Protected areas are key to meeting biodiversity conservation goals, but direct measures of effectiveness have proven difficult to obtain. We address this challenge by using environmental DNA from leech-ingested bloodmeals to estimate spatially-resolved vertebrate occupancies across the 677 km2 Ailaoshan reserve in Yunnan, China. From 30,468 leeches collected by 163 park rangers across 172 patrol areas, we identify 86 vertebrate species, including amphibians, mammals, birds and squamates. Multi-species occupancy modelling shows that species richness increases with elevation and distance to reserve edge. Most large mammals (e.g. sambar, black bear, serow, tufted deer) follow this pattern; the exceptions are the three domestic mammal species (cows, sheep, goats) and muntjak deer, which are more common at lower elevations. Vertebrate occupancies are a direct measure of conservation outcomes that can help guide protected-area management and improve the contributions that protected areas make towards global biodiversity goals. Here, we show the feasibility of using invertebrate-derived DNA to estimate spatially-resolved vertebrate occupancies across entire protected areas.

These are the input fastq files to our metabarcoding processing pipeline. 

The raw samples are tubes of leeches collected from a subtropical forest in southwestern China, preserved in the equivalent of RNALater solution. Each sample replicate is a small ziploc baggie of tubes with leeches, collected by one ranger in one site along a stretch of trail. 

Excerpt from methods section:  We extracted DNA from each replicate, and then PCR-amplified two mitochondrial markers: one from the 16S rRNA (MT-RNR2) gene, and the other from the 12S rRNA (MT-RNR1) gene. We hereafter refer to these two markers as LSU (16S) and SSU (12S), respectively, referring to the ribosomal large subunit and small subunit that these genes code for. The LSU primers are designed to target mammals, and the SSU primers to amplify all vertebrates. A third primer pair targeting the standard cytochrome c oxidase I marker [36] was tested but not adopted in this study as it co-amplified leech DNA and consequently returned few vertebrate reads. Primers were ordered with sample-identifying tag sequences, and we used a fully-redundant twin-tagging strategy to identify and remove ‘tag jumping’ errors [60] using the DAMe protocol [81]. From our 893 replicate tubes, we successfully PCR-amplified in triplicate 661 samples using our LSU primers and 745 samples using our SSU primers. Successful amplifications were sent to Novogene (Beijing, China) for PCR-free library construction and 150 bp paired end sequencing on an Illumina HiSeq X Ten.

We have three pipelines, reflecting the successive contributions of YJ, DWY, and CCMB, respectively, to the three stages of the analysis

(1) Pipeline for processing raw Illumina HiSeq/MiSeq files https://github.com/jiyinqiu/ailaoshan_leeches_method_code

https://zenodo.org/badge/latestdoi/238595182

(2) Bioinformatic scripts for processing pipeline output, including taxonomic reference datasets https://github.com/dougwyu/screenforbio-mbc-ailaoshan/releases/tag/1.3

https://doi.org/10.5281/zenodo.6971096

(3) Code for all statistical analysis and figures https://github.com/bakerccm/leeches2018-public

https://doi.org/10.5281/zenodo.5914708

For a list of software needed, please refer to pipeline 1:  https://zenodo.org/badge/latestdoi/238595182