Biology subjects

Abrams, J. F.

Publications and source records attributed to Abrams, J. F..

7 recordsLinked to original sources

bioRxiv2023

Can snare removal safeguard protected areas caught in the Southeast Asian snaring crisis? Promises and perspectives

Unsustainable snaring is causing biodiversity declines across tropical protected areas, resulting in species extinctions and jeopardizing the health of forest ecosystems. Here, we used 11 years of ranger collected data to assess the impact of intensive snare removal on snaring levels in two protected areas in Viet Nam. Snare removal resulted in significant declines in snare occupancy (36.9, 95% BCI [4.6, 59.0] reduction in percent area occupied), but snaring levels nonetheless remained high (31.4, [23.6, 40.8] percent area occupied), and came with a substantial financial cost. Our results indicate that snare removal remains an important component of efforts to protect tropical protected areas, but by itself, is likely insufficient to address this threat. To stop snaring in protected areas, a multifaceted approach will be necessary that combines short-term reactive snare removal with long-term proactive programs that address the underlying drivers behind snaring.

ecology↗

bioRxiv2022

Biosphere functional integrity for people and Planet

Defining a safe and just biosphere space requires a synthetic scaleable measure of biosphere functional integrity to secure Natures Contributions to People (NCP). Using a systematic review of 153 peer-reviewed studies we estimated the minimum level of functional integrity needed to secure multiple critical NCP, including pollination, pest and disease control, water quality regulation, soil protection, recreation and natural hazards mitigation in human-modified landscapes. We characterise functional integrity by the quantity, quality and spatial configuration of (semi-)natural habitat within any landscape. We find that at least 20-25% of structurally complex and biologically diverse (semi-)natural habitat in each 1 km2 of land area is needed to maintain the supply of multiple NCP simultaneously. Exact quantity, quality and spatial configuration required is dependent on local context, and may differ for individual NCP. Today, about 50-60% of human-modified lands have less than 10% and 20% (semi-)natural habitat per 1 km2 respectively. These areas require immediate attention to regenerate functional integrity in order to secure ecological functioning in those landscapes.

ecology↗

bioRxiv2022

Quantifying the Human Cost of Global Warming

The costs of climate change are often estimated in monetary terms1,2 but this raises ethical issues3. Here we express them in terms of numbers of people left outside the human climate niche4 - defined as the historically highly-conserved distribution of relative human population density with respect to mean annual temperature (MAT). We show that climate change has already put [~]8% of people (>500 million) outside this niche. By end-of-century (2080-2100), current policies leading to around 2.7 {degrees}C global warming5-9 could leave one third (21-42% or 2-4 billion) of a future 9.5 billion population outside the niche. Limiting global warming to 1.5 {degrees}C could halve this exposure, reducing it by [~]1.5 billion people. For the two countries with the most people affected - India and Nigeria - reducing global warming from 2.7 to 1.5 {degrees}C results in a >6-fold decrease in the population exposed to unprecedented temperatures, MAT [≥]29 {degrees}C. The lifetime emissions of [~]3.5 global average citizens today (or [~]1.2 average US citizens) expose 1 future person to MAT [≥]29 {degrees}C by end-of-century. That person comes from a place where emissions today are around half of the global average. These results highlight the need for more decisive policy action to limit the human costs and inequities of climate change.

ecology↗

bioRxiv2020

Capturing biodiversity complexities while accounting for imperfect detection: the application of occupancy-based diversity profiles.

O_LIMeasuring the multidimensional diversity properties of a community is of great importance for ecologists, conservationists and stakeholders. Diversity profiles, a plotted series of Hill numbers, simultaneously capture all the common diversity indices. However, diversity metrics require information on species abundance. They often rely on raw count data without accounting for imperfect and varying detection, although detectability can vary between species and study sites. Hierarchical occupancy models explicitly account for variation in detectability, and Hill numbers have been expanded to allow estimation based on occupancy probability. But agreement between occupancy and abundance-based diversity profiles has not been investigated. C_LIO_LIHere, we fit community occupancy models to simulated animal communities to explore how well occupancy-based diversity profiles reflect true abundance-based diversity. Because we expect occupancy-based diversity to be overestimated, we further tested a novel occupancy thresholding approach to reduce potential biases in the estimated diversity profiles. Finally, we use empirical data from a megadiverse bird community to present how the framework can be extended to consider trait or phylogeny-based similarity when calculating diversity profiles. C_LIO_LIThe simulation study showed that occupancy-based diversity profiles produced among-community patterns in diversity similar to true abundance diversity profiles, although within-community diversity was overestimated with the exception of richness. While applying an occupancy threshold reduced this positive bias, this resulted in negative bias in species richness estimates and slightly reduced the ability to reproduce true differences among the simulated communities. Application of our approach to a large bird dataset revealed differential diversity patterns in communities of different habitat types. Accounting for phylogenetic and ecological similarities between species reduced diversity and its variability among habitats. C_LIO_LIOur framework allows investigating the complexity of diversity for incidence data, while accounting for imperfect and varying detection probabilities, as well as species similarities. Visualizing results in the form of diversity profiles facilitates comparison of diversity between sites or across time. Therefore, our extension to the diversity profile framework will be a useful tool for studying and monitoring biodiversity. C_LI

ecology↗

bioRxiv2019

Identifying conservation priorities in a defaunated tropical biodiversity hotspot

AimUnsustainable hunting is leading to widespread defaunation across the tropics. To mitigate against this threat with limited conservation resources, stakeholders must make decisions on where to focus anti-poaching activities. Identifying priority areas in a robust way allows decision-makers to target areas of conservation importance, therefore maximizing the impact of conservation interventions.\n\nLocationAnnamite mountains, Vietnam and Laos.\n\nMethodsWe conducted systematic landscape-scale surveys across five study sites (four protected areas, one unprotected area) using camera-trapping and leech-derived environmental DNA. We analyzed detections within a Bayesian multi-species occupancy framework to evaluate species responses to environmental and anthropogenic influences. Species responses were then used to predict occurrence to unsampled regions. We used predicted species richness maps and occurrence of endemic species to identify areas of conservation importance for targeted conservation interventions.\n\nResultsAnalyses showed that habitat-based covariates were uninformative. Our final model therefore incorporated three anthropogenic covariates as well as elevation, which reflects both ecological and anthropogenic factors. Conservation-priority species tended to found in areas that are more remote now or have been less accessible in the past, and at higher elevations. Predicted species richness was low and broadly similar across the sites, but slightly higher in the more remote site. Occupancy of the three endemic species showed a similar trend.\n\nMain conclusionIdentifying spatial patterns of biodiversity in heavily-defaunated landscapes may require novel methodological and analytical approaches. Our results indicate to build robust prediction maps it is beneficial to sample over large spatial scales, use multiple detection methods to increase detections for rare species, include anthropogenic covariates that capture different aspects of hunting pressure, and analyze data within a Bayesian multi-species framework. Our models further suggest that more remote areas should be prioritized for anti-poaching efforts to prevent the loss of rare and endemic species.

ecology↗

bioRxiv2018

Habitat-Net: Segmentation of habitat images using deep learning

Understanding environmental factors that influence forest health, as well as the occurrence and abundance of wildlife, is a central topic in forestry and ecology. However, the manual processing of field habitat data is time-consuming and months are often needed to progress from data collection to data interpretation. Computer-assisted tools, such as deep-learning applications can significantly shortening the time to process the data while maintaining a high level of accuracy. Here, we propose Habitat-Net: a novel method based on Convolutional Neural Networks (CNN) to segment habitat images of tropical rainforests. Habitat-Net takes color images as input and after multiple layers of convolution and deconvolution, produces a binary segmentation of the input image. We worked on two different types of habitat datasets that are widely used in ecological studies to characterize the forest conditions: canopy closure and understory vegetation. We trained the model with 800 canopy images and 700 understory images separately and then used 149 canopy and 172 understory images to test the performance of Habitat-Net. We compared the performance of Habitat-Net with a simple threshold based method, a manual processing by a second researcher and a CNN approach called U-Net upon which Habitat-Net is based. Habitat-Net, U-Net and simple thresholding reduced total processing time to milliseconds per image, compared to 45 seconds per image for manual processing. However, the higher mean Dice coefficient of Habitat-Net (0.94 for canopy and 0.95 for understory) indicates that accuracy of Habitat-Net is higher than that of both the simple thresholding (0.64, 0.83) and U-Net (0.89, 0.94). Habitat-Net will be of great relevance for ecologists and foresters, who need to monitor changes in their forest structures. The automated workflow not only reduces the time, it also standardizes the analytical pipeline and, thus, reduces the degree of uncertainty that would be introduced by manual processing of images by different people (either over time or between study sites). Furthermore, it provides the opportunity to collect and process more images from the field, which might increase the accuracy of the method. Although datasets from other habitats might need an annotated dataset to first train the model, the overall time required to process habitat photos will be reduced, particularly for large projects.

ecology↗

bioRxiv2018

Shifting up a gear with iDNA: from mammal detection events to standardized surveys

O_LIInvertebrate-derived DNA (iDNA), in combination with high throughput sequencing, has been proposed as a cost-efficient and powerful tool to survey vertebrate species. Previous studies, however, have only provided evidence that vertebrates can be detected using iDNA, but have not taken the next step of placing these detection events within a statistical framework that allows for robust biodiversity assessments.\nC_LIO_LIHere, we compare concurrent iDNA and camera-trap surveys. Leeches were repeatedly collected in close vicinity to 64 camera-trap stations in Sabah, Malaysian Borneo. We analyze iDNA-derived mammalian detection events in a modern occupancy model that accounts for imperfect detection and compare the results with those from occupancy models parameterized with camera-trap-derived detection events. We also combine leech-iDNA and camera-trap data in a single occupancy model.\nC_LIO_LIWe found consistent estimates of occupancy probabilities produced by our camera-trap and leech datasets. This indicates that the metabarcoding of leech-iDNA method provides reasonable estimates of occupancy and can be a suitable method for studying and monitoring mammal species in tropical rainforests. However, we also show that a more extensive collection of leeches would be needed to assess mammal biodiversity with a similar robustness as with camera traps. As certain taxa were only detected in leeches, we see great potential in complementing camera-trap studies with the iDNA approach, as long as the collection of leeches follows a similar robust and standardized sampling scheme.\nC_LIO_LISynthesis and applications. The approach we describe here is not restricted to the processing of leech samples, but can be used for the analysis of other iDNA and environmental DNA (eDNA) data. Our study is the first step to shift the application of e/iDNA studies from opportunistic ad-hoc collections to systematic surveys required for long-term wildlife populations and biodiversity monitoring programs.\nC_LI

ecology↗