Aim Unsustainable 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. Location Annamite mountains, Vietnam and Laos. Methods We conducted systematic landscape‐scale surveys across five study sites (four protected areas, one unprotected area) using camera‐trapping and leech‐derived environmental DNA. We analysed detections within a Bayesian multispecies 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. Results Analyses 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. Main conclusion Identifying spatial patterns of biodiversity in heavily defaunated landscapes may require novel methodological and analytical approaches. Our results indicate that 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 analyse data within a Bayesian multispecies 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.

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

17While large carnivores are recovering in Europe, assessing their distributions can help to predict and 18 mitigate conflicts with human activities. Because they are highly mobile, elusive and live at very low 19 density, modeling their distributions presents several challenges due to i) their imperfect detectability, 20 ii) their dynamic ranges over time and iii) their monitoring at large scales consisting mainly of 21 opportunistic data without a formal measure of the sampling effort. Not accounting for these issues can 22 lead to flawed inference about the distribution. 23Here, we focused on the wolf (Canis lupus) that has been recolonizing France since the early 90's. We while accounting for species imperfect detection and time-and space-varying sampling effort using 27 dynamic site-occupancy models. 28Ignoring the effect of sampling effort on species detectability led to underestimating the number of 29 occupied sites by 50% on average. Colonization increased with increasing number of occupied sites at 30 short and long-distances, as well as with increasing forest cover, farmland cover and mean altitude. 31Colonization decreased when high-altitude increased. The growth rate, defined as the number of sites 32 newly occupied in a given year divided by the number of occupied sites in the previous year, decreased 33 over time, from over 100% in 1994 to 5% in 2014. This suggests that wolves are expanding in France 34 but at a rate that is slowing down. Our work shows that opportunistic data can be analyzed with species 35 distribution models that control for imperfect detection, pending a quantification of sampling effort. 36Our approach has the potential for being used by decision-makers to target sites where large carnivores 37 are likely to occur and mitigate conflicts. 39 Key words 40Canis lupus, gray wolf, large carnivores, occupancy models, opportunistic data, sampling effort, species 41 detectability, species distribution models, recolonization 42 43 not peer-reviewed)