Indian Elephant 3d Model Free Download [2021]
Sumiko Fagnoni
Abstract:The Asian elephant (Elephas maximus Linnaeus) is a globally endangered species, an internationally protected species, and a first-class protected animal in China. However, future climate change and human activities exacerbate the instability of its habitat range, leading to a possible reduction in the range. By using multi-source remote sensing data and products, as well as climate change models, including ASTER GDEM v3, Landsat8 OLI image and ClimateAP, we examined the effects of ecological factors related to climate and natural and anthropogenic influences on the distribution of Asian elephants in Sipsongpanna. Multiyear elephant field tracking data were used with a MaxEnt species distribution model and the climate model. First, the distribution of Asian elephants in potentially suitable areas in Sipsongpanna was simulated under current climatic conditions without considering human activities. The predicted distribution was verified by existing Asian elephant migration trajectories. Subsequently, the distribution of potentially suitable areas for Asian elephants in Sipsongpanna was simulated under two climate change scenarios (RCP4.5, RCP8.5) in three periods (2025, 2055, and 2085). The changes in potentially suitable areas for Asian elephants in Sipsongpanna were analyzed under multiple climate change scenarios for the current (2017) and different future periods by considering the effects of human activities. The results show the following: (1) under anthropogenic interference (AI), the optimal MaxEnt model has a high prediction accuracy with the area under the curve (AUC) of 0.913. The feature combination (FC) includes linear, quadratic, and threshold features, and the regularization multiplier (RM) is 2.1. (2) Jackknife analyses of the non-anthropogenic interference (NAI) and anthropogenic interference (AI) scenarios indicate that topography (altitude (Alt)), temperature (mean warmest month temperature (MWMT)), and precipitation (mean annual precipitation (MAP)) are the top three factors influencing the distribution of Asian elephants. (3) The total area suitable for Asian elephants under current climate conditions and AI accounts for 46.35% of the total area. Areas of high suitability (occurrence probability >0.5) are located in Jinghong City in central Sipsongpanna and Mengla County in southeastern Sipsongpanna. Among them, the minimum habitat range and ecological corridors are mainly located in Mengman Town, Mohan Town, Mengla Town, Mengban Township, Dadugang Township, and Mengwang Township. (4) The change in potentially suitable areas for Asian elephants between current and future conditions is small under AI and large under undisturbed conditions.Keywords: remote sensing; Asian elephant; habitat suitability; climate change; MaxEnt; population projections; Sipsongpanna
At the local scale, past efforts to monitor Asian elephant populations have focused on estimation of population size and density. However, regional, national and range-wide estimates are based on indefensible extrapolation of such local-scale (i.e. individual reserve) estimates that vary widely in their reliability[4]. Blake and Hedges[4] justifiably worry that beyond locations and relative abundances of some of these elephant populations, we know very little. Not only have unreliable field and analytical methods been applied to estimate elephant numbers, there is also a critical mismatch between the population parameter being estimated (i.e. abundance or density) and the spatial scale of such estimation effort. While abundance and density can be reliably estimated at smaller spatial scales (e.g. individual reserves) using appropriate methods[40,49],estimates of abundance or density at larger scales tend to be unreliable (see[4]).
The appropriate metric for landscape level studies in wide-ranging species such as elephants is the estimation of habitat occupancy: the probability (Pr) that a species is present within a site(see[44,50]). Formal occupancy modeling explicitly accounts for imperfect detectability and other observation processes, which, if ignored, could lead to biased inferences of occupancy, habitat relationships and temporal changes in these. The approach also permits modeling of ecological and anthropogenic effects on detection probabilities as well as habitat occupancy (or both). This removes biases from heterogeneity among sites, in either detectability or occupancy, as well the confounding of true patterns in occupancy with detectability and sampling probability[41,44]. Consequently, rigorous occupancy modeling is rapidly emerging as a method of choice. It has been successfully used on other wide-ranging species, such as the tiger Panthera tigris [51].
In this study, we used habitat occupancy sampling of Asian elephants over a large and diverse landscape in the Western Ghats of Karnataka State in India. We employed a modeling approach recently developed by Hines et al. [52] that relies on spatial rather than temporal replication and uses surveys of animal signs, which has been successfully used to investigate the distribution of tigers [51] and dhole Cuon alpinus [53] based on the same survey effort. Our estimates provide a useful baseline to monitor future changes in spatial distribution of the largest wild population of Asian elephants. We also elucidate the role of key ecological and anthropogenic covariates as determinants of elephant habitat occupancy. We demonstrate how this approach can improve future management and monitoring of Asian elephants.
We conducted our surveys across a c. 38,000-km2 landscape (Fig 1), with our sampling frame defined by the presence of natural vegetation types including forest (evergreen, moist deciduous, dry deciduous, thorn scrub), tree plantations (e.g. Tectona grandis, Acacia auriculiformis, Eucalyptus spp. Casuarina equisetifolia, among others), tree savanna, shrub savanna, grassland (in our landscape, primarily montane grasslands) and uncultivated revenue department or private lands. Thus, other than a few areas where small numbers of itinerant and conflict-prone elephants subsist in heavily human-dominated areas (e.g. in Tumkur and Hassan districts) and which have been identified for removal [54], our field surveys covered the entire distributional range of elephants in the state of Karnataka. The surveyed areas encompassed the full gradients in rainfall, land cover and intensity of human use.
The altitude in the study area peaks at 1927m, with the terrain abruptly rising from the coastal plains and then descending from the ridge of the mountains eastwards, intergrading into the Deccan Plateau. The mean annual rainfall declines from well over 5000 mm in the coastal plains and crest of the Ghats to 600 mm eastwards [55]. Western slopes of the Ghats support wet evergreen rainforests and montane shola-grassland habitats; semi-evergreen and moist and dry deciduous forest types dominate areas eastward, where rainfall declines progressively. These blocks of natural forests are interspersed with or abut areas under varied forms of agriculture, horticulture and plantations as well as human settlements. We defined elephant habitat as comprising all the natural vegetation types described above, which together cover 21,167 km2 of the study landscape [51], and support some of the largest populations of elephants as well as other threatened large mammal species typical of the region [56,57].
The landscape includes 14 wildlife reserves that cover an area of 5,500 km2 and lie in a human-dominated matrix populated by >10.2 million people. The elephant habitats are subject to threats from illegal hunting, livestock grazing and forest biomass extraction as well as pressures from developmental projects and industrial growth [51]. For detailed accounts of the study area see [51,56,58,59].
Because of low elephant densities, low sighting probabilities and logistical factors, rather than sightings, we chose to survey and detect elephant presence via signs, primarily fresh dung [40]. Although direct sightings, tracks and other evidences were also recorded, we based our analyses on fresh dung, since other signs have very different times of persistence (direct sightings are instantaneous), affecting both detectability and occupancy. Because of logistical practicality (see[51]) we chose spatial [62] over temporal replication, to estimate detectability of elephant signs. To address the potential problem of spatially autocorrelated non-closure of occupancy state along such spatial replicates, we used the recently developed occupancy model of Hines et al. [52], as more fully described below.
In addition to additive effects of covariates on occupancy, we also included interactions between some of the covariates where we expected the influence of one covariate on ψ to depend on the value of another covariate. Pettorelli et al. [71,72] show that NDVI is an excellent index of vegetation productivity and list several examples of the successful use of NDVI to understand patterns of distribution and abundance of animals. We included a quadratic term for vegetation productivity in some models because we expected probability of elephant occupancy to exhibit a non-linear, peaked response from low (thorn scrub-tree savannah-dry deciduous) through medium (moist deciduous) to highly productive (wet evergreen) habitats. All covariates were modeled using the logit link function[44,50]. Covariate values were scaled as described in Table 1. We used the software program PRESENCE v 4.1 [73] to implement all these occupancy models and to estimate relevant parameters.
The covariance between estimated site-specific occupancy estimates in the expression above was estimated using a parametric bootstrap [74] where the untransformed β parameter estimates and the associated variance-covariance matrix from the best model were used to generate 100 random deviates from a multivariate normal distribution using the R package MSBVAR [75,76]. These simulated β values were then used to compute 100 site-specific occupancy probabilities for each of the 205 sites using the inverse logit link function. For each pair of site-specific occupancy estimates, covariance was then computed as shown below (please see Appendix S2 in [51] for more details):
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