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

Assessing the spatiotemporal interactions of mesopredators in Sumatra's tropical rainforest.

Co-occurrence between mesopredators can be achieved by differentiation of prey, temporal activity, and spatial habitat use. The study of mesopredator interactions is a growing area of research in tropical forests and shedding new light on inter-guild competition between threatened vertebrate species that were previously little understood. Here, we investigate sympatry between the Sunda clouded leopard (Neofelis diardi) and Asiatic golden cat (Pardofelis temminckii) living in the Sumatran rainforests of Indonesia. We investigate: i) spatial overlap of predator-prey species using a combination of single-species occupancy modelling and Bayesian two-species modelling, while controlling for the possible influence of several confounding landscape variables; and, ii) temporal overlap between mesopredators and their shared prey through calculating their kernel density estimate associations. From four study areas, representing lowland, hill, sub-montane and montane forest, 28,404 camera trap nights were sampled. Clouded leopard and golden cat were respectively detected in 24.3% and 22.6% of the 292 sampling sites (camera stations) and co-occurred in 29.6% of the sites where they were detected. Golden cat occupancy was highest in the study area where clouded leopard occupancy was lowest and conversely lowest in the study area where clouded leopard occupancy was highest. However, our fine-scale (camera trap site) analyses found no evidence of avoidance between these two felid species. While both mesopredators exhibited highest spatial overlap with the larger-bodied prey species, temporal niche separation was also found. Clouded leopard was more nocturnal and, consequently, had higher temporal overlap with the more nocturnal prey species, such as porcupine and mouse deer, whereas the more diurnal golden cat had higher overlap with the strictly diurnal great argus pheasant. The Bayesian two species occupancy modelling approach applied in our study fills several important knowledge gaps of Sumatra's lesser known mesopredators and provides a replicable methodology for studying interspecific competition for other small-medium sized carnivore species in the tropics

Conserving tigers Panthera tigris in selectively logged Sumatran forests

The response of most large carnivores to selective logging is poorly understood. On the one hand, selective logging may represent loss of important habitat, yet, on the other hand, selective logging may increase browse availability for a terrestrial ungulate prey base, thereby indirectly benefiting large carnivores. Using a camera trap-based sampling method, we estimate tiger density in two primary-selectively logged forest areas that straddle Kerinci Seblat National Park, Sumatra. We then investigate potential differences between the habitat use of tigers: within these study areas and forest types; and, within the finer-scale landscape features associated with these covariates. Across the mixed forest study areas, tiger density estimates (adult individuals/100 km(2) +/- S.E.) of 2.95 +/- 0.56 and 1.55 +/- 0.34 were produced. However, within these areas, tigers showed a preference for primary over degraded forest, and this was related to the greater accessibility of degraded forest sites to people, e.g., through their proximity to roads. Presently, the majority of Sumatran tigers occur within large tracts of primary forest, but these extend outside of the island's protected area borders, and these unprotected forests are especially at risk from the high levels of deforestation in Sumatra. As forest is cleared, previously remote, and therefore safer, tracts of primary forest become accessible and, eventually, degraded. Yet, from our study, degraded forest in combination with primary forest supported sufficiently high tiger densities and can, therefore, make an important contribution to tiger conservation. It is therefore essential to lessen the detrimental effects of accessibility through increasing law enforcement and destroying ex-logging roads. Crown Copyrigh

Estimating occupancy of a data deficient mammalian species living in tropical rainforests: Sun bears in the Kerinci Seblat region, Sumatra

Tropical mammals represent some of the most threatened species, but also the least known because they tend to be difficult to study. To objectively evaluate the conservation status of these species, standardized methods are urgently required. The sun bear Helarctos malayanus is a case in point: it is cryptic, difficult to detect and consequently classified on the IUCN Red List as Data Deficient, and the highest priority for bear conservation research. In this study, we apply a detection/non-detection sampling technique using camera trap data with environmental covariates to estimate sun bear occupancy from three tropical forest study areas with different levels of degradation and protection status in Sumatra. Sun bear detections, and encounter rates, were highest in one of the primary forest study areas, but sun bear occupancy was highest in the degraded forest study area. Whilst, sun bears were recorded at a greater proportion of camera placements in degraded forest, these records were often on only one occasion at each placement, which greatly increased the final occupancy estimate. Primary forests with their large fruiting trees undoubtedly represent good sun bear habitat, but our results indicate that degraded forest can also represent important habitat. These forests should therefore not be considered as having limited conservation value and assigned to other uses, such as oil palm production, as has previously happened in Sumatra. Estimating occupancy between years will yield information on the population trends of sun bears and other tropical mammals, which can be used to provide more reliable conservation assessments

Camera trapping rare and threatened avifauna in west-central Sumatra

Tropical forests are becoming increasingly degraded and fragmented by logging, which can affect the survival of forest bird species in different ways. In this study, we present avifauna data collected from a monitoring programme in west-central Sumatra that set camera traps in three study areas with different habitat types, levels of degradation and protection status. From 5,990 camera trap-nights, 248 independent bird photographs were recorded, comprising four orders and nine species, including three endemic species. The Great Argus Pheasant (Argusianus argus) was recorded in all study areas and most frequently (n = 202 photographs), followed by the threatened Salvadori's Pheasant (Lophura inornata). The greatest diversity of bird species (five) and abundance index (1.44 bird photographs/loo trap-nights) was recorded from a primary hillsubmontane forest site located inside Kerinci Seblat National Park (KSNP) bordering degraded forest in a former logging concession recently repatriated into KSNP. However, inside a primary-selectively logged hill-submontane forest site spread over KSNP and an ex-logging concession, a Sumatran Ground Cuckoo (Carpococcyx viridis) was photographed. This species is noteworthy because prior to this study it had only been documented once since 1916. It is therefore crucial to use the camera trap results to increase the protection status for the ground cuckoo area. This has already happened in the other two study areas, where camera trap data have been used to reclassify the areas as Core Zones, the highest level of protection inside KSNP. This study illustrates how routine monitoring can have wider benefits through recording, and conserving, threatened and endemic non-target species in unexpected habitats that might not otherwise have been surveyed