture rate parameter in a harvest‐based Bayesian state‐space model. We constructed a Bayesian state‐space model that integrated administration data on the number of captured wild boar with the prior distribution of capture efficiency estimated by camera trap data. The model with informative prior distribution from the REST model successfully estimated population dynamics, whereas the model using only the administration data did not, owing to a lack of parameter convergence. We identified areas where (1) wild boars exhibit a high potential population growth rate and a high carrying capacity, (2) current trapping efforts are effectively suppressing local populations, and (3) trapping reinforcement is required to control populations in the whole region. The model could be used to predict future trends in populations under the assumptions of ongoing trapping pressure. This will help identify spatially explicit trapping efforts to achieve target population levels.

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“…Genetic material has been widely used to assess population genetic health (Brook et al, 2002), but is increasingly applied to address topics ranging from individual‐based identification to population‐level demography (e.g., Ausband et al, 2015; Grauer et al, 2019; Kendall et al, 2016). Contemporary quantitative approaches that allow incorporation of data from different sources can further strengthen inferences and optimize conservation impacts of research (Fidino et al, 2022; Gilbert et al, 2021; Pearson et al, 2006; Stabach et al, 2017). For instance, collaborative and interdisciplinary efforts to implement non‐invasive genetic techniques with other approaches (e.g., camera traps) have greatly improved understanding of American marten ( M. americana ) abundance, distribution, and population viability in the Great Lakes region, offering a precedent of success that could be replicated elsewhere (Day et al, 2022; Manlick et al, 2017; Smith et al, 2021).…”

Section: Making Progress When “Best Available Science” Is Not Very Goodmentioning

confidence: 99%

“…Genetic material has been widely used to assess population genetic health (Brook et al, 2002), but is increasingly applied to address topics ranging from individual-based identification to population-level demography (e.g., Ausband et al, 2015;Grauer et al, 2019;Kendall et al, 2016). Contemporary quantitative approaches that allow incorporation of data from different sources can further strengthen inferences and optimize conservation impacts of research (Fidino et al, 2022;Gilbert et al, 2021;Pearson et al, 2006;Stabach et al, 2017). For instance, collaborative and interdisciplinary efforts to…”

Section: Effective Application Of Techniques and Technologymentioning

confidence: 99%

Conservation of rare and cryptic species: Challenges of uncertainty and opportunities for progress

Martin

Delheimer

Moriarty

et al. 2022

Conservat Sci and Prac

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Effective conservation of at‐risk species presents a conundrum, often requiring rapid status assessments and timely actions regardless of the adequacy of best available information. Here, we present a case study on Humboldt martens (Martes caurina humboldtensis), a rare and cryptic carnivore listed as threatened in 2020 under the United States Endangered Species Act. Given their rarity, many aspects of Humboldt marten population ecology remain understudied. To help inform marten conservation, we conducted exploratory analyses to estimate population growth by incorporating empirically derived data into two demographic modeling approaches. Population growth rates from each approach exhibited substantial variability and were uninformative to evaluating population status. Our results highlight the inherent difficulties of studying cryptic animals and exemplify the issues of using sparse or uncertain data in potentially consequential circumstances. Considering the shortcomings of our findings, we provide a framework of reliable actions to improve future conservation outcomes for poorly‐understood species.

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Integrating harvest and camera trap data in species distribution models

Cited by 12 publications

References 33 publications

Monitoring mesocarnivores with tracks and technology using multi‐method modeling

Monitoring mesocarnivores with tracks and technology using multi‐method modeling

State‐space model combining local camera data and regional administration data reveals population dynamics of wild boar

Conservation of rare and cryptic species: Challenges of uncertainty and opportunities for progress

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