Carnivore hotspots in Peninsular Malaysia and their landscape attributes

Spatial Distribution Patterns, Environmental Drivers, and Hotspot Dynamics of the European Rabbit on a Mediterranean Island: Implications for Conservation and Management

The European rabbit (Oryctolagus cuniculus) presents a significant conservation and management challenge in Greece. While it has been listed in national biodiversity assessments, its population dynamics on the island of Lemnos demonstrate the characteristics of a highly adaptable and rapidly expanding species, exerting substantial ecological and economic impacts. Addressing this issue requires a spatially explicit understanding of its distribution patterns and habitat preferences, particularly given its extensive population growth over the past three decades. To this end, we conducted 40 field surveys across the island, documenting 1534 presence records of the species. We applied Kernel Density Estimation, Getis-Ord Gi *, and Anselin Local Moran's I to identify the spatial distribution patterns and significant hotspots. A spatial lag model was used to quantify hotspot intensity and clustering dynamics, while abiotic, biotic, and anthropogenic factors were analyzed to assess habitat associations. Our results revealed that rabbit hotspots are predominantly concentrated in fertile lowland agroecosystems, with nearly 60% of high-density areas overlapping conservation zones. Soil and field conditions, grazing-supporting landscapes, and arable and subsidized agricultural areas emerged as significant predictors of O. cuniculus presence. The observed spatial dependencies indicated that while hotspot intensities and clustering dynamics are influenced by the conditions in neighboring areas, habitat characteristics remain fundamental in shaping their distribution, highlighting the broader landscape-scale spatial patterns affecting rabbit populations. These findings underscore the necessity of adopting spatially informed management strategies that mitigate agricultural impacts while accounting for interconnected spatial dynamics, providing a foundation for informed decision-making to manage rabbit populations while balancing conservation and agricultural priorities.

Detection rates and diel activity patterns of four understudied felids from Borneo

Carnivore guilds are charismatic and have vital and irreplaceable roles in their native ecosystems, yet many of these species are threatened and remain understudied. Borneo is a biodiversity hotspot that hosts a rich diversity of endemic wildlife but is threatened by deforestation and habitat loss. Using cameras placed by the Smithsonian Institution in Sabah, Borneo, we assessed the detection rates and diel activity patterns of the native felid species. Across 51 camera trap sites between 2016 and 2019, felids were detected 55 times across a combined 9958 trap nights, including 20 independent detection events for Sunda leopard cats, 15 for Sunda clouded leopards, 12 for Borneo bay cats, and 5 for marbled cats, with no detections of flat-headed cats. Collectively, this demonstrates the challenge of sampling cryptic species that have declined due to habitat loss and conflict with humans. Despite this, we show that Borneo bay cats and marbled cats exhibited different diel activity patterns than Sunda cloud leopards, suggesting the smaller species use temporal displacement to avoid competition and predation. Sunda leopard cats exhibited broadly similar activity patterns to Sunda clouded leopard, potentially because the two species typically occupy different dietary and habitat niches. These results demonstrate the importance of devoting future research towards monitoring these species and understanding the mechanisms by which they co-exist.

Distributed Hydrological Model Based on Machine Learning Algorithm: Assessment of Climate Change Impact on Floods

Rapid population growth, economic development, land-use modifications, and climate change are the major driving forces of growing hydrological disasters like floods and water stress. Reliable flood modelling is challenging due to the spatiotemporal changes in precipitation intensity, duration and frequency, heterogeneity in temperature rise and land-use changes. Reliable high-resolution precipitation data and distributed hydrological model can solve the problem. This study aims to develop a distributed hydrological model using Machine Learning (ML) algorithms to simulate streamflow extremes from satellite-based high-resolution climate data. Four widely used bias correction methods were compared to select the best method for downscaling coupled model intercomparison project (CMIP6) global climate model (GCMs) simulations. A novel ML-based distributed hydrological model was developed for modelling runoff from the corrected satellite rainfall data. Finally, the model was used to project future changes in runoff and streamflow extremes from the downscaled GCM projected climate. The Johor River Basin (JRB) in Malaysia was considered as the case study area. The distributed hydrological model developed using ML showed Nash–Sutcliffe efficiency (NSE) values of 0.96 and 0.78 and Root Mean Square Error (RMSE) of 4.01 and 5.64 during calibration and validation. The simulated flow analysis using the model showed that the river discharge would increase in the near future (2020–2059) and the far future (2060–2099) for different Shared Socioeconomic Pathways (SSPs). The largest change in river discharge would be for SSP-585. The extreme rainfall indices, such as Total Rainfall above 95th Percentile (R95TOT), Total Rainfall above 99th Percentile (R99TOT), One day Max Rainfall (R × 1day), Five-day Max Rainfall (R × 5day), and Rainfall Intensity (RI), were projected to increase from 5% for SSP-119 to 37% for SSP-585 in the future compared to the base period. The results showed that climate change and socio-economic development would cause an increase in the frequency of streamflow extremes, causing larger flood events.

Carnivores and their prey in Sumatra: Occupancy and activity in human-dominated forests

Understanding the effect of anthropogenic disturbance, and its interaction with carnivores and their prey, is crucial to support the conservation of threatened carnivores, particularly in rapidly changing landscapes. Based on systematic camera-trap sampling of four protected areas in Riau Province of central Sumatra, we assessed the habitat occupancy and spatiotemporal overlap between people, potential carnivore prey, and four threatened species of medium-sized or large carnivores: Sumatran tigers (Panthera tigris sumatrae), Malayan sun bears (Helarctos malayanus), dholes (Cuon alpinus), and Sunda clouded leopards (Neofelis diardi). To assess spatial overlap of target species, we used single-species occupancy models and applied a Species Interaction Factor (SIF) to conditional two-species occupancy models. We also used kernel density estimation (KDE) to assess temporal overlap among these species. Our habitat use models showed that altitude (elevation) strongly influenced the occupancy of all large carnivores and potential prey species. Except for Sunda clouded leopards, the occurrence of large carnivore species was positively related to the spatial co-occurrence of humans (SIF > 1). In addition, we found that sun bears and dholes both exhibited high spatial overlap with tigers, and that sun bears alone exhibited high temporal overlap with people. Our findings contribute to an improved understanding of the contemporary ecology of carnivores and their prey in rapidly changing, southeast Asian landscapes. Such knowledge is important to the conservation and recovery of large carnivores in conservation hotspots that are increasingly dominated by humans across Sumatra, as well as globally.

Spatial-temporal changes of forests and agricultural lands in Malaysia from 1990 to 2017

Forests and agricultural lands are the main resources on the earth's surface and important indicators of regional ecological environments. In this paper, Landsat images from 1990 and 2017 were used to extract information on forests in Malaysia based on a remote-sensing classification method. The spatial-temporal changes of forests and agricultural lands in Malaysia between 1990 and 2017 were analyzed. The results showed that the natural forests in Malaysia decreased by 441 Mha, a reduction of 21%. The natural forests were mainly converted into plantations in Peninsular Malaysia and plantations and secondary forests in East Malaysia. The area of agricultural lands in Malaysia increased by 55.7%, in which paddy fields increased by 1.1% and plantations increased by 98.2%. Paddy fields in Peninsular Malaysia are mainly distributed in the north-central coast and the Kelantan Delta. The agricultural land in East Malaysia is dominated by plantations, which are mainly distributed in coastal areas. The predictable areas of possible expansion for paddy fields in Peninsular Malaysia's Kelantan (45.2%) and Kedah (16.8%) areas in the future are large, and in addition, the plantations in Sarawak (44.7%) and Sabah (29.6%) of East Malaysia have large areas for expansion. The contradiction between agricultural development and protecting the ecological environment is increasingly prominent. The demand for agriculture is expected to increase further and result in greater pressures on tropical forests. Governments also need to encourage farmers to carry out existing land development, land recultivation, or cooperative development to improve agricultural efficiency and reduce the damage to natural forests.

Oil palm plantations in Peninsular Malaysia: Determinants and constraints on expansion

Agricultural expansion is one of the leading causes of deforestation in the tropics and in Southeast Asia it is predominantly driven by large-scale production for international trade. Peninsular Malaysia has a long history of plantation agriculture and has been a predominantly resource-based economy where expanding plantations like those of oil palm continue to replace natural forests. Habitat loss from deforestation and expanding plantations threatens Malaysian biodiversity. Expanding industrial plantations have also been responsible for drainage and conversions of peatland forests resulting in release of large amounts of carbon dioxide. The demand for palm oil is expected to increase further and result in greater pressures on tropical forests. Given Malaysia’s high biophysical suitability for oil palm cultivation, it is important to understand patterns of oil palm expansion to better predict forest areas that are vulnerable to future expansion. We study natural forest conversion to industrial oil palm in Peninsular Malaysia between 1988 and 2012 to identify determinants of recent oil palm expansion using logistic regression and hierarchical partitioning. Using maps of recent conversions and remaining forests, we characterize agro-environmental suitability and accessibility for the past and future conversions. We find that accessibility to previously existing plantations is the strongest determinant of oil palm expansion and is significant throughout the study period. Almost all (> 99%) of the forest loss between 1988 and 2012 that has been converted to industrial oil palm plantations is within 1 km from oil palm plantations that have been established earlier. Although most forest conversions to industrial oil palm have been in areas of high biophysical suitability, there has been an increase in converted area in regions with low oil palm suitability since 2006. We find that reduced suitability does not necessarily restrict conversions to industrial oil palm in the region; however, lack of access to established plantations does.

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.