Philippa Kaur

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Broadcasted social calls attracted hoary bats during both the spring and fall migration. Broadcasting conspecific social calls increased hoary bat capture rates at netting sites intentionally removed from normal capture locations. We had very low capture rates during control periods, because we intentionally placed nets in locations removed from flyways to reduce incidental captures. Moreover, capture rates of hoary bats tend to be low even in many locations where they are known to occur24,25, and capture rates of approximately one bat per hour in a single mist net suggest a very strong attraction response to broadcasted calls.Hoary bat activity, as measured by acoustic monitoring was not associated with increased capture rates in response to call broadcasting. However, subsequent research has shown that hoary bats periodically use higher frequency, inconspicuous calls, or do not constantly echolocate during the fall, which may mean acoustic monitoring did not effectively measure hoary bat activity in the vicinity of our trials26,27. We recorded substantially higher acoustic activity during the spring migration, which could represent either more hoary bats and/or bat activity, or a seasonal difference in echolocation or flight behavior such as differences in flight altitude27. It remains unknown if hoary bats use inconspicuous calls or fly in silence during spring migration or other times of year other than the fall when these inconspicuous echolocation behaviors were observed, and seasonally variable behavior could affect detectability or exposure to our playback trials in ways not captured by our acoustic activity covariate. In addition, while we did audibly hear social calls of hoary bats during the fall, we did not record any during fieldwork for this study, which may be an artifact or due to differences in social behavior, context, or number of hoary bats present in the area during our trials.We only captured one female during trials in New Mexico, and were unable to locate any females during the fall migration in coastal regions of California, despite high concentrations of males in the area during what is presumably the mating season. In New Mexico, during spring migration, females migrate through the study area before males28, with very little temporal overlap. As a result, we were unable to determine sex specific responses to call playback, however we have subsequently captured several female hoary bats and Ope’ape’a (Hawaiian hoary bat, L. semotus) using call playback during capture and radio-tracking studies (GAR, pers. obs.).It is difficult to elucidate the meaning of social calls based on the behaviors observed in the field. In bats, social call complexity often reflects social behavior complexity, with a range of uses including but not limited to attracting mates, locating pups within colonies, defending roosting or foraging territory, and attracting bats to roosts10. Attraction to conspecific call broadcasting could indicate positive social interactions (e.g., maintaining group cohesion or investigation) or agonistic behavior (e.g., hoary bats approaching to chase conspecific bats), as has been observed in other bat species29 and in hoary bats during the maternity season30. We did not observe any obvious instances of aggressive hoary bat interactions, and the social calls differ from hisses and clicks that hoary bats use defensively (Fig. 2). We would also audibly hear pairs of hoary bats calling in close proximity to each other, with no indication of aggressive or territorial responses, and these calls being low frequency and audible to humans means that they attenuate at greater distances than hoary bat echolocation calls.Aggressive or territorial interactions in many taxa are often driven by seasonally variable contexts, such as mating, defending food resources, or rearing of young. It may be unlikely that migrating hoary bats would expend energy defending territory during migration when they are utilizing roosts or foraging habitat for such limited periods of time (i.e., a few hours to a day). During active migration birds are often not territorial even when foraging at stopover sites31, and there may be benefits to maintaining group cohesion during migration including navigation and identification of favorable habitat. It is unknown if hoary bats utilize stopover sites for refueling during migration. However the silver-haired bat Lasionycteris noctivagans was found to utilize a migration stopover site in Long Point, Canada, where they opportunistically foraged for short periods of time (1 to 2 days32). Tracking studies would be required to determine temporal patterns of site usage by individual bats to examine stopover behavior.As we had recorded most of our initial social calls during late summer and early fall when hoary bats mate21, we had originally hypothesized that these social calls were associated with mating behavior, which would have been consistent with observations in this study had we found both increased attraction during the fall, and less attraction to calls during the spring. However, social calls attracted hoary bats effectively during both the spring and fall migration. In addition, from acoustic recordings and capture observations in the field, hoary bats produced many social calls during the spring migration when only males were present. There is a possibility, due to our lack of understanding of the mating systems of hoary bats that some mating may continue into the spring. However the majority of taxonomic, physiological, and observational data suggests mating behavior ends by the spring migration19,33, and the majority of females are already pregnant when travelling through New Mexico28. While hoary bats may or may not use social calls as a component of mating behavior, social calls recorded during the spring likely serve purposes not associated with mating.Previous studies describe the hoary bat as solitary throughout most of the year, which would imply only brief social interactions limited to mating or association with offspring, and the many historical accounts of aggregations of hoary bats are thought to be related to mating behavior20,33,34. However the use of, and attraction to, social calls during both spring and fall migration supports that these calls are used for social interactions beyond mating behavior. Further research may determine if hoary bats use these social calls to maintain group cohesion during migration, and what, if any, relationships exist between individual hoary bats that appear to be migrating together. Baerwald and Barclay35 found that geographic and genetic relationships of hoary bats and silver-haired bat carcasses collected at wind turbines were not more closely related than expected by chance, which provides some evidence that groups of migrating hoary bats may not form based on kinship.Many studies hoping to elucidate the causes of fatalities at wind energy facilities have focused only on the fall migration period when bats are most often killed13,20,36. However hoary bats migrate during the spring as well, when they do not suffer high fatality rates. Investigating the spring migration presents a valuable baseline to compare behavioral changes and other factors that may place hoary bats or other impacted species at risk. If social behavior makes a major contribution to the risk of fatalities at wind energy developments, then social behavior should differ between spring and fall migration. We did not find a large difference in response to social calls between seasons. While this represents just an initial study into the social calling behavior of hoary bats during migration, it provides some conclusions to guide subsequent investigations: (1) detecting hoary bat social calls does not necessarily indicate mating behavior, and (2) researchers should be cautious in interpreting evidence of social interactions during the fall at wind energy sites as evidence of mating behavior as in the mating landmarks hypothesis22,37. Because it can separate out mating from other behavioral components, comparing spring and fall migration can benefit the investigation of social and other behaviors in hoary bats and other migratory species. Comparing flight behavior, diet, roost selection, hormonal and physiological changes, and further studies of social interactions including scent and, between the spring and fall migration will allow researchers to elucidate which behaviors change seasonally and which may underlie seasonal patterns of wind turbine fatalities. Additionally, exploring social attraction to audible sounds produced by turbines or other potential signals that could seasonally elicit social attraction could lead to additional insights.Hoary bats have proven challenging to capture and study in many locations across their range24, driven by their solitary tree roosting behavior and as they often fly out of the reach of mist nets or ground-based acoustic monitoring stations36,38. Using call broadcasting to increase capture rates can be a useful research tool, especially in locations where the habitat does not provide any ideal capture locations. Using this technique we have captured hoary bats on coastal sand dunes, in large open fields, and in groves of Eucalyptus trees adjacent to wind energy sites, all of which would normally yield low bat capture success without the use of lures. The ability to capture hoary bats more reliably is a great asset for research and conservation throughout the range of hoary bats.Our study tested the use of social call playback as a methodology to study the social behavior of hoary bats during migration, and the utility of using call playback as a research tool and acoustic lure for hoary bats. Increasing capture rates from conspecific social call playback during mating and non-mating season indicates social interactions during both migratory periods, despite the solitary roosting behavior of this species. Future studies to elucidate the behavioral function of these calls, and response during non-migratory seasons could refine our understanding of social behaviors of this elusive bat species. More

The CAN-SAR22 database was created to provide access to publicly available data on species at risk in Canada in a standardized format that can be used in a wide range of applied research contexts. The variables included in the database were chosen to provide a range of information available for species at risk with a particular focus on climate change to support the first publication using the database6. The database includes numerous data fields including extinction risk status, various biological and geographical attributes, threat assessments, date of listing, recovery actions, and a set of climate change impact and adaptation variables. CAN-SAR is a living database that can be updated as new information and reports become available, or as other targeted data extraction efforts become available23.In Canada, the listing process begins with an assessment of a wildlife species’ risk of extinction by the Committee on the Status of Endangered Wildlife in Canada (COSEWIC). A wildlife species can be either a species or a ‘designatable unit’, which includes subspecies, varieties, or other geographically or genetically distinct populations. Herein these are referred to collectively as ‘species’. COSEWIC is an independent body of experts who synthesize the best available information to date into a status report containing elements such as population size and trends, habitat availability, and threat assessments (Fig. 1)17. This report is then used as the basis for a status recommendation that is passed on to the Government of Canada, who makes the final decision on whether to legally list the species under Schedule 1 of SARA24. The species can be listed as ‘Special concern’, ‘Threatened’, ‘Endangered’, or ‘Extirpated’. If a species is listed as ‘Threatened’, ‘Endangered’ or ‘Extirpated’ then a recovery strategy is required, while for species listed as ‘Special concern’ a management plan must be created24. Recovery strategies must provide a description of the species’ needs, address identified threats, identify critical habitat (where applicable and to the extent possible), and include population and distribution objectives for the species’ recovery. Management plans include conservation measures for the species and its habitat24. Hereafter, we refer to recovery strategies and management plans collectively as ‘recovery documents’.Information included in the database was extracted from various sources and documents that are available from the online SAR Public Registry, including COSEWIC status reports and status appraisal summaries, and recovery documents (Fig. 1). A COSEWIC status appraisal summary is produced instead of a new status report when a species has been previously assessed and COSEWIC experts are confident that its status will not change (https://www.cosewic.ca/index.php/en-ca/assessment-process/status-appraisal-summary-process.html). It is considered an addendum to the existing status report; thus, we use ‘status report’ to refer to either a status report or a status appraisal summary and the previous status report. From the SAR Public Registry website we accessed information from 1146 documents for all 594 species listed under SARA Schedule 1 as of March 23, 2021, that were classified with the status of ‘Special concern’, ‘Threatened’, or ‘Endangered’. Some species have multiple documents of the same type because COSEWIC reassesses at risk species every 10 years or less and recovery strategies and management plans are reviewed every 5 years and updated as needed. As new documents have become available they have been added to the CAN-SAR database without overriding the previously existing document, which allows for tracking of changes in various data fields over time. Only documents between 2018 and 2021, inclusive, have an updated version due to our updating schedule.Data extractionVariables included in the CAN-SAR database were categorised as either directly transcribed or derived. Directly transcribed variables reflect information extracted from documents that require limited interpretation, such as scientific name or date of legal listing (Online-only Table 1). Derived variables reflect species’ attributes that required interpretation of text by data recorders (Online-only Table 1). The data dictionary (CAN-SAR_data_dictionary.xlsx) contains a description of each variable, including details of their extraction and synthesis22.Several derived variables were extracted from the status report technical summary section, including whether the species is endemic to Canada or North America, and whether the species’ range is continuous with the United States. Endemism was determined for each species at two spatial extents, Canada and North America, based on descriptions of their global distributions from status reports. Whether a Canadian species’ range is continuous with its conspecifics in the United States was interpreted from descriptions of geographic isolation in the distribution and rescue effect sections of the status reports.Variables related to species’ threats were derived from information in the status reports, recovery strategies and management plans. In 2012, COSEWIC initiated use of the IUCN threats classification system in status reports for some species; a ‘threats calculator’25. Threats calculators may also be included in recovery strategies and management plans. A threats calculator is a table included in the document that classifies threats into 11 general ‘level one’ classes and, more specific ‘level two’ subclasses (Table 1)26. Four variables (impact, severity, scope, and timing) for each level one and level two threats were scored independently and then combined into an overall impact score for each species. Impact is defined as the degree to which the species is threatened by the threat class; severity is the level of damage to the species from the threat class that is expected within ten years or three generations, whichever is longer; scope is the proportion of the species that is expected to be affected within ten years; and timing is the immediacy of the threat25. Threat-related variables were either transcribed directly from the threats calculator, or from the derived description of threats in the document if a threats calculator was not included.Table 1 Definitions of level one threat classes and names of level two threat classes following Version 1.1 of the IUCN threats classification system.Full size tableFor species where a threats calculator was included, we recorded whether each of the level one and level two threat classes were identified (i.e., considered a threat), and transcribed the scores for each of impact, scope, severity, and timing. Threat classes were considered identified if the impact was negligible, low, moderate, high, very high, unknown, or not calculated (outside assessment timeframe). Impact, scope, severity, and timing values were coded as ranked values of ‘0’: not a threat; ‘1’: neglible; ‘2’: low; ‘3’: moderate; ‘4’: high; ‘5’: very high; ‘-1’: unknown; ‘-2’: not calculated; or ‘NA’ where there were blank values. For exact ranking interpretations see CAN-SAR_data_dictionary22. For some species, the threats calculator was available from the COSEWIC Secretariat as a Microsoft Excel file, in which case threats information was extracted directly from the spreadsheet using R v 3.6.227. For species where a Microsoft Excel file was not available, threats calculator information was manually extracted from the status report.For species where a threats calculator was not included in the document, threats described in the text were classified into threat classes based on version 1.1 of the IUCN threats classification system (Table 1)26. Although a more recent version of the threats calculator exists, we applied version 1.1 classification to reflect the approach applied across the majority of species. Threats were considered identified if the threat was discussed as having any negative or potentially negative impact on the species. In cases where no threat calculator was available, the threat attributes of impact, scope, severity, and timing were scored as not applicable; ‘NA’.Several variables were derived to determine how climate change was addressed in status reports and recovery documents. Whether climate change was mentioned anywhere in the status report was determined by searching the document for the words climat*, warm, temperat*, and drought. If a document contained any of these search terms, we assessed the context for description of anthropogenic climate change impacts. In cases where the terms were not found, the threats section was checked for any other descriptions that were related to climate change; if none were found, climate change was recorded as not mentioned. When climate change was mentioned, we then determined if it was identified as a threat by interpreting whether it was described as having a negative or potentially negative impact on the species. If a threats calculator was included in the status report, climate change was considered a threat if the ‘Climate change and severe weather’ threat class had an impact that was more than negligible or if climate change was described outside the threats calculator as a threat or potential threat. We recorded whether the threat of climate change was unknown. This included instances where climate change was described as having unknown effects on the species, if ‘unknown’ was assigned to impact, scope, severity, or timing in the threats calculator, or if knowledge gaps related to climate change were identified. Finally, the impact of climate change relative to other threats was classified based on descriptions of threats in the status report. The relative impact of climate change was classified as ‘0’ if it was not a threat; ‘1’ if it was described as a minor, potential, possible, or other threat; ‘2’ if it was a significant threat but not the most important or if it was among the list of threats with no indication of relative importance; or ‘3’ if it was among the most important threats described.Additional derived variables extracted from recovery documents available on the SAR Public Registry included those related to critical habitat identification and recovery actions. For species with recovery strategies, we recorded whether critical habitat was described as identified, partially identified, or not identified. In cases where critical habitat was described as “identified to the extent possible”, it was marked as identified. We extracted information from recovery documents on what types of actions were recommended and whether the actions addressed the threat of climate change. Actions were categorized into four categories: outreach and stewardship, research and monitoring, habitat management, and population management (Table 2). Within each of the four categories, a set of 16 sub-types were recorded if any actions of that type were recommended or already completed. We also recorded action types and sub-types that specifically addressed climate change threats if climate change was listed as the threat addressed or the reason the action was necessary6.Table 2 Categories of actions specified in Recovery Strategies.Full size tableFive data recorders conducted the initial data extraction, synthesis, and interpretation. All recorders were trained on the definitions, interpretation, and general process of data extraction to ensure consistent extraction of all variables. Data extraction occurred in multiple stages and included an iterative set of verifications and assessments of the same species among recorders to ensure consistent and standardized interpretations. Once convergence of interpretations was achieved, each recorder was assigned a set of species/reports from which to extract information.Next stepsThe CAN-SAR database is intended to be a living database that can be updated by adding information from new documents or species as they become available, adding more historical documents, or extracting new information from all documents. The current set of species and associated information includes those listed on Schedule 1 of SARA (as of March 23rd 2021) as ‘Special concern’, ‘Threatened’, or ‘Endangered’. Examples of future data additions include integration of data from species assessed by COSEWIC that are not listed under Schedule 1 of SARA, adding fields that specify the criteria used to arrive at a risk status designation, and integration of data from action plans. We anticipate updating the database periodically, as time and resources allow, and we also encourage anyone interested in extending or expanding on the CAN-SAR database to communicate to discuss a collaboration. Integration of new datasets will require screening and validation to ensure adherence to data standards and consistent interpretations. In the longer term, we foresee the implementation of automatic updating of the CAN-SAR database for variables that do not require interpretation by using machine-readable formatted status and recovery documents.ApplicationsApplications of the CAN-SAR database reflect both opportunities to synthesise the data in novel ways and to expand the scope of the current database to include new data fields representing information contained in status assessments and recovery documents. The CAN-SAR database facilitates independent data analysis and synthesis efforts ranging from trend analysis of threats, identifying research and monitoring gaps, and assessing the effectiveness of recovery actions, which target various steps of the listing and recovery process. For example, the database provides a platform to extend existing climate change focused work6 to assess the prevalence of recommended climate change targeted recovery actions, such as translocations. With recent adoption of the ‘Pan-Canadian approach to transforming Species at Risk conservation in Canada’28, which emphasizes multi-species recovery planning approaches, there is an opportunity to assess patterns in key sectors, which include agriculture, forestry, and urban development, over time and by taxa and how they map to threats.With the integration of additional variables through future data extraction or integration efforts, the CAN-SAR database can be used to assess novel questions. For example, broadening recovery action categories to include those that reflect natural climate solutions can highlight where recovery efforts may provide co-benefits, thus achieving biodiversity conservation and climate change mitigation goals29. Specifically, habitat restoration actions for a forest-dependent species primarily threatened by habitat loss may lead to improved recovery outcomes while also resulting in carbon sequestration and improved climate change mitigation efforts. Tracking these types of actions in CAN-SAR could highlight both opportunities and gaps for the integration of climate smart conservation principles30 into species at risk recovery planning and the adoption of climate change adaption measures for species directly considered climate change threatened and those that are not6. More

TRY meta-­analysisWe used the TRY plant trait database27 to identify traits that show systematic differences between the tree and liana growth forms, as a way to narrow the scope of the rest of the analysis. We chose traits to represent major trade­offs within the “economic spectrum” framework, which places plants along a spectrum of strategies from acquisitive, fast return on investment to conservative, slow return on investment according to key functional trait values30. We narrowed traits to those that had observations for at least four tree and liana species. We then compiled our dataset using the following steps during November and December 2019. For each trait, we downloaded the dataset for all species available globally and averaged the observations of the trait to the species level to avoid statistical biases introduced in our growth form comparison due to a high density of observations in a few commercially valuable species. We matched the species ID number with the most frequently used growth form identifier using the TRY “growth form” trait and kept the species with the most frequent identifier of “tree,” “liana,” or “woody vine.” We subsetted the data to keep only species with a majority of observations ascribed to the tree and liana growth forms (i.e., no herbaceous species, ferns, etc.), resulting in observations for 44,222 total species. Finally, we filtered the dataset of 44,222 species by hand to remove species misclassified as trees or lianas; species occurring entirely in temperate to boreal biomes; species from all gymnosperm lineages except the order Gnetales; and entries for taxonomic classifications broader than the genus level (e.g., taxonomic families). We found that hydraulic functional traits in the TRY database (i.e., Ks,max and P50) show systematic differences between growth forms (Supplementary Fig. 1; Supplementary Tables 3 and 4), while there is mixed evidence for differences in the acquisitiveness of trees and lianas in terms of stem anatomical traits (Supplementary Fig. 1; Supplementary Tables 3 and 4) and leaf functional traits (Supplementary Fig. 6; Supplementary Tables 3 and 4), and no evidence of differences between tropical trees and lianas with respect to root functional traits (Supplementary Fig. 7; Supplementary Tables 3 and 4).Extended meta­-analysisWe conducted an additional literature search to supplement the hydraulic trait observations from the TRY database. The additional literature search served two purposes: (1) to fill a major gap identified during our TRY analysis in terms of liana trait observations, and (2) to address the methodological inconsistency of measuring Ks,max and P50 on liana branches shorter than the longest vessel, which incorrectly measures Ks,max and P50 without accounting for end wall resistivity59,60.We conducted a literature search using Web of Science and Google Scholar. We searched the following phrases in combination with “liana:” “hydraulic conductivity,” “hydraulic trait,” “hydraulic efficiency,” and “hydraulic K.” Of the literature we found, we kept only the studies that met the following criteria: (1) reported Ks,max measurements for lianas, (2) measured Ks,max instead of computing Ks,max from xylem conduit dimensions, (3) measured Ks,max on sunlit, terminal branches of mature individuals or saplings, and (4) measured Ks,max on a branch longer than the longest vessel. We considered the authors to have used a branch length longer than maximum vessel length if the authors measured or reported maximum vessel length for the species and a longer branch was used. Because the best methodological practice for measuring P50, especially in species with long vessels, is currently a matter of debate, we additionally removed all observations of P50  > ­0.75. This filtering was performed to reduce the probability that falsely high (i.e., less negative) P50 values were retained in our analysis because of improper measurement technique and is consistent with the P50 filtering performed by Trugman et al.61. Improper measurement technique is a particular concern for lianas, whose wide and long vessels require cautious implementation of the traditional measurement techniques developed for trees. We note that retaining all liana P50 observations (i.e., not filtering out observations  > −0.75) results in a significant difference between trees and lianas (Mann­–Whitney test statistic = 1029, ntree = 61, nliana = 46, p  More

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.This is a summary of: Qiao, L. et al. Soil quality both increases crop production and improves resilience to climate change. Nat. Clim. Change https://doi.org/10.1038/s41558-022-01376-8 (2022). More

cpDNA haplotype databaseDNA sequencing of the choloroplast (cp) markers produced sequences of the following lengths: 573 bp (atpB-rbcL); 487 bp (petG-trnP); 500 bp (trnL1-trnL2); and 593 bp (psbM-trnD). Alignment of the 352 individuals from the 44 populations yielded a total 28 variable sites: 11 in the atpB-rbcL spacer, seven in both the petG-trnP and psbM-trnD spacers, and three in the trnL1-trnL2 spacer (Supplementary Table S1). Based on these 28 variable sites (21 base substitutions and 7 deletions) across the combined intergenic regions, a total of 22 unique haplotypes were found (Fig. 1a).Figure 1(a) Chloroplast haplotype distribution in the Shorea leprosula populations. The pie chart colours indicate haplotype distributions; and sector areas are proportional to sample size (Map was generated by ArcGIS-ArcMap version 10.8). (b) STRUCTURE analysis identified two clusters (K = 2) corresponding to Region A and B.Full size imageSSR allele frequency databaseThe reproducibility of SSR genotyping was confirmed by achieving consistent genotypes from five independent PCR amplifications on a single individual for each of the ten SSR loci. Individual bar plots from STRUCTURE analysis are presented in Fig. 1b. At the highest Delta K likelihood scores, the best representation of the data was K = 2 suggesting that the 44 populations in Peninsular Malaysia can be divided into two main genetic clusters: Region A and Region B. The first cluster, ‘Region A’ consists of 12 populations, namely SBadak, BPerangin, BEnggang, GJerai, RTelui, GInas, GBongsu, Belum, Piah, BHijau, Korbu and Bubu. The second cluster, ‘Region B’ consists of 32 populations, namely Behrang, Ampang, HGombak, HLangat, SLalang, PPanjang, Berembun, Angsi, Kenaboi, Triang, Pasoh, BSenggeh, GLedang, Krau, TNegara, Terenggun, SBetis, USat, CTongkat, HTerengganu, Jengai, AGading, Tekam, Beserah, Jengka, Lentang, Lesong, ERompin, GArong, Labis, AHitam and Panti. Similarly, the UPGMA dendrogram analysis also divided the 44 populations into two genetic clusters (Fig. 2) corresponding to Region A and B of the STRUCTURE result.Figure 2Dendrogram showing the relationship between 44 populations of Shorea leprosula in Peninsular Malaysia based on the UPGMA cluster analysis of SSR markers.Full size imageSSR allele frequency databases were established according to Region A and B, and characterized to evaluate the relative usefulness of each SSR marker in forensic investigation. The distribution of allele frequencies for each locus is listed in Table S2 (Region A database) and Table S3 (Region B database). Forensic parameters are shown in Table 1, with a total of 143 alleles and 174 alleles detected in the Region A and B databases, respectively. The observed (Ho) and expected (He) heterozygosity ranged from 0.3570 to 0.8346 and 0.4375 to 0.8795, respectively for populations in the Region A database; and ranged from 0.3298 to 0.8356 and 0.3469 to 0.8793, respectively for populations in the Region B database. The power of discrimination (PD) for the SSR loci ranged from 0.601 to 0.972 and 0.554 to 0.975, in Region A and B databases, respectively. The most discriminating locus was Sle605 in both the Region A (PD = 0.972) and Region B (PD = 0.975) databases. Minimum allele frequency was adjusted for alleles falling below the thresholds of 0.0066 (Region A) and 0.0024 (Region B).Table 1 Genetic diversity and forensic variables (A: total number of alleles; Ho: observed heterozygosity; He: expected heterozygosity; PIC: polymorphic information content; HWE: Hardy–Weinberg equilibrium; MP: matching probability; PD: power of discrimination) for each the 10 SSR loci of Shorea leprosula in the Region A and B databases.Full size tableDeviations from HWE were detected in four of the SSR loci for Region A (SleT11, SleT15, SleT17 and Sle465) and six SSR loci in Region B (SleT01, SleT11, SleT15, SleT17, SleT29 and SleT31). We evaluated these loci in each population independently to rule out the possible presence of null alleles. There were four populations in Region A (GJerai, RTelui, GBongsu and Piah) where a single one locus deviated from HWE; whereas there were eight populations in Region B (Behrang, HGombak, SLalang, Angsi, Klau, USat, Jengka and Panti) with a single locus and a single population (GLedang) with two loci that deviated from HWE (Table S4). Observed deviation from HWE was substantially lower in each population (either absence or not more than two loci) and thus it might be due to Wahlund effect caused by population substructuring in both Region A and B. Linkage disequilibrium (LD) testing was used to evaluate the independence of frequencies for all the SSR genotypes. A total of 13.3% and 28.9% of the 45 pairwise loci were found significant evidence of LD for Region A and B, respectively. Some of the loci might be linked as a result of population substructuring and inbreeding (inbreeding coefficient = 0.0822 [Peninsular Malaysia]). These results are in line with observations in real populations, where the assumption of completely random mating and zero migration required for HWE and LD are unlikely to be met, either in humans, animals or plants 21,22,23.Mean self-assignment, the proportion of individuals correctly assigned back to their population, was 45.9% and ranged from 14.3% (Kenaboi) to 81.3% (CTongkat) between population (Table 2). At the regional level, correct assignment rate of individuals to their region of origin was higher, 87.4% for Region A and 90.0% for Region B, (average of 88.7%).Table 2 Self-assignment test outcomes for Shorea leprosula individuals at the population and regional levels.Full size tableConservativeness of the databaseThe coancestry coefficient (θ) for Peninsular Malaysia (0.0579) was higher than those of Region A (0.0454) and Region B (0.0500) (Table 3). A total of 4.54% and 5.00% of the genetic variability was distributed among populations within Region A and Region B, respectively. In terms of inbreeding coefficient (f), the value for the Region A database (f = 0.0892) was highest, followed by Peninsular Malaysia (f = 0.0822) and Region B (f = 0.0666). All the θ and f values were significantly greater than zero, demonstrated by the 95% confidence intervals not overlapping with zero. Both of the θ and f values were used to calculate the conservativeness of each database by testing the cognate database (Porigin) against the regional database (Pcombined). The databases were non-conservative at the calculated θ value. In order for both the Region databases (A and B) to be conservative, the value of θ was adjusted from 0.0454 to 0.1900 for Region A and from 0.0500 to 0.1500 for Region B. For the Region A database, the most common SSR profile frequency is 2.69 × 10–7 or 1 in 3.72 million and the rarest profile frequency is 1.84 × 10–14 or 1 in 54.3 trillion. For the Region B database, the most common SSR profile frequency is 1.06 × 10–7 or 1 in 9.43 million and the rarest profile frequency is 4.03 × 10–16 or 1 in 2.48 quadrillion.Table 3 Coancestry (θ) and inbreeding (f) coefficients for Shorea leprosula at each hierarchical level.Full size table More

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