An evaluation of spatial capture‐recapture models applied to ungulate non‐invasive genetic sampling data

Journal of Wildlife Management, Год журнала: 2023, Номер 87(3)

Опубликована: Фев. 8, 2023

Abstract Non‐invasive genetic sampling (NGS) methods are becoming a mainstay in wildlife monitoring and can be used with spatial capture‐recapture (SCR) to estimate population density. Yet SCR based on NGS remains relatively underused for ungulate monitoring, despite the importance of robust density estimates this ecologically economically important group species. This may part attributed biological characteristics species data collection that lead violations model assumptions. We conducted simulation study evaluate robustness spatially heterogeneous (i.e., configuration individuals into groups variable sizes composition), individual heterogeneity space‐use patterns, adaptive variation detectability across space correlates density). evaluated each violation separately combination. parameterized our simulations published information preliminary analyses sets 3 species: chamois ( Rupicapra rupicapra ), red deer Cervus elaphus wild boar Sus scrofa ). While were grouping sampling, abundance could negatively biased (up 10% simulations) presence unaccounted use. The degree which underestimated depended mostly amount use among age classes. bias was also accompanied by reduction precision coverage probability estimators. discuss implications these findings, possible approaches identify problematic available (goodness‐of‐fit tests), potential further developments models ensure reliable populations from data.

Язык: Английский

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Occupancy and N-mixture modeling applications in ecology: A bibliometric analysis

Global Ecology and Conservation, Год журнала: 2024, Номер 50, С. e02838 - e02838

Опубликована: Фев. 7, 2024

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

Язык: Английский

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The importance of independence in unmarked spatial capture–recapture analysis

Wildlife Biology, Год журнала: 2024, Номер 2024(3)

Опубликована: Март 13, 2024

Wildlife populations can be unmarked, meaning individuals lack distinguishing features for individual identification. Populations may also exhibit non‐independent movements, move together. For of either unmarked or individuals, models based on spatial capture–recapture (SCR) approaches used to estimate abundance, density, and other parameters critical monitoring, management, conservation. However, when are both non‐independent, few model options available. One approach has been apply not address the non‐independence despite unquantified impacts bias, precision, ability make robust ecological inferences. We conducted a simulation study quantify impact performance count (SC) partial identity (SPIM) – two SCR‐based modeling fully marked independent SCR as reference. varied levels (aggregation cohesion), detection probability, number covariates resolve identities in SPIM estimation. expected abundance estimates increasingly biased precise aggregation cohesion increased. Results showed that indeed became less increasing non‐independence, but importantly suggested only could reliably applied under low sufficient SC yielded consistently with poor precision. was across combinations cohesion, expected. therefore advise against use estimating population known caution narrow conditions, encourage continued investigations into sampling design methods development individuals.

Язык: Английский

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Environmental impact assessment for large carnivores: a methodological review of the wolf Canis lupus monitoring in Portugal

Wildlife Biology, Год журнала: 2024, Номер 2024(6)

Опубликована: Июнь 18, 2024

The continuous growth of the global human population results in increased use and change landscapes, with infrastructures like transportation or energy facilities being a particular risk to large carnivores. Environmental impact assessments were established identify probable environmental consequences any new proposed project, find ways reduce impacts, provide evidence inform decision making mitigation. Portugal has wolf approximately 300 individuals, designated as an endangered species full legal protection. They occupy northern mountainous areas country which also been focus over last 20 years. Consequently, dozens monitoring programs have evaluate status, appropriate mitigation compensation measures. We reviewed Portuguese answer four key questions. Do examine adequate biological parameters meet objectives? Is study design suitable for measuring impacts? Are data collection methods effort sufficient stated inference statistical analyses lead robust conclusions? Overall, we found mismatch between aims reported, often neither aligns existing national guidelines. Despite vast expended diversity used, analysis makes almost exclusive relative indices summary statistics, little consideration potential biases that arise through (imperfect) observational process. This comparisons impacts across space time difficult is therefore unlikely contribute general understanding responses infrastructure‐related disturbance. recommend development standardized protocols advocate account imperfect detection guarantee accuracy, reproducibility, efficacy programs.

Язык: Английский

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Similarity learning networks uniquely identify individuals of four marine and terrestrial species

Ecosphere, Год журнала: 2024, Номер 15(10)

Опубликована: Окт. 1, 2024

Abstract Estimating the size of animal populations plays an important role in evidence‐based conservation and management. Some methods for estimating population rely on animals being individually identifiable. Traditionally, this has been done by marking physically captured animals, but increasingly, with distinctive natural markings are surveyed noninvasively using cameras. Animal reidentification from photographs is usually manually, which expensive, laborious, requires considerable skill. An alternative to develop computer vision that can support or replace manual identification task. We developed automated approach deep learning identify whether a pair same individual not. The core similarity network uses paired convolutional neural networks triplet loss function summarize image pairs decide they individual. Prior main matching step, two additional perform segmentation, cropping object within image, orientation prediction, deciding side was photographed. applied four species, images often spanning several years: systematic surveys bottlenose dolphins ( Tursiops truncatus , 2008–2019) harbor seals Phoca vitulina 2015–2019), citizen science dataset western leopard toads Sclerophrys pantherina unknown dates), publicly available repository humpback whale Megaptera novaeangliae dates). For these our best‐performing models were able different individuals 95.8%, 94.6%, 88.2%, 83.8% cases, respectively. found functions outperformed binary cross‐entropy data augmentation curation training provided small consistent improvements performance. These results demonstrate potential or, more likely, facilitate efforts.

Язык: Английский

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