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In this study, koalas from two geographically separated populations in SE Qld, at the Moreton Bay site (MB)9 and the Old Hidden Vale site (HV), underwent regular field monitoring and clinical examinations approximately every 6 months (or more frequently if required for health or welfare concerns). Blood samples, ocular conjunctival swabs and a urogenital tract swab were collected during each clinical examination. From these samples, C. pecorum load and genotype, koala MHC immunogenetics and KoRV proviral subtypes were determined. These results were evaluated in the context of clinical records compiled at the time of sample collection, which included chlamydial disease status.
Chlamydial epidemiology at each study site
The overall prevalence of chlamydial infection and disease differed between the study sites
Longitudinal monitoring of 24 HV koalas (over 113 individual sampling points) identified 24 chlamydial infections for strain typing analysis and eight new chlamydial infections for disease progression analysis. This complemented longitudinal monitoring of 148 MB koalas (over 479 individual sampling points)9 that identified 76 chlamydial infections for strain typing analysis and 38 new chlamydial infections for disease progression analysis. Overall, there was a significantly higher prevalence of infection at HV (58%, 14/24) compared to MB (35%, 89/254)26 (Fisher’s exact test p = 0.028) (Table 1), as well as a significantly higher prevalence of disease at HV (58%, 14/24) compared to MB (27%, 75/279) 26 (Fisher’s exact test p = 0.002).
Table 1 A comparison of chlamydial epidemiology between the Moreton Bay site (MB) and the Old Hidden Vale site (HV).
Full size table

Chlamydial disease progression was common at both study sites
A total of eight HV koalas met our study inclusion criteria for disease progression analysis by having a new chlamydial infection detected at the ocular (n = 1) or urogenital tract site (n = 7) by quantitative polymerase chain reaction (qPCR) over a period of 18 months. These koalas had no evidence of chlamydial infection (infection loads below detection limit) or disease (clinical examination within normal limits) at that anatomical site at their previous clinical examination. If disease was detected at their first clinical examination, they were excluded from disease progression analyses only (unless it was their first sampling as an independent offspring, n = 1).
Interestingly, all of the new chlamydial infections at HV (100%, 8/8) progressed to disease, which was not significantly different to the number of new chlamydial infections at MB that progressed to disease (66%, 25/38)9 (Fisher’s exact test p = 0.084) (Supplementary Fig. S1). For six of these new chlamydial infections at HV (one ocular and five urogenital tract), the infection was detected at the same clinical examination as disease. For the other two new chlamydial infections at HV (both urogenital tract), the infection was present at a clinical examination 2.5 months and 4 months before disease was detected.
The urogenital tract infection load dynamics were similar at both study sites
The urogenital tract infection load (C. pecorum genome copies/µL) in both HV and MB9 koalas was significantly higher when infections were detected at the same clinical examination as disease (1,028,000 copies/µL, range 11,400–4,760,000 copies/µL), in comparison to infections that were present for one or more consecutive clinical examinations before disease was detected or infections that did not progress to disease (600 copies/µL, range 49–522,800 copies/µL) (Mann–Whitney U = 3, p = 0.030). Similarly, the urogenital tract infection load in both HV and MB9 koalas was significantly higher when koalas acquired a new chlamydial infection (within the last three months) (1,834,000 copies/µL, range 52,400–4,760,000 copies/µL), compared to koalas who had long-term infections (present for more than three months) (724 copies/µL, range 35–7,142 copies/µL) (Mann–Whitney U = 0, p = 0.010). Interestingly, the urogenital tract infection load was significantly higher at HV (1,028,000 copies/µL, range 11,400–4,760,000 copies/µL) compared to MB (3,824 copies/µL, range 138–1,340,000 copies/µL) when infections were detected at the same clinical examination as disease (Mann–Whitney U = 11, p = 0.003). In contrast, the urogenital tract infection load was not significantly different between the study sites (HV 600 copies/µL, range 49–522,800 copies/µL vs MB 794 copies/µL, range 16–13,900 copies/µL) when infections were present for one or more consecutive clinical examinations before disease was detected or infections did not progress to disease (Mann–Whitney U = 28, p = 0.703).
The prevalence of chlamydial strains, as determined by Multi-Locus Sequence Typing, differed between the study sites
Overall, 69 C. pecorum-positive samples, comprised of 45 samples from MB (4 ocular and 41 urogenital tract samples from 25 koalas) and 24 samples from HV (2 ocular and 22 urogenital tract samples from 14 koalas), were analysed using a C. pecorum-specific MLST scheme27. Three sequence types (STs) were detected in this study: ST 69, ST 202 and a novel ST (ST 281). ST 69 and ST 202 were detected at both study sites, however their prevalence at each study site was significantly different (Fig. 1). ST 69 was the most prevalent ST at HV, detected in 63% of total samples (15/24) and in 59% of urogenital tract site samples (13/22). ST 69 was significantly less prevalent at MB, detected in 11% of total samples (5/45) and in 12% of urogenital tract site samples (5/41) (overall and urogenital tract site Fisher’s exact test p  More

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Study areas: biodiversity hotspots
We focused on eight biodiversity hotspots21: those with at least 25% of their extent within the “tropical and subtropical moist broadleaf forests” biome44 and for which we obtained at least 1000 checklists from eBird (after applying the data selection procedure described below): Atlantic Forest, Tropical Andes, Tumbes-Chocó-Magdalena, and Mesoamerica (Americas); Eastern Afromontane (Africa); Western Ghats and Sri Lanka, Indo-Burma and Sundaland (Asia). Within each hotspot, we analysed only areas overlapping the “tropical and subtropical moist broadleaf forests” biome44 (Fig. 1, Supplementary Figs. 1, 4, and 5), assumed to have been originally forested (see Supplementary Methods 4d).
Data selection: eBird checklists
We obtained bird sightings from the eBird citizen science database23. The reporting system is based on checklists, whereby the observer provides: list of birds detected; GPS location; sampling effort (whether or not all detected species are reported; sampling duration; sampling protocol, e.g., stationary point, travel, and banding; and distance travelled in case of travelling protocol); starting time of the sampling event; and number of observers.
We used the eBird dataset released in December 201845, focusing on records from 2005 to 2018, as data collected prior to 2005 were too scarce for analysis. We filtered this dataset to obtain high-quality checklists comparable in protocol and effort: we selected complete checklists only (i.e. in which observers explicitly declare having reported all bird species detected and identified); following either the “stationary points” or the “travelling counts” protocol; with durations of continuous observation of 0.5–10 h; with observers travelling distances during the checklist 60% of the 1-km buffer around the point is forested47) versus non-forest ( More