tigers and leopards have experienced considerable declines in their population due to habitat loss and fragmentation across their historical ranges. Multi-scale habitat suitability models (HSM) can inform forest managers to aim their conservation efforts at increasing the suitable habitat for tigers by providing information regarding the scale-dependent habitat-species relationships. However the current gap of knowledge about ecological relationships driving species distribution reduces the applicability of traditional and classical statistical approaches such as generalized linear models (GLMs), or occupancy surveys to produce accurate predictive maps. This study investigates the multiscale habitat relationships of tigers and leopards and the impacts of future climate change on their distribution using a machine-learning algorithm random forest (Rf). the recent advancements in the machine-learning algorithms provide a powerful tool for building accurate predictive models of species distribution and their habitat relationships even when little ecological knowledge is available about the species. We collected species occurrence data using camera traps and indirect evidence of animal presences (scats) in the field over 2 years of rigorous sampling and used a machine-learning algorithm random forest (Rf) to predict the habitat suitability maps of tiger and leopard under current and future climatic scenarios. We developed niche overlap models based on the recently developed statistical approaches to assess the patterns of niche similarity between tigers and leopards. tiger and leopard utilized habitat resources at the broadest spatial scales (28,000 m). Our model predicted a 23% loss in the suitable habitat of tigers under the RCP 8.5 Scenario (2050). Our study of multi-scale habitat suitability modeling provides valuable information on the species habitat relationships in disturbed and human-dominated landscapes concerning two large felid species of conservation importance. these areas may act as refugee habitats for large carnivores in the future and thus should be the focus of conservation importance. this study may also provide a methodological framework for similar multi-scale and multi-species monitoring programs using robust and more accurate machine learning algorithms such as random forest. Tigers and leopards are two large carnivore species of conservation importance occurring in sympatry across much of their range in India. The nationwide tiger census conducted by Govt. of India after every 4 years has shown a gradual increase in the tiger population across many protected areas. However, a significant proportion of the tiger population still occurs in fragmented landscapes outside the conventional protected areas 1,2. Smallsized protected areas, increased habitat fragmentation, and high anthropogenic pressure on the remaining intact habitats increase the likelihood of tiger populations becoming more isolated and thereby restricting the potential dispersal opportunities 3. Tigers and leopards are wide-rangi...

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Background The habitat resources are structured across different spatial scales in the environment, and thus animals perceive and select habitat resources at different spatial scales. Failure to adopt the scale-dependent framework in species habitat relationships may lead to biased inferences. Multi-scale species distribution models (SDMs) can thus improve the predictive ability as compared to single-scale approaches. This study outlines the importance of multi-scale modeling in assessing the species habitat relationships and may provide a methodological framework using a robust algorithm to model and predict habitat suitability maps (HSMs) for similar multi-species and multi-scale studies. Results We used a supervised machine learning algorithm, random forest (RF), to assess the habitat relationships of Asiatic wildcat (Felis lybica ornata), jungle cat (Felis chaus), Indian fox (Vulpes bengalensis), and golden-jackal (Canis aureus) at ten spatial scales (500–5000 m) in human-dominated landscapes. We calculated out-of-bag (OOB) error rates of each predictor variable across ten scales to select the most influential spatial scale variables. The scale optimization (OOB rates) indicated that model performance was associated with variables at multiple spatial scales. The species occurrence tended to be related strongest to predictor variables at broader scales (5000 m). Multivariate RF models indicated landscape composition to be strong predictors of the Asiatic wildcat, jungle cat, and Indian fox occurrences. At the same time, topographic and climatic variables were the most important predictors determining the golden jackal distribution. Our models predicted range expansion in all four species under future climatic scenarios. Conclusions Our results highlight the importance of using multiscale distribution models when predicting the distribution and species habitat relationships. The wide adaptability of meso-carnivores allows them to persist in human-dominated regions and may even thrive in disturbed habitats. These meso-carnivores are among the few species that may benefit from climate change.

AimWe aim to investigate local perceptions of animal health challenges; current animal health knowledge; and methods to provide effective, relevant education to animal keepers in the Kanha Tiger Reserve area.Materials and methodsA farmer education programme was undertaken in the Kanha Tiger Reserve area. Local animal health priorities were investigated through participatory village meetings (n = 38), individual animal keeper questionnaires (n = 100) and a written survey of local paravets (n = 16). Educational interventions were: veterinary surgeon led education meeting (VE); paravet led education meeting (PVE); distribution of printed materials (PM). 230 village meetings were carried out across 181 villages, contacting 3791 animal keepers. 20 villages received printed materials. Information was gathered on perceptions of local animal health challenges and current remedies. Efficacy of knowledge transfer was assessed four to five months later using a purposeful sample of 38 villages.ResultsGroup meetings identified ticks (35/38), foot and mouth disease (FMD) (31/38) and diarrhoea (30/38) as the greatest animal health challenges. Individual interviews identified haemorrhagic septicaemia (HS) (87/100), blackquarter (BQ) (66/100) and plastic ingestion (31/100). Paravets identified FMD (7/16), BQ (6/16) and HS (6/16), and also indicated that animal husbandry and socio-economic factors were important. Current treatments were primarily home remedies and herbalism, but also included contacting a paravet, use of pharmaceuticals and faith healing. Animal treatment knowledge prior to intervention was not significantly different between groups (P = 0.868). Following intervention animal health knowledge was assessed: PVE performed better than controls (P = 0.001) and PM (P = 0.003); VE performed better than controls (P = 0.009). There was no significant difference between VE and PVE (P = 0.666) nor PM and controls (P = 0.060).Conclusions and recommendationsOpen access participatory village meetings are an effective way to provide animal health education. In this region distribution of posters and leaflets did not appear to be an effective way to contact animal keepers. Meetings led by paravets can be as effective as those led by veterinarians and paravets can rapidly and sustainably contact large numbers of animal keepers. Investigation of the local animal health situation is essential to ensure education is relevant and accessible to intended recipients. Interventions must be carefully planned to maximise engagement of all sections of the community, particularly women.

Background Habitat resources occur across the range of spatial scales in the environment. The environmental resources are characterized by upper and lower limits, which define organisms’ distribution in their communities. Animals respond to these resources at the optimal spatial scale. Therefore, multi-scale assessments are critical to identifying the correct spatial scale at which habitat resources are most influential in determining the species-habitat relationships. This study used a machine learning algorithm random forest (RF), to evaluate the scale-dependent habitat selection of sloth bears (Melursus ursinus) in and around Bandhavgarh Tiger Reserve, Madhya Pradesh, India. Results We used 155 spatially rarified occurrences out of 248 occurrence records of sloth bears obtained from camera trap captures (n = 36) and scats located (n = 212) in the field. We calculated focal statistics for 13 habitat variables across ten spatial scales surrounding each presence-absence record of sloth bears. Large (> 5000 m) and small (1000–2000 m) spatial scales were the most dominant scales at which sloth bears perceived the habitat features. Among the habitat covariates, farmlands and degraded forests were the essential patches associated with sloth bear occurrences, followed by sal and dry deciduous forests. The final habitat suitability model was highly accurate and had a very low out-of-bag (OOB) error rate. The high accuracy rate was also obtained using alternate validation matrices. Conclusions Human-dominated landscapes are characterized by expanding human populations, changing land-use patterns, and increasing habitat fragmentation. Farmland and degraded habitats constitute ~ 40% of the landform in the buffer zone of the reserve. One of the management implications may be identifying the highly suitable bear habitats in human-modified landscapes and integrating them with the existing conservation landscapes.