Role of Monitoring in Global Tiger Conservation

Karanth, Krithi K.; Nichols, James D.; Goodrich, John M.; Reddy, G. Viswanatha; Mathur, Vinod B.; Wibisono, Hariyo T.; Sunarto, Sunarto; Pattanavibool, Anak; Gumal, Melvin

doi:10.1007/978-981-10-5436-5_1

Cited by 8 publications

(5 citation statements)

References 27 publications

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“…We developed two predictive models on tiger distribution: a rule‐based model (hereafter called the expert model ) based on expert knowledge of tiger ecology (Johnsingh, 2004; Karanth, 2001; Schaller, 1967; Seidensticker, 1996) without utilizing tiger occurrence points or its current distribution; and an observation‐based model (hereafter called the observation model ) using correlations between observed tiger occurrence and environmental variables. The expert model was based on an Analytic Hierarchy Process (AHP) framework (Saaty, 1987) which assigned weights derived by a square matrix with values inserted from expert judgment, to spatial layers in ArcGIS.…”

Section: Methodsmentioning

confidence: 99%

Assessing the adequacy of a protected area network in conserving a wide‐ranging apex predator: The case for tiger (Panthera tigris) conservation in Bhutan

Thinley

Rajaratnam

Morreale

et al. 2020

Conservat Sci and Prac

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Protected area networks (PAN) are essential for conserving wide‐ranging apex predators but their adequacy in species protection has rarely been assessed. Here, we assess the adequacy of Bhutan's PAN in conserving and providing connectivity to the endangered tiger (Panthera tigris). We determine the current extent of tiger habitat, predict new suitable habitat, identify potential corridors, and empirically estimate the range of tiger numbers that Bhutan can spatially support. We use two spatial models with different approaches to ascertain current tiger distribution and predict new suitable tiger areas: (a) an expert model based on tiger ecology and (b) an observation model from observed tiger distribution. The expert model identified more suitable tiger areas (32,887 km2) over the observation model (29,962 km2), with the PAN encompassing 46% and 45% of predicted suitable areas, respectively. Vast suitable tiger habitat remains unprotected. Based on our estimates of total suitable habitats, Bhutan can spatially support 138–151 tigers compared to the current estimate of 103, thereby precluding a doubling in tiger numbers. To ensure adequate protection of tigers in Bhutan, we recommend readjusting and/or expanding existing PAN boundaries, including the designation of new corridors, protecting habitats, and conserving prey populations.

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Section: Methodsmentioning

confidence: 99%

Assessing the adequacy of a protected area network in conserving a wide‐ranging apex predator: The case for tiger (Panthera tigris) conservation in Bhutan

Thinley

Rajaratnam

Morreale

et al. 2020

Conservat Sci and Prac

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“…It is crucial to acknowledge and address potential biases that may arise from the violation of critical assumptions. This is important to consider because many camera trapping studies selectively place cameras on landscape features (e.g., trail, road) to maximize detections of target species like carnivores (Karanth & Nichols, 2017; Tanwar et al., 2021), or simply to reduce data collection effort in the field (Cusack et al., 2015). Landscape features, such as trails, that affect local detection of animals, cannot be accounted for in models like the STE that use density estimates from the collective viewshed of cameras to extrapolate abundance across the study area (Gilbert et al., 2021).…”

Section: Discussionmentioning

confidence: 99%

“…Robust estimates of animal abundance from camera‐trap data are some of the most critical population parameters and allow field conservationists to make informed management and conservation decisions such as for hunting management (Acevedo et al., 2014) and large carnivore conservation (Jhala et al., 2021). For species in which at least some individuals can be reliably identified from photographs, spatial and non‐spatial capture‐recapture models are widely used to estimate density (Jimenez et al., 2019; Karanth & Nichols, 2017; O'Connell et al., 2011; Royle et al., 2014). Such models and their associated sampling methods are used across many scales, from site‐based (e.g., Gray & Prum, 2012) to national‐level monitoring programs, such as the India Tiger Census (Jhala et al., 2021).…”

Section: Introductionmentioning

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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“…With significant funding from the Government of India, much of the conservation focus in the country has been centred around this species (Jhala et al, 2021). Many global developments in research techniques have been driven by studies on the ecology of this species (Ullas Karanth & Nichols, 2017). What was missing was genetic data on the otherwise well-studied populations.…”

Section: Population Genetics Of Animals In the Wild To Aid Conservati...mentioning

confidence: 99%

Population genetics of animals in the wild to aid conservation: Uma Ramakrishnan—Recipient of the 2023 Molecular Ecology Prize

Robin

2024

Molecular Ecology

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Role of Monitoring in Global Tiger Conservation

Cited by 8 publications

References 27 publications

Assessing the adequacy of a protected area network in conserving a wide‐ranging apex predator: The case for tiger (Panthera tigris) conservation in Bhutan

Assessing the adequacy of a protected area network in conserving a wide‐ranging apex predator: The case for tiger (Panthera tigris) conservation in Bhutan

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

Population genetics of animals in the wild to aid conservation: Uma Ramakrishnan—Recipient of the 2023 Molecular Ecology Prize

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