Background The use of environmental DNA for species detection via metabarcoding is growing rapidly. We present a co-designed lab workflow and bioinformatic pipeline to mitigate the 2 most important risks of environmental DNA use: sample contamination and taxonomic misassignment. These risks arise from the need for polymerase chain reaction (PCR) amplification to detect the trace amounts of DNA combined with the necessity of using short target regions due to DNA degradation. Findings Our high-throughput workflow minimizes these risks via a 4-step strategy: (i) technical replication with 2 PCR replicates and 2 extraction replicates; (ii) using multi-markers ( 12S,16S,CytB ); (iii) a “twin-tagging,” 2-step PCR protocol; and (iv) use of the probabilistic taxonomic assignment method PROTAX, which can account for incomplete reference databases. Because annotation errors in the reference sequences can result in taxonomic misassignment, we supply a protocol for curating sequence datasets. For some taxonomic groups and some markers, curation resulted in >50% of sequences being deleted from public reference databases, owing to (i) limited overlap between our target amplicon and reference sequences, (ii) mislabelling of reference sequences, and (iii) redundancy. Finally, we provide a bioinformatic pipeline to process amplicons and conduct PROTAX assignment and tested it on an invertebrate-derived DNA dataset from 1,532 leeches from Sabah, Malaysia. Twin-tagging allowed us to detect and exclude sequences with non-matching tags. The smallest DNA fragment ( 16S ) amplified most frequently for all samples but was less powerful for discriminating at species rank. Using a stringent and lax acceptance criterion we found 162 (stringent) and 190 (lax) vertebrate detections of 95 (stringent) and 109 (lax) leech samples. Conclusions Our metabarcoding workflow should help research groups increase the robustness of their results and therefore facilitate wider use of environmental and invertebrate-derived DNA, which is turning into a valuable source of ecological and conservation information on tetrapods.

1. Invertebrate-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.2. Here, 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 analyse iDNA-derived mammalian detection events in a modern occupancy model that accounts for imperfect detection and compare the results with those from occupancy models parameterised with camera-trap-derived detection events. We also combine leech-iDNA and camera-trap data in a single occupancy model.3. We found consistent estimates of occupancy probabilities produced by our cameratrap and leech datasets. This indicates that the metabarcoding of leech-iDNA method provides reasonable estimates of occupancy and may 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 robustness similar to that of 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 robust and standardised sampling scheme. Synthesis and applications.Here, we describe an approach to analyse detection records of mammals derived from leech samples using an occupancy framework that accounts for leech-specific factors influencing the detection probability.We further combined camera trap and leech data, which lead to increased confidence in occupancy estimates. Our approach is not restricted to the processing of leech samples, but can be used for the analysis of other invertebrate DNA and | 1641Journal of Applied Ecology ABRAMS et Al.

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

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