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Study sitesThe sample site Tei Tong Tsai is located within the Island District (112°5’ E, 22°5’ N Hong Kong, China) and connected to Lantau Country Park. The rich woods in Tei Tong Tsai provide a suitable environment for insects to survive, with rich biodiversity. Weather records (Supplement 1) for May 2019 show that the highefst temperature was 27.2 °C, the lowest was 15.7 °C, the average was 21.7 °C; and the annual average rainfall was 297.8 mm. The suitable temperature and rainfall have created a suitable ecological environment and high biodiversity, establishing Tei Tong Tsai as a prime location for studying beetle diversity. In May 2019, a 13 sample sites were selected for beetle collection (Fig. 1). All latitude and longitude formats were converted to degrees, minutes, and seconds.Fig. 1Sampling points for the three passive acquisition methods used in the Tei Tong Tsai sampling site (indicated by red dots).Full size imageExperimental protocolIn this study, three passive collection methods were used for beetle collection. FIT is an efficient collecting method for insects with strong flying abilities and was first developed and used abroad14. MT and PT collect insects that are not strong flyers and live on the surface. A flight interception trap, a malaise trap, and 10 pitfall traps were set up to collect beetles in each sample site. Samples were selected to cover ecological environments at different longitudes, latitudes, altitudes, and distances from water sources. Reasonable sampling distances (depending on the terrain, with an interval between 100 and 200 m) were set up between sample sites to fully cover Tei Tong Tsai’s habitats. Due to the topography, the distance between the 10th and 11th sample points was about 350 m. The distance between two other close sample points were in the range of 100–200 m. All three traps were based on the original device to maximize the advantages and achieve better collection results.Collection devices. The flight interception trap (Fig. 2a) mainly comprises an interceptor screen (plastic net, PVC plastic glass, or plexiglas) and an insect specimen receiver (PVC), which is an efficient collection device for intercepting and collecting insects with strong flight ability. The detailed installation steps include the following: Firstly, punch two holes on the long side of the PVC screen with a hole puncher spaced about 30 cm apart; then, fix the screen to a bamboo pole with silk, install the specimen receiver, fix all three, bolt the rope, and fix it in the air with a thick rope (the sink is about 0.5–1 m from the ground). After installation, relevant drugs were placed inside the specimen receiver to poison the insects. The drugs used depend on the purpose of the study. For morphological studies, saline (5 mmol/L NaCl solution) or water with detergent is used. By contrast, DNA molecular studies use a mixture of 2% SDS (sodium dodecyl sulfate) and EDTA (ethylene diamine tetraacetic acid, 0.1 mol/L, PH = 8) or highly concentrated alcohol, which effectively controls the degradation of DNA. Currently, high-concentration alcohol, SDS and EDTA mixtures are commonly used. The device is widely applicable and can be installed in almost any habitat; however, it is best installed along the insects’ flight paths, including roads, rivers, or creeks between valleys. In this experiment, we improved this device by increasing the size of the water trough considering the actual situation of the sample site. Also, to properly conduct the molecular experiments, the reagents we used were a mixture of SDS and EDTA. Therefore, the improved device was more suitable for diverse habitats, and the insect species collected were abundant, reflecting good collection practices14.Fig. 2Three passive acquisition methods: (a) flight interception trap; (b) malaise trap; (c) pitfall trap.Full size imageMalaise traps (Fig. 2b) are large tent-like structures constructed from thin mesh. They are among the most commonly used static non-attractant insect traps and insect collection devices. Invented by Malaise (1937) and later improved upon by Townes and Sharkey, these traps are important tools for insect collection and monitoring worldwide15. The malaise trap used at the Tei Tong Tsai Country Park was the Townes type, which is generally set up in forest areas with rich habitats and relatively stable ground. The material is usually meshed mosquito netting fabricated into a tent-shaped insect interception field. The insects hit the net vertically, continue to fly upward, and are gradually led into the trap by the tilted top. The drug in the trap is usually anhydrous ethanol, which intercepts beetles with weak flying abilities16,17.The pitfall trap (Fig. 2c) is an effective method for capturing surface beetles; it is simple to use, easy to carry, and a common device for collection in the wild. The PT is created by digging a pit into the ground with the same depth as a wide-mouth plastic cup (20 cm high, 10 cm in diameter); The upper edge of the cup must be flushed with the soil surface, and a mixture of absolute ethanol is poured inside to collect flightless beetles14. About one-quarter of the way from the top, small holes are punched above the wide-mouth cup to prevent the loss of specimens from rainwater filling the cups. The 10 sets of traps in this experiment were not evenly distributed, but they were all in suitable habitats.Specimen samplingThe sampling site for this study was Tei Tong Tsai, and the sampling period was from 1st May to 28th May (2019). FIT, and PTs were collected once every two days. Due to the small number of beetles collected by MT, mt was collected only once. All beetles were picked out and arranged separately after collection, added to anhydrous ethanol, preserved, and labeled. The beetles collected by the three passive acquisition methods were picked according to morphological species.Specimen identificationThe taxonomic status for the family level of all samples was determined based on the relevant literature18,19,20,21. Relevant experts completed further identification (Supplement 2).All the specimens collected in this study are currently in the zoological museum of the Institute of Zoology, Chinese Academy of Sciences (Beijing, China).Specimen photographyBeetles were poured from the bottle and arranged separately according to the general species. Firstly, we used tweezers or a brush to place the beetles on unbreakable and unwrinkled paper (as far as possible with the backside upwards to keep them tight and neat, reducing the space left, and considering the label in the photograph). Simultaneously, we captured multiple photos according to the size and species of insect for the large specimens in the tube, adjusted the light near them to brighten the background, placed graph paper next to the beetles as a reference scale, then adjusted our Olympus camera settings to the appropriate photographing parameters. Finally, we inserted the photographed beetles and matching labels back into the tube and added anhydrous ethanol for preservation (Fig. 3). The labels were set in the photos as 2019 DTZ-FIT/MT/PTX-5XX-5XX (-N), in which 2019 represents the collection time, DTZ represents Tei Tong Tsai, FIT/MT/PT signifies the collection method, X represents the number of sampling points, 5XX-5XX represents sampling time, and N represents the photo number. If a sample site had many insects on the same date and required more than one photo, n was used to represent the number of photos. See the Supplement 3 for the complete document.Fig. 3Examples of beetles collected from three passive acquisition methods: overall photos of beetles collected by (a) FIT, (b) PT, and (c) MT. On the bottom right corner shows scale in each photo.Full size imageAfter the morphological data of the samples were collected, their Latin name and collection information were recorded in a table. Each passive acquisition method corresponded to a table, and each table was divided into 13 sheets according to 13 sampling points. The collection time was listed horizontally on each sheet, and the beetles’ species names were listed vertically (were named in the morphological species order as 1, 2, 3, …, N). The number of beetles was recorded in the corresponding position and the Supplement 4 file.Finally, data show the beetles’ biodiversity collected from each sampling site. Firstly, we summarized the data from each sampling point after completing the data statistics. Afterward, we counted the number of beetle individuals collected under the different passive acquisition methods at different points (Fig. 4). In Fig. 4, red, blue, and green represent the number of beetle individuals collected by MT, PT, and FIT, respectively. Fig. 4 shows that MT collected fewer beetles than FIT and PT. Secondly, the data of 13 sampling points in each collecting method were summarized to obtain the total number of families and species collected by each method (Fig. 5). A graph created in Excel 2016 displays the collection method as the horizontal coordinate and the number as the vertical coordinate. In the graph, red represents the number of families, and blue represents the number of species. Fig. 5 shows that FIT collected more beetle species and individuals than PT and MT, and MT collected the least. Thirdly, all data from the 13 sampling points and the three collection methods were summarized. The number of species collected in all families was counted. Families with more than ten species were selected (a total of 11 families) for data presentation (Fig. 6). Finally, a graphic was drawn in Excel 2016. Fig. 6 shows that the number of species in Staphylinidae, Curculionidae, and Chrysomelidae accounted for a large number, and the diversity was relatively high.Fig. 4Data table of numbers of individual beetles collected by different methods at 13 sampling points. The red, blue, and green columns represent the number of beetles collected by MT, PT, and FIT, respectively.Full size imageFig. 5The number of beetles collected by different passive acquisition methods. Horizontal coordinates represent collection methods. The red column and blue column represent the number of beetles collected on the family level and species level, respectively.Full size imageFig. 6Families with more than ten species (a total of 11 families) were selected for presentation. The sample sizes of each groups were also shown.Full size image More

The difficulties inherent in ranking global threats are due to them being context-dependent, which result from conditions and the nature of the threats themselves differing among locations, habitats, and taxa (Fig. 1). Current high-risk hotspots from habitat loss and overexploitation are primarily located in the tropics, whereas Europe is documented as a threat hotspot for pollution6. On islands, biological invasions mainly threaten biodiversity in the Pacific and Atlantic Oceans, while islands in the Indian Ocean and near the coasts of Asia are mostly threatened by overexploitation and agriculture3. Climate change affects species more at higher latitudes and altitudes because species are constrained by the physical environment (geographic barriers and mountain tops) to follow their optimal isotherms.Fig. 1: Divergence of global threat rankings across different references and international agencies.IPBES, WWF, and IUCN established global rankings of the five threats responsible for the current biodiversity crisis (B: central, yellow panel). However, the relative importance of each threat depends on the taxon, system, species’ characteristics, time, and/or the metric considered, resulting in divergences. Global biodiversity threats are represented by colors and symbols, given in the top panel. This figure encapsulates results combined from different studies detailed in Supplementary Table 1 with their associated references.Full size imageThe relative importance of threats also depends on the taxon considered. At the global scale, vertebrates are primarily threatened by habitat loss, overexploitation, and then biological invasions. But even within the vertebrates rankings differ — birds and mammals are mainly affected by overexploitation, while amphibians have a higher probability of succumbing to habitat loss6. Because of species-specific traits and adaptations, some species are likely to respond differently to global threats even within a clade. Large-bodied vertebrates are more likely to be threatened by overexploitation, whereas small-bodied vertebrates are more prone to habitat loss or pollution (Fig. 1). Threat ranking also depends on the habitat under consideration. Marine mammals are more threatened by overexploitation and pollution than terrestrial mammals for which habitat loss is the primary threat (Fig. 1). On islands, habitat loss is secondary to the pressures of biological invasions in freshwater systems, but the former is more important for terrestrial vertebrates and plants3. Another source of uncertainty is that most studies examining threats are based on well-studied taxa such as terrestrial vertebrates, which only represent a small subset of the tree of life. For instance, only 0.2% of fungi, 1.7% of invertebrates, and 10% of described plants are assessed in the IUCN update of 20197, potentially underestimating the intensity of some threats and biasing conservation priorities for these groups. Similarly, there is a bias of research effort towards regions with high-income countries, while research from low or middle-income countries is generally underrepresented8. This may give the false impression of absence of threats in some regions of the world.Likewise, period-specific global threat ranks are subject to the vagaries of temporal dynamics (Fig. 1). However, distinguishing past, current, and future threats is essential for current or future conservation interventions. Historically, overexploitation caused most of the Pleistocene megafauna extinctions, likely exacerbated by climate change. As agricultural practices intensified, habitat loss played a major role in extinctions. As humans later colonized islands, biological invasions caused the extinction of hundreds of species worldwide3. In contrast, climate change is only predicted to become major in the near future9. In fact, the effects of recent threats might be masked by delayed species’ responses, especially in under-studied regions, resulting in a large extinction debt. For instance, the severity of biological invasions often causes native species to decline rapidly to local extinction, while other threats such as habitat loss might affect species more slowly. In both cases, the eventual extinctions are ultimately if similar magnitude. More

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All research methods included in this study were performed in accordance with the relevant guidelines and regulations, under ZA/LP/81996 research permit, with ethical approval from the Animal Welfare Ethical Review Board (AWERB) at Durham University. The authors confirm the study was carried out in compliance with ARRIVE guidelines.All inter-individual association data was collected between June 2018 and June 2019 on a wild habituated group of Afro-montane chacma baboons in the western Soutpansberg Mountains, South Africa (central coordinates S29.44031°, E23.02217°) (for study site description see2). The study group was habituated circa 2005 and was the focus of intermittent research attention until 2014. The study area experienced long-term anthropogenic activities (local farming, forestry, and residences) prior to 2005, as such, consistent interactions with humans have been ongoing with this population for some time. From 2007 onwards numerous researchers were able to collect expansive datasets on the study group (e.g. Refs.17,18), indicating that habituation was at a typical level found elsewhere (also validated by AA and RH, who had researched chacma baboons elsewhere). From 2014 the group received full day (dawn until dusk) follows 3–4 days a week, with occasional gaps of up to 5 weeks in duration. These gaps did not appear to effect habituation levels, likely due to the presence of other researchers at the field site who always tried to act benignly when encountering the habituated group. The follow schedule was designed so that the study group retained as much of their natural interactions with predators as possible by ensuring the baboons spent significant time without observers who may influence the frequency and nature of predator–prey interactions19.The study site was located in a private nature reserve and the study group was not hunted during observation gaps or engaged in any conflict with humans, other than occasionally being scared (chasing, yelling, throwing stones etc.) from a small plantation by local workers, usually resulting in alarm barks and fleeing responses. However, the study group appeared adept at recognising the differences between researchers and these threats20. The majority of the study group’s home-range typically overlapped with the core area of the Lajuma Research Centre, and as a result, interactions with staff living in the area, unfamiliar researchers, and tourists were frequent. However, the baboons had not engaged in ‘raiding’ residences, threatening humans, or any other potentially negative symptom of habituation before the end of this study.Sampling methodology for proximity associations30-s focal sampling was used to collect proximity associations between all group members (excluding infants). All data was collected between June and December 2018 and January and June 2019; the majority of 2018s data was collected during the wet season, whilst most of 2019s data was collected during the dry season. To account for time of day, each day was split into four time-periods that were seasonally adjusted ensuring each period accounted for 25% of the current day length. A randomly ordered list of individuals was produced for each day, the first individual identified from the top 15 (approx. 20% of group size) individuals on the list was sampled immediately. Individuals could only be sampled once per time period per day, and a maximum of twice total per day. All individuals received at least 14 focal observations per time period (56 total) across the study period (see below for how we handled uneven sampling for some individuals). A video camera was used by AA (the only observer to collect this data) to record all focal observations (Panasonic HC-W580 Camcorder). At the end of the 30-s focal observation the identities of all neighbouring conspecifics within 5 m, 2.5 m, 1 m, and touching the focal animal were recorded (audibly by AA). We chose the end of the focal observation to record this data as this was most likely to reflect the conditions during the focal, i.e., the observer had been in proximity for at least 30 s.Neighbour information was extracted from video footage and entered manually by AA and AW. Data was split into separate years to reflect an observation gap of several weeks and to understand whether there was consistency in the hypothesized effects through time and to reflect underlying differences in environmental conditions during the two study periods; during the dry season fruits and seeds are scarce and day lengths are several hours shorter than in the wet season such that day journey lengths are often shorter in the dry season and animals are much more sedentary which could impact inter-individual spacings. In 2018 each individual was sampled between 28 and 30 times; 28 focals were randomly selected from each individual to make sampling even. For 2019 there were between 25 and 27 focals per individual; 25 samples of each individual were randomly selected. Observations were undertaken at a range of distances. For both years the median end observer distance was 4.5 m; data was thus split into close focal observations of less than or equal to 4.5 m (2018: n = 918, 2019: n = 809), and observations greater than 4.5 m (2018: n = 902 2019: n = 816). See supporting information Table S1 for summary statistics of the observation distances of each individual.We did not make any attempt to record our focal data evenly across the various habitats at our field site (see Supporting information text S1 for complete habitat descriptions) as our previous research indicated there was little difference in general spatial cohesion/inter-individual proximity patterns across these habitats (see Supporting information text S2 and Table S2). As a result, we considered it unlikely that there were fundamental differences in inter-individual association patterns across habitats, or that observers struggled to reliably detect or identify neighbours in dense habitats. We do acknowledge, however, that there will always be an element of bias with such methods, as observations were avoided, aborted, or excluded if visual obstructions (e.g., cliffs, rocks, walls, buildings, very dense vegetation etc.) prohibited accurate assessments; the observations used in the current study are from occasions when these factors were not an issue.During this study the group contained between 85 and 92 individuals. Age-sex class was defined according to secondary sexual characteristics (e.g., testes descending/enlarging, sexual swelling, canine eruption) and changes in pelage throughout juvenile development (see Supporting information text S3 for full descriptions). All 65 non-infant individuals that were present during 2017 (when displacement tolerances were calculated) and still remaining in the group by the end of 2019 were used in this study (4 individuals from the prior FID study were no longer present). There were a high number of births between 2018 and 2019, but none were independent by the time either of our sampling periods begun in 2018 or 2019. There was no immigration of foreign individuals, but two individuals disappeared, both during the 2018 focal sampling period. As a result, we had a very consistent pool of individuals to sample from during this study. We removed all data associated with the two individuals who disappeared as their occurrences as neighbours would have been poorly sampled (due to missing more than half the study) relative to the rest of the group which would have led to statistical biases21.Flight initiation distance procedureIndividual displacement tolerance estimates were previously quantified in our previous research2 using a flight initiation distance (FID) procedure22 that was completed between October 2017 and April 2018, prior and independent to the commencement of proximity association focal sampling in June 2018. Individual baboons were approached by an observer, and the distance at which the animal displaced away from the observer measured (see Supporting information Table S2 for summary statistics). This procedure was repeated 24 times for each individual baboon, with approaches spread evenly across two observers differing in familiarity. At the beginning of each approach we also recorded several behavioural, social, and environmental factors that could have hypothetically influenced an individual’s FID2 including whether the animal was engaged (e.g., digging or grooming) or not engaged (e.g., resting, chewing food, being groomed), habitat type (open/closed: see Supporting text S1), whether the animal was on the ground or sat on a low branch or rock within 50 cm of the ground, the number of conspecifics within 5 m of the focal animal, and whether there had been any external events within the preceding 5 min (e.g., alarm calls, aggressions, encountering another group or predator). During the approach, we also recorded the visual orientation distance (the distance at which the focal animal directed its line of vision towards the head of the approaching observer) and whether one of the focal animal’s neighbours had displaced/fled before the focal animal. Although all but neighbour flee first and external events showed some importance for predicting looking (see Table S4), FID was found to be distinct amongst individuals and repeatable within each individual, evidence that displacement tolerance may be an individual level trait2. Full details of methods, statistical analysis, and results (including comparison to the original model) for this updated model are in Supporting information text S4, with model summary results for the previous and updated models in Tables S3 and S4.The notion of an observer approaching a habituated primate may be considered atypical or likely to result in habituation/sensitization effects or agonistic behaviours being directed towards the approaching observers. However, our previous study2 showed that almost all approaches resulted in the animal passively relocating (98.85%), a very benign response identical to the behaviours of subordinate baboons displacing away from dominant conspecifics. This suggests that in this group, observers may be considered equivalent to a high-level social threat2. Throughout observation periods on habituated animals, observers are likely to approach or displace animals either incidentally or accidentally multiple times throughout the day, especially during lengthy focal observations. As such, the approach methodology is unlikely to represent a stimulus outside of the norm for our study animals. This may explain why displacement responses were so passive and why there was no evidence of habituation or sensitization effects across the group or individually through a range of temporal periods2 or after life-threatening events20. As a result, our situation was possible without risk of causing stress or anxiety in the study subjects, eliciting agonistic behaviours towards observers, or interfering with their prior habituation levels.Statistical analysisInfluence of tolerance and observer distance on inter-individual association patternsQuantifying displacement toleranceTo quantify displacement tolerance towards observers we extracted the individual conditional modes from the updated FID model using the ranef function in brms. Conditional modes are often referred to as Best Linear Unbiased Predictors (BLUPs) and are the difference between the predicted mean population-level response for a given set of treatments (i.e., population-level effects) and the predicted responses for each individual, and therefore infer the extent to which each individual differs from the population mean. The conditional modes and their associated standard deviations can be found in supporting information Table S5.To validate that the conditional modes from the updated model were both representative of the individual’s flight responses and in line with the estimates produced from our previous study2 we performed additional tests. Firstly, we performed a Pearson’s correlation between the conditional modes from the updated model and the conditional modes from the previous article. Individual tolerance estimates were consistent (r(63) = 0.915, p  More

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