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The present communication indicates the timeline-based on accelerator mass spectrometry (AMS) dates - that has emerged from a sediment core originating in the Bandhavgarh National Park and Tiger Reserve (Madhya Pradesh, India) and attempts to see if those dates broadly coincide with the changing historical signature as recorded in archaeological evidence in this tract. A comprehensive overview of vegetational and climatic changes in the forested tract of Bandhavgarh across time is under preparation, and the present study aims to report on the first scientific dates from the Bandhavgarh National Park and Tiger Reserve and the rich possibilities that open up when they are juxtaposed with its archaeology. [ABSTRACT FROM AUTHOR]

This article arises out of a disquiet about the archaeology of historical India which has largely been concerned with cities and villages. Forests and wilderness rarely figure there, except in passing when the expanding agricultural terrain is described in relation to forest lands being domesticated or when there is an exploration of lines of communication, some of which pass through forested tracts. Because of this gap in engaging with lands beyond agrarian tracts and city sites, large expanses that carry markers of ancient human use have not centrally figured in such research. Here, we describe our field work in the Bandhavgarh National Park and Tiger Reserve and its implications. The earliest remains go back in the form of caves to the 2nd century CE. Shrines of early medieval antiquity, sculptures, and reservoirs begin in the time of Kalachuri kings (7th century CE till the 13th century CE) and continue into the high medieval with Vaghela fortifications and palace remains (13th century CE onwards). What these can tell us when they are immersed in the hills and meadows of Bandhavgarh, in its forests and around its water bodies, is explored here. [ABSTRACT FROM AUTHOR]

Abstract 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.

Abstract 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.

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. [ABSTRACT FROM AUTHOR]

The conservation of large carnivores often requires precise and accurate estimates of their populations. Being cryptic and occurring at low population densities, obtaining an unbiased population estimate is difficult in large carnivores. To overcome the uncertainties in the conventional capture–recapture (CR) methods used to estimate large carnivore densities, more robust methods such as spatially explicit capture-recapture (SECR) framework are now widely used. We modeled the CR data of tiger (Panthera tigris tigris) and leopard (Panthera pardus fusca) in the SECR framework with biotic and abiotic covariates likely believed to influence their densities. An effort of 2,211 trap nights resulted in the capture of 33 and 38 individual tigers and leopards. A total of 95 and 74 detections of tigers and leopards were achieved using 35 pairs of camera traps. Tiger and leopard density were estimated at 4.71 ± 1.20 (3.05–5.11) and 3.03 ± 0.78 (1.85–4.99) per 100 km2. Our results show that leopard density increased with high road density, high terrain ruggedness and habitats with high percentage of cropland and natural vegetation. The tiger density was positively influenced by the mosaic of cropland and natural vegetation. This study provides the first robust density estimates of tiger and leopard within the study area. Our results support the notion that large carnivores can attain moderate densities within human-dominated regions around protected areas relying on domestic livestock. Broader management strategies aimed at maintaining wild prey in the human-dominated areas around protected areas are necessary for large and endangered carnivores’ sustenance in the buffer zones around protected areas.

The conservation of large carnivores often requires precise and accurate estimates of their populations. Being cryptic and occurring at low population densities, obtaining an unbiased population estimate is difficult in large carnivores. To overcome the uncertainties in the conventional capture-recapture (CR) methods used to estimate large carnivore densities, more robust methods such as spatially explicit capture-recapture (SECR) framework are now widely used. We modeled the CR data of tiger (Panthera tigris tigris) and leopard (Panthera pardus fusca) in the SECR framework with biotic and abiotic covariates likely believed to influence their densities. An effort of 2,211 trap nights resulted in the capture of 33 and 38 individual tigers and leopards. A total of 95 and 74 detections of tigers and leopards were achieved using 35 pairs of camera traps. Tiger and leopard density were estimated at 4.71 ± 1.20 (3.05-5.11) and 3.03 ± 0.78 (1.85-4.99) per 100 km2. Our results show that leopard density increased with high road density, high terrain ruggedness and habitats with high percentage of cropland and natural vegetation. The tiger density was positively influenced by the mosaic of cropland and natural vegetation. This study provides the first robust density estimates of tiger and leopard within the study area. Our results support the notion that large carnivores can attain moderate densities within human-dominated regions around protected areas relying on domestic livestock. Broader management strategies aimed at maintaining wild prey in the human-dominated areas around protected areas are necessary for large and endangered carnivores' sustenance in the buffer zones around protected areas. [ABSTRACT FROM AUTHOR]