Spatial variation in the response of tiger gene flow to landscape features and limiting factors

Reddy, P. Anuradha; Puyravaud, Jean Philippe; Cushman, Samuel A.; Segu, Harika

doi:10.1111/acv.12488

Cited by 19 publications

(10 citation statements)

References 28 publications

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“…However, many areas that are potentially of the highest inherent ecological stability for wildlife are also facing the greatest impact by humans. These rough landscapes likely offer refugia from human disturbance for large mobile species such as tigers (Krishnamurthy et al 2016;Reddy et al 2019) as well as smaller meso-carnivores like yellow-throated marten (Vergara et al 2016). Approximately 50% of the modelled species were negatively associated with agriculture, suggesting that a landscape perforated or fragmented by agriculture, in general, is less suitable for the occurrence of the wildlife species (Table 2).…”

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confidence: 99%

Prioritizing areas for conservation outside the existing protected area network in Bhutan: the use of multi-species, multi-scale habitat suitability models

et al. 2021

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Context Understanding the environmental and anthropogenic factors influencing habitat selection of multiple species is a foundation for quantifying human impacts on biodiversity and developing effective conservation measures. Objectives To determine the effect of multiple scales of environmental/topographic and anthropogenic variables and landscape patterns on habitat suitability of terrestrial mammals in Bhutan, assess the effectiveness of the current protected area network, identify areas of high species richness outside of the existing protected area, and evaluate the potential effectiveness of indicator and umbrella species for conservation planning. Methods We modelled multi-scale habitat selection of sixteen species of terrestrial mammals across Bhutan using data from a nation-wide camera trap survey. We used the predicted species distribution maps to assess the multi-species conservation effectiveness of the existing protected area network. We performed simulations to identify high priority areas for multiple species based on their habitat suitability, proximity to existing protected areas and overall connectivity within the predicted distribution of species. We used correlation analysis among predicted occurrence maps and multivariate cluster analysis to identify potential indicator species. We evaluated the potential utility of each species as umbrella species by assessing how well optimal protected areas for that species would protect suitable habitat for all 16 species simultaneously. Results Protected areas and forest cover were strongly associated with habitat use of most modelled species. Additionally, topographical features, like terrain roughness and slope position, contributed to habitat selection of multiple species, but often in different ways. Environmental and topographical variables were mostly selected at medium to broad scales. Anthropogenic variables (agriculture and built-up areas) were negatively associated with habitat suitability of most species at both fine and broad scales. Conservation effectiveness assessment of existing protected areas found protected areas in south-central Bhutan have high effectiveness in terms of both mean and total richness protected. Similarly, biological corridors in the south-central region offered high mean richness protection. Our simulation of optimal areas for additional protection found areas abutting protected areas in southern Bhutan offered high relative species richness protection. Our umbrella species analysis found muntjac, wild pig, serow, sambar and Asian golden cat are the most effective umbrella species for broader biodiversity protection. Our indicator species analysis found tiger, gaur, dhole, clouded leopard, Asian black bear and common leopard as effective indicator species. Conclusions This study highlights the need to protect optimally located species-rich areas outside the current protected areas. This kind of multi-species habitat assessment provides important information to optimize future conservation and development plans at national and regional scales.

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Prioritizing areas for conservation outside the existing protected area network in Bhutan: the use of multi-species, multi-scale habitat suitability models

et al. 2021

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“…We studied 599 tigers across the tiger range at the Taqpep c.1360C > T variant site. We genotyped 85 tigers using whole-genome data for four subspecies [Amur, Malayan, and Sumatran: 29 (44), and ongoing studies in Central India, North, and northeast India]. Of these, 309 individuals (58.5%) were successfully genotyped and were homozygous for the wild-type allele.…”

Section: Resultsmentioning

confidence: 99%

“…We genotyped 85 tigers using whole-genome data for four subspecies [Amur, Malayan, and Sumatran: 29, Armstrong et al ( 40 ), and Bengal tigers: 56, Khan et al ( 5 ), Khan et al ( 41 ), Armstrong et al ( 40 ), and this study] and recorded all of them as homozygous for the wild-type allele. We also attempted to PCR amplify the Taqpep c.1360C > T mutation locus ( Methods ) from 528 genetically identified individuals from primarily noninvasive sources [samples from Natesh ( 42 ), Reddy et al ( 43 ), Reddy et al ( 44 ), and ongoing studies in Central India, North, and northeast India]. Of these, 309 individuals (58.5%) were successfully genotyped and were homozygous for the wild-type allele.…”

Section: Resultsmentioning

confidence: 99%

“…To survey different populations across the tiger range for the presence/absence of Taqpep p.H454Y and to increase our sample size for population genetics analyses, we used samples collected for other studies. The samples used in this study are from Reddy et al ( 43 ), Reddy et al ( 44 ), Natesh et al ( 42 ), and ongoing studies in Central India, North, and northeast India. Additionally, we also used whole-genome sequence datasets from Armstrong et al ( 40 ), Khan et al ( 41 ), and Khan et al ( 5 ) for our Admixture ( 96 ) analysis, to increase the sample size for our 81-SNP dataset (by subsetting data from whole-genome sequences) used for population genetics analysis, and to increase the sample size for Taqpep c.1360C > T genotyping.…”

Section: Methodsmentioning

confidence: 99%

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High frequency of an otherwise rare phenotype in a small and isolated tiger population

Sagar

Kaelin

Natesh

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

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Most endangered species exist today in small populations, many of which are isolated. Evolution in such populations is largely governed by genetic drift. Empirical evidence for drift affecting striking phenotypes based on substantial genetic data are rare. Approximately 37% of tigers (Panthera tigris) in the Similipal Tiger Reserve (in eastern India) are pseudomelanistic, characterized by wide, merged stripes. Camera trap data across the tiger range revealed the presence of pseudomelanistic tigers only in Similipal. We investigated the genetic basis for pseudomelanism and examined the role of drift in driving this phenotype's frequency. Whole-genome data and pedigree-based association analyses from captive tigers revealed that pseudomelanism cosegregates with a conserved and functionally important coding alteration in Transmembrane Aminopeptidase Q (Taqpep), a gene responsible for similar traits in other felid species. Noninvasive sampling of tigers revealed a high frequency of the Taqpep p.H454Y mutation in Similipal (12 individuals, allele frequency = 0.58) and absence from all other tiger populations (395 individuals). Population genetic analyses confirmed few (minimal number) tigers in Similipal, and its genetic isolation, with poor geneflow. Pairwise FST (0.33) at the mutation site was high but not an outlier. Similipal tigers had low diversity at 81 single nucleotide polymorphisms (mean heterozygosity = 0.28, SD = 0.27). Simulations were consistent with founding events and drift as possible drivers for the observed stark difference of allele frequency. Our results highlight the role of stochastic processes in the evolution of rare phenotypes. We highlight an unusual evolutionary trajectory in a small and isolated population of an endangered species.

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“…Their results highlight the importance for landscape models to be developed specific to the region of interest as a precursor to conservation management decisions, and for the effects of spatial nonstationarity to be carefully considered and evaluated in any application of connectivity modelling. In a study through both central and western India, Reddy et al (2019) found tiger movements to be primarily related to topographic roughness and secondarily to diffuse disturbance. However, the relationship between the tiger and these two limiting factors was inverted between the west and central India, suggesting that a resistance surface built from a spatially constant functional relationship would at best be appropriate for only one of these two regions.…”

Section: Spatial Variabilitymentioning

confidence: 99%

Moving beyond landscape resistance: considerations for the future of connectivity modelling and conservation science

et al. 2022

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Landscape connectivity, the extent to which a landscape facilitates the flow of ecological processes such as organism movement, has emerged as a central focus of landscape ecology and conservation science. Connectivity modelling now encompasses an enormous body of work across ecological theory and application. The dominant connectivity models in use today are based on the framework of ‘landscape resistance’, which is a way of measuring how landscape structure influences movement patterns. However, the simplistic assumptions and high degree of reductionism inherent to the landscape resistance paradigm severely limits the ability of connectivity algorithms to account for many fundamental aspects of animal movement, and thus greatly reduces the effectiveness and relevance of connectivity models for conservation theory and practice. In this paper, we first provide an overview of the development of connectivity modelling and resistance surfaces. We then discuss several key drivers of animal movement which are absent in resistance-based models, with a focus on spatiotemporal variation, human and interspecies interactions, and other context-dependent effects. We look at a range of empirical studies which highlight the strong impact these effects have on movement and connectivity predictions. But we also provide promising avenues of future research to address this: we discuss newly emerging technologies and interdisciplinary work, and look to developing methodologies, models and conversations which move beyond the limiting framework of landscape resistance, so that connectivity models can better reflect the complexities and richness of animal movement.

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Spatial variation in the response of tiger gene flow to landscape features and limiting factors

Cited by 19 publications

References 28 publications

Prioritizing areas for conservation outside the existing protected area network in Bhutan: the use of multi-species, multi-scale habitat suitability models

Prioritizing areas for conservation outside the existing protected area network in Bhutan: the use of multi-species, multi-scale habitat suitability models

High frequency of an otherwise rare phenotype in a small and isolated tiger population

Moving beyond landscape resistance: considerations for the future of connectivity modelling and conservation science

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