Identifying conservation priorities in a defaunated tropical biodiversity hotspot

Abstract

Aim: Unsustainable hunting is leading to widespread defaunation across the tropics. To 21 mitigate against this threat with limited conservation resources, stakeholders must 22 make decisions on where to focus anti-poaching activities. Identifying priority areas in a 23 robust way allows decision-makers to target areas of conservation importance, 24 therefore maximizing the impact of conservation interventions. 25 Location: Annamite mountains, Vietnam and Laos.26 2 Methods: We conducted systematic landscape-scale surveys across five study sites (four 27 protected areas, one unprotected area) using camera-trapping and leech-derived 28 environmental DNA. We analyzed detections within a Bayesian multi-species occupancy 29 framework to evaluate species responses to environmental and anthropogenic 30 influences. Species responses were then used to predict occurrence to unsampled 31 regions. We used predicted species richness maps and occurrence of endemic species to 32 identify areas of conservation importance for targeted conservation interventions.33 Results: Analyses showed that habitat-based covariates were uninformative. Our final 34 model therefore incorporated three anthropogenic covariates as well as elevation, which 35 reflects both ecological and anthropogenic factors. Conservation-priority species tended 36to found in areas that are more remote now or have been less accessible in the past, and 37 at higher elevations. Predicted species richness was low and broadly similar across the 38 sites, but slightly higher in the more remote site. Occupancy of the three endemic species 39 showed a similar trend. 40Main conclusion: Identifying spatial patterns of biodiversity in heavily-defaunated 41 landscapes may require novel methodological and analytical approaches. Our results 42 indicate to build robust prediction maps it is beneficial to sample over large spatial 43 scales, use multiple detection methods to increase detections for rare species, include 44 anthropogenic covariates that capture different aspects of hunting pressure, and analyze 45 data within a Bayesian multi-species framework. Our models further suggest that more 46 remote areas should be prioritized for anti-poaching efforts to prevent the loss of rare 47 and endemic species. 48

Beetle biodiversity in forest habitats in Laos depends on the level of human exploitation

“…Recent studies on coleopterans have increased our taxonomic understanding on several groups of beetles (Brancucci et al 2016) while the study here is in particular pointing towards differences in beetle assemblages between forest habitats and the need to better protect less disturbed forests from human exploitation. Logging and generally habitat degradation are major drivers of declines in biodiversity in tropical biodiversity hotspots such as Laos, while unsustainable hunting is increasingly emerging as a major threat especially larger wildlife (Alroy 2017, Tilker et al, 2020. Compared to other countries in the region, Laos has 21% and thus a large area under protection, however, resources of the government to implement this are limited and more international efforts to support this seem warranted.…”

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

“…For many species of conservation concern, the detection/non-detection surveys underlying estimates of occurrence are the main source of information on their population status, and therefore have a significant role in setting conservation priorities (MacKenzie, 2005;Joseph et al, 2006). They are useful for a wide range of purposes from estimating changes in occurrence to identifying high conservation priority areas (Zipkin et al, 2010;Olea & Mateo-Tomas, 2011;Tilker et al 2020). Occupancy-based diversity profiles are an important contribution to the occupancy toolkit as they allow comparing biodiversity across space and time while accounting for imperfect and varying detection.…”

Measuring protected-area outcomes with leech iDNA: large-scale quantification of vertebrate biodiversity in Ailaoshan nature reserve

“…We addressed this problem in part by using data from two marker genes. More than half of the species were detected by both markers, and high correlation in species richness and co-inertia of community composition between the datasets suggested that broad ecological inferences would not have been strongly affected had either marker been chosen by itself (Figures 3 and 5 [24] reported that wild mammal species occupancy increased with remoteness and elevation. However, as Gogarten et al [95] have found, camera-trap and fly-iDNA data classify habitats similarly, even when the two monitoring methods detect largely different communities (only 6% to 43% of species were found by both methods in any given location).…”

“…As such, eDNA has great potential to complement camera traps and acoustic recorders [24], while circumventing some of the logistical issues with deployment and/or loss of field equipment, as well as taxonomic bias. Here, we focus on iDNA, which is a subset of eDNA [25], as an emerging sample type for broad taxonomic and spatial biodiversity monitoring.…”

“…Our result likely reflects higher levels of anthropogenic disturbance in the lower, more-accessible parts of the park, leading to local extinctions of many wildlife species at lower elevations (due to some combination of hunting, disease transmitted from domestic animals to wildlife, and habitat alteration). Alternatively, some species may simply have moved away from their preferred lower-elevation areas into less suitable habitat to escape human encroachment[24].Elevation and distance to reserve edge were important predictors of vertebrate community richness and composition (Figures 3e,f and 5a,b). Examining the distribution of individual taxa revealed that many species, especially birds and small mammals, had higher occupancy at higher elevation and in the reserve interior.…”