Not seeing the forest for the trees: Generalised linear model out-performs random forest in species distribution modelling for Southeast Asian felids

Species Distribution Models (SDMs) are a powerful tool to derive habitat suitability predictions relating species occurrence data with habitat features. Two of the most frequently applied algorithms to model species-habitat relationships are Generalised Linear Models (GLM) and Random Forest (RF). The former is a parametric regression model providing functional models with direct interpretability. The latter is a machine learning non-parametric algorithm, more tolerant than other approaches in its assumptions, which has often been shown to outperform parametric algorithms. Other approaches have been developed to produce robust SDMs, like training data bootstrapping and spatial scale optimisation. Using felid presence-absence data from three study regions in Southeast Asia (mainland, Borneo and Sumatra), we tested the performances of SDMs by implementing four modelling frameworks: GLM and RF with bootstrapped and non-bootstrapped training data. With Mantel and ANOVA tests we explored how the four combinations of algorithms and bootstrapping influenced SDMs and their predictive performances. Additionally, we tested how scale-optimisation responded to species' size, taxonomic associations (species and genus), study area and algorithm. We found that choice of algorithm had strong effect in determining the differences between SDMs' spatial predictions, while bootstrapping had no effect. Additionally, algorithm followed by study area and species, were the main factors driving differences in the spatial scales identified. SDMs trained with GLM showed higher predictive performance, however, ANOVA tests revealed that algorithm had significant effect only in explaining the variance observed in sensitivity and specificity and, when interacting with bootstrapping, in Percent Correctly Classified (PCC). Bootstrapping significantly explained the variance in specificity, PCC and True Skills Statistics (TSS). Our results suggest that there are systematic differences in the scales identified and in the predictions produced by GLM vs. RF, but that neither approach was consistently better than the other. The divergent predictions and inconsistent predictive abilities suggest that analysts should not assume machine learning is inherently superior and should test multiple methods. Our results have strong implications for SDM development, revealing the inconsistencies introduced by the choice of algorithm on scale optimisation, with GLM selecting broader scales than RF

Predicting biodiversity richness in rapidly changing landscapes: climate, low human pressure or protection as salvation?

Rates of biodiversity loss in Southeast Asia are among the highest in the world, and the Indo-Burma and South-Central China Biodiversity Hotspots rank among the world’s most threatened. Developing robust multi-species conservation models is critical for stemming biodiversity loss both here and globally. We used a large and geographically extensive remote-camera survey and multi-scale, multivariate optimization species distribution modelling to investigate the factors driving biodiversity across these two adjoining biodiversity hotspots. Four major findings emerged from the work. (i) We identified clear spatial patterns of species richness, with two main biodiverse centres in the Thai-Malay Peninsula and in the mountainous region of Southwest China. (ii) Carnivores in particular, and large ungulates to a lesser degree, were the strongest indicators of species richness. (iii) Climate had the largest effect on biodiversity, followed by protected status and human footprint. (iv) Gap analysis between the biodiversity model and the current system of protected areas revealed that the majority of areas supporting the highest predicted biodiversity are not protected. Our results highlighted several key locations that should be prioritized for expanding the protected area network to maximize conservation effectiveness. We demonstrated the importance of switching from single-species to multi-species approaches to highlight areas of high priority for biodiversity conservation. In addition, since these areas mostly occur over multiple countries, we also advocate for a paradigmatic focus on transboundary conservation planning.The majority of the team, as well as the data, were part of the core WildCRU effort supported principally by a Robertson Foundation grant to DWM

The analysis of Neighbourhood Watch structure using Geographic Information System (GIS)

This project explores the use of spatial analysis tool such as Geographic Information System (GIS) in the study of crime prevention schemes. This study is concentrating on the Toowoomba region. Crime prevention programmes have been established to reduce crime rates in particular communities as well as nationally. The Neighbourhood Watch (NHW) Scheme falls into a category of the crime prevention programmes. This is when a number of areas are set up in which members of a community play an important role in preventing crime within their respective community area. This study focuses on determining effective performance of Neighbourhood Watch Structure. Datasets used to perform this analysis include a street central line and Toowoomba suburb and district boundary dataset provided by Toowoomba city council. Major data sets such as crime statistics and information on Toowoomba Neighbourhood Watch areas were provided by Toowoomba police department. The analysis of this study was performed in 3 phases. Mapping of crime distribution within Toowoomba overtime was one of the analysis phases. Further, the analysis also included mapping of individual crime type over a ten month's period. Mapping of individual crime class within selected areas was also performed as part of the analysis methodology. Findings indicated the possibility in utilizing GIS in the analysis of the crime prevention program such as NHW. Generated results of this project also showed that the useful information supporting policing could be provided through the implementation of analysis methods. These methods were used to provide the final findings of this project

The intraguild interactions of large carnivores and their impacts on their prey and other smaller carnivores in the Nam Et - Phou Louey National Protected Area, Lao PDR

In an environment, the species populations are not only affected by the abiotic factors but also by the interactions between the organisms. In contexts where human-induced habitat loss and anthropogenic disturbances are prominent, inter-specific interaction will undoubtedly increase, at least initially, as wild animals are confined to smaller areas with greater resource limitations. In light of this, a key question of conservation concern emerges: what mechanisms do subordinate predators adopt in order to persist alongside dominant predators when space and prey resources are limited? This thesis, therefore, aimed to investigate interactions of sympatric carnivores (felids and dhole) in the Nam Et - Phou Louey National Protected Area (hereafter NEPL) in northern Lao PDR. The NEPL harbours six felid species (tiger Panther tigris, leopard Panthera pardus, clouded leopard Neofelis nebulosa, Asian golden cat Catopuma temminckii, marbled cat Pardofelis marmorata, and leopard cat Prionailurus bengalensis) and dhole Cuon alpinus. Over the past decade, the main threat to the flora and fauna of the NEPL has been the unregulated over-harvesting of animals and plants and forest clearance. This study not only will contribute to our scientific knowledge of NEPLâs carnivore species, the findings will also benefit conservation efforts via providing much-needed information regarding their ecology and status to guide decision making. First, I investigated the status of all hypercarnivores and prey species of NEPL (Chapter 2). My results showed that tiger and leopard had been extirpated, most likely due to the rise of indiscriminate snaring. Consequently, clouded leopard and dhole, the largest and most common remaining hypercarnivores. Large ungulate prey species (e.g., gaur Bos gaurus and sambar Cervus unicolor) were rare, whereas smaller ungulate species were relatively common. Overall, NEPL still had rich communities of carnivores and prey. Second, I estimated density trends of clouded leopard, leopard cat, and marbled cat, to better understand what likely happens to smaller felids when the top felid, tiger, disappears from the system (Chapter 3). The findings showed a declining trend in density across the study years for all three felid species, with a stronger rate of decline for clouded leopard and leopard cat. Snaring likely was responsible for the continuing decline of the smaller felids, and this factor was likely to override any potential effects of mesopredator release on their densities and interactions. Third, I assessed spatial and temporal niche segregation, to understand factors facilitating coexistence of all sympatric hypercarnivores (Chapter 4). Dhole and clouded leopard did not have high spatial and temporal avoidance, but presumably had some differences in dietary niches. Asian golden cat adopted a cathemeral activity pattern and is likely to have diet specialisation to co-occur with dhole and clouded leopard. The leopard cat and marbled cat are likely to coexist with the Asian golden cat via prey specialisation whereas leopard cat and marbled cat clearly segregated temporally and potentially by diet. Fourth, I predicted the distribution of suitable habitat across the country for the three largest remaining hypercarnivores: dhole, clouded leopard, and Asian golden cat (Chapter 5). My results highlighted the importance of the National Protection Forest Areas for the conservation of carnivores in the country, especially in the northern provinces and along the central Annamite landscape. Overall, my findings contributed to the knowledge of the ecology and interactions of a diverse carnivore community in northern Laos, and results will be used to guide management plans and conservation efforts for carnivores and their prey within NEPL and other landscapes throughout the country. </p

Diet and prey selection of clouded leopards and tigers in Laos

Abstract In Southeast Asia, conservation of ‘Vulnerable’ clouded leopards (Neofelis nebulosa) and ‘Endangered’ tigers (Panthera tigris) might depend on the management of their preferred prey because large felid populations are limited by the availability of suitable prey. However, the diet of clouded leopards has never been determined, so the preferred prey of this felid remains unknown. The diet of tigers in the region has been studied only from one protected‐area complex in western Thailand, but prey preferences were not determined. To better understand the primary and preferred prey of threatened felids, we used DNA‐confirmed scats and prey surveys to determine the diet and prey selection of clouded leopards and tigers in a hilly evergreen forest in northern Laos. For clouded leopards, the primary prey was wild pig (Sus scrofa; 33% biomass consumed), followed by greater hog badger (Arctonyx collaris; 28%), small rodents (15%), and mainland serow (Capricornis sumatraensis; 13%; hereafter, serow). For tigers, the primary prey was wild pig (44%), followed by serow (18%), sambar (Rusa unicolor; 12%), and Asiatic black bear (Ursus thibetanus; 10%). Compared to availability, serow was positively selected by both clouded leopards (D = 0.69) and tigers (0.61), whereas all other ungulate species were consumed in proportion to the availability or avoided. Our results indicate that clouded leopards are generalist predators with a wide prey spectrum. Nonetheless, mid‐sized ungulates (50–150 kg) comprised nearly half of their diet, and were the preferred prey, supporting a previous hypothesis that the enlarged gape and elongated canines of clouded leopards are adaptations for killing large prey. Because serow was the only ungulate preferred by both felids, we recommend that serow populations be monitored and managed to help conservation efforts for clouded leopards and tigers, at least in hilly evergreen forests of Southeast Asia

Riverbank changes along the Mekong River: Remote sensing detection in the Vientiane-Nong Khai area

10.1016/j.quaint.2007.10.015Quaternary International1861100-11

Mainland Clouded Leopard Habitat Suitability Model - 4 Thresholds

The zip file contains four GIS raster layers representing a range of thresholds of the mainland clouded leopard habitat suitability model. Layers include 50th, 75th, 90th, and 97.5th percentiles of the multi-scale GLMM. Suitable habitat is classified as 1; all habitat below a specified threshold is classified as 0. Raster resolution: 250m; coordinate system: Asia South Albers Equal Area Conic

Mainland Clouded Leopard Habitat Suitability Model - Continuous

This GIS raster layer is the projected multi-scale habitat suitability model for the mainland clouded leopard, based on beta coefficients of the GLMM and a mean of 79.47 camera trap nights averaged across all camera stations. Model projection units are number of clouded leopard observations predicted across multivariate niche space, given 79.47 active camera trap nights. Thus the specific output value is of little importance; the model output should rather be interpreted on a relative scale. Habitat >4000m elevation was reclassified as non-suitable and assigned the lowest model output value. Resolution: 250m; coordinate system: Asia South Albers Equal Area Conic

Top 28 High Quality Habitat Patches for the Mainland Clouded Leopard

This GIS raster layer contains the top 28 patches of high quality habitat for the mainland clouded leopard, based on the 90th percentile threshold of the habitat suitability model. We identified and ranked all habitat patches >1000km2, based on descending habitat area. Table S8 in the Supplementary Information file associated with Macdonald et al. (2019) provides additional information for each habitat patch, including associated protected areas, mean correlation length within each patch, and percent of patch area designated as protected. Resolution: 500m; coordinate system: Asia South Albers Equal Area Conic

Data from: Multi-scale habitat modeling identifies spatial conservation priorities for mainland clouded leopards (Neofelis nebulosa)

Aim Deforestation is rapidly altering Southeast Asian landscapes, resulting in some of the highest rates of habitat loss worldwide. Among the many species facing declines in this region, clouded leopards rank notably for their ambassadorial potential and capacity to act as powerful levers for broader forest conservation programmes. Thus identifying core habitat and conservation opportunities are critical for curbing further Neofelis declines and extending umbrella protection for diverse forest biota similarly threatened by widespread habitat loss. Furthermore, a recent comprehensive habitat assessment of Sunda clouded leopards (N. diardi) highlights the lack of such information for the mainland species (N. nebulosa), and facilitates a comparative assessment. Location Southeast Asia. Methods Species-habitat relationships are scale-dependent, yet <5% of all recent habitat modeling papers apply robust approaches to optimize multivariate scale relationships. Using one of the largest camera trap datasets ever collected, we developed scale-optimized species distribution models for two con-generic carnivores, and quantitatively compared their habitat niches. Results We identified core habitat, connectivity corridors, and ranked remaining habitat patches for conservation prioritization. Closed canopy forest was the strongest predictor, with ~25% lower Neofelis detections when forest cover declined from 100 to 65%. A strong, positive association with increasing precipitation suggests ongoing climate change as a growing threat along drier edges of the species' range. While deforestation and land use conversion were deleterious for both species, N. nebulosa was uniquely associated with shrublands and grasslands. We identified 800km2 as a minimum patch size for supporting clouded leopard conservation. Main Conclusions We illustrate the utility of multi-scale modeling for identifying key habitat requirements, optimal scales of use, and critical targets for guiding conservation prioritization. Curbing deforestation and development within remaining core habitat and dispersal corridors, particularly in Myanmar, Laos, and Malaysia, is critical for supporting evolutionary potential of clouded leopards and conservation of associated forest biodiversity