Philippa Kaur

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CORRESPONDENCE
05 April 2022

Regreening: green is not always gold

Michael C. Orr

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Alice C. Hughes

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Michael C. Orr

Institute of Zoology, Chinese Academy of Sciences, Beijing, China.

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Alice C. Hughes

University of Hong Kong, Hong Kong, China.

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As the upcoming United Nations Biodiversity Conference in Kunming, China, ushers in the UN decade of ecosystem restoration, regreening efforts are sprouting worldwide. Adding vegetation — expedited by new technologies such as EcoFit, which predicts what trees will thrive in a given environment — can salvage highly disturbed habitats, benefiting native species and offsetting climate change. But when aimed at halting desertification, regreening can have a devastating cost for native ecosystems.

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Nature 604, 40 (2022)
doi: https://doi.org/10.1038/d41586-022-00944-4

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The DarkCideS database was initially conceptualised and developed by KCT, JAG, and ACH as part of the “Global Bat Cave Vulnerability and Conservation Mapping Initiative” in 2014, and later with the “Mapping Karst Biodiversity in Yunnan” and the “Southeast Asian Atlas of Biodiversity” projects. The initiative includes developing tools and methods (e.g., the Bat Cave Vulnerability Index14) and synthesis (e.g., the global bat cave vulnerability assessment11) to identify conservation priorities and important bat caves in the tropics. Since 2019, the initiative has expanded and potential collaborators and contributors were invited through scientific conferences (Association for Tropical Biology and Conservation 2018, International Bat Research Conference 2019), social media platforms, and personal correspondences. At present, the database has 36 collaborators from twenty countries on six continents with expertise and research interests in bat conservation. Four main datasets for all known cave-dwelling bats were built for the DarkCideS database version 1.0.Datasets and compilation for species checklistThe first dataset contains taxonomic checklists for all extant cave-dwelling bats species extracted from the expert-based International Union for the Conservation Union (IUCN) Red List database version 2020-1 (Table 1). We screened and included all bat species that were reported to use, roost in, or aggregate in “Caves”, “Underground”, and “Karsts” habitats in any part of their life histories. We also scanned major publicly available bat cave databases from expeditions such as “Bats in China” (http://www.bio.bris.ac.uk/research/bats/China/) and UNEP-EUROBATS (https://www.eurobats.org/) for European bats24 for additional information and datasets. In addition, the first dataset contains species ecological traits, distribution range, and threatening processes (Table 1).Table 1 DarkCideS 1.0 includes key traits for all living cave-dwelling bat species (N = 679). General metadata for traits included in the current version of the database: habitat preference, ecological status, feeding groups, geographical range, island endemism, geopolitical endemism, distribution range, biogeographical breadth, generation length, body mass, and threatening process.Full size tableInformation per species was pooled from the IUCN Red List versions 2020-125. Species taxonomy was then curated and updated (e.g., synonyms or merged species) using the nomenclature from Simmons and Cirranello12. The “checklist for global cave-dwelling bats” derived from the IUCN Red List includes 679 species. Meanwhile, the DarkCideS 1.0 dataset contains occurrence data for 402 species from 16 families representing 59% of all cave-dwelling species11 (Fig. 2). We found a marginally significant relationship between the species richness and proportion of threatened species between the IUCN-based global cave-dwelling bat and DarkCideS datasets (Kendall’s τ b = 0.60, P = 0.07). The highest completeness of sampled species is in the Neotropics (67.38%) and Indomalayan region (66.08%), and the greatest gaps are in Austral-Oceania (40.28%). Highest endemism was recorded in Austral-Oceania (58.62%) (χ2 = 227.32, df = 5, P  More

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Study areaThe study area covers the total land area of Thailand. Where it is useful, we divided Thailand into six regions (Fig. 2a), the names and boundaries of which are widely used, although they have no official administrative status. We focused on the elements of Thailand’s protected area system that were concerned principally with the in-situ conservation of biodiversity: existing and proposed National Parks, Wildlife Sanctuaries, Non-hunting Areas, and Forest Parks, covering 111, 201 km2 or 21.7% of the country’s land area37 (Fig. 1).Environmental dataA set of environmental variables that were expected to be directly or indirectly related to species distributions in Thailand was used to model suitable habitat in the present and future (Supplementary Material Table S1). These variables were chosen to encompass ecologically relevant variables and enable consistent comparison between species, regardless of species-specific preferences. GIS layers for the whole of the study area were compiled using a variety of data sources at 1-km2 resolution. For variables originally at higher than 1-km resolutions, we used the plus function in ArcMap to combine them with a mask of the study area to use the mask dimensions for all cells.The physical variables, altitude, slope, aspect, and soil pH are widely used in species distribution modeling. Slope and aspect have biologically significant impacts on both temperature and rainfall at these latitudes8 and are particularly important at the poleward margins of species ranges where species may be confined to one aspect. Slope also affects soil maturity and depth. Soil pH is a consistently measured soil variable that broadly correlates with fertility in tropical soils8. Additional soil variables, particularly soil phosphorus, have been shown to be important filters of plant species distributions in the tropics38, but they are not available for Thailand with a useful accuracy and spatial resolution. Altitudes were downloaded from the CGIAR-Consortium for Spatial Information, CGIAR-CSI version 4.1. Slope and aspect were generated by using surface tools in ArcGIS. Soil pH was extracted from ISRIC-World Soil Information version 2.0.Unlike the temperate zone, where tolerances of winter cold and requirements for summer warmth dominate plant and animal distributions, our understanding of how tropical climates filter species distributions is still weak38,39. In Thailand, as in most of the tropics, there are two major climatic gradients which correlate with changes in species composition: a rainfall gradient in the lowlands, along which total rainfall declines and the length of the dry season increases, and a gradient of steadily declining temperature with elevation7. There is no simple relationship between elevation, and thus temperature, and rainfall. An additional complication is that temperature seasonality may be significant in northern Thailand (north of c.18° N), where cooler winters reduce dry-season water stress and extreme low temperatures at high altitudes may exceed physiological tolerances. We therefore chose 8 bioclimatic variables (Supplementary Material Table S1) related to precipitation and temperature, and their seasonality, all of which have previously been used in species distribution modelling in this region9,40. These are available at a resolution of 30 arc sec (approximately 1 km at the equator) from WorldClim ver. 1.4 based on averages of 1970–1990. These variables are available from the same source (and downscaled using the same methods) for the future climate projections.Vegetation structure is an additional major influence on plant and animal distributions in the tropics, both in intact natural vegetation38,39 and when the original vegetation has been degraded or cleared8. Vegetation structure was represented through the inclusion of two continuous variables, percentage forest cover and tree density, as most of the modelled species are known to be sensitive to both the presence of forest and the degree of intactness of the tree cover9. Mean tree density per km2 was extracted from Crowther et al.41 version 2 and percentage coverage of forest per km2 was extracted from the European Space Agency (ESA) GlobCover Version 2.3.Note that the mechanistic basis of the correlations between all these variables and the current distributions of tropical plants and animals are rarely known. Temperature has a direct physiological impact on all organisms, and water supply may be seasonally limiting for plants and some amphibians, but indirect links through biotic interactions are expected to be more important in the tropics, including pest pressure on plants38 and food supply for animals39. Competition is probably also important in shaping local species assemblies. For future projections, we assumed that temperature and precipitation were changing, and that other variables (topography, soil, and vegetation) were stable, so our analysis represents the impacts of climate alone. For 2070, we used the same variables projected by three CMIP5 Earth System Models, CNRM-CM5, GFDL-CM3 and HadGEM2-ES, which have been previously used in Southeast Asia9,42 and in Thailand7. We used two Representative Concentration Pathways, RCP2.6 and RCP8.5, representing low and high greenhouse-gas concentration scenarios, respectively, and thus the potential range of radiative forcing by the end of the century43. RCP2.6 is consistent with meeting the Paris Agreement’s 2 °C global warming target.Species occurrence dataMany locality records for vertebrates were supplied by the Department of National Parks, Wildlife and Plant Conservation (DNP). Trained DNP staff walked along trails throughout the protected areas in Thailand during 2017–2018. They recorded 271,695 locations for 70 mammal species, 18 locations for 3 amphibian species, 318 locations for 18 reptile species, and 43,057 locations for 65 bird species44. We supplemented this with data downloaded from the Global Biodiversity Information Facility (GBIF, https://www.gbif.org/) for 1960–2019 for amphibians (2063 localities for 86 species)45, reptiles (1722 localities from 196 species)46, mammals (2508 localities from 191 species)47, and birds (1,559,222 localities from 884 species)48. More than 95% of the bird records from GBIF were identified as coming from eBird49, which is popular among birders in Thailand. For plants, we used occurrence data from the DNP’s forest resource inventory project from 221 plots, including 24,605 localities for 363 species, the DNP’s Forest Herbarium, including 227 localities for 141 species, and locations for 12 rare and endangered forest species collected from all over Thailand. We also downloaded data from the Botanical Information and Ecology Network (BIEN, https://bien.nceas.ucsb.edu/bien/), including 7209 localities for 1422 species.We removed suspect records (coordinate issues, name problems, etc.), duplicates from the same locality (i.e., more than one individual of the same species recorded in a cell), and species with  0.5 as adequate, but since only five SDMs out of the 1457 generated in this study had values lower than this (0.3–0.5), we retained all the models.Assessment of climate change impactsThe estimated current distribution for each species from Maxent was used as the baseline for comparison with projected distributions of suitable habitat for these species by 2070, under the two emission scenarios and three ESMs, and with and without unlimited dispersal into newly available habitat. We then assessed the impacts of climate change, both on the spatial distribution of individual species and on the pattern of species richness. To generate a species richness map, the binary habitat suitability maps for all species were stacked to produce a consolidated map, which showed the number of species for each 1 km grid cell, and then classified them into five classes (lowest, low, moderate, high, and highest), using the mean ± standard deviation as a break class40.Current and future maps were then compared for each species to calculate the change in species richness, and contingency tables were generated containing the numbers of cells (each of 1 km2) in each richness class. Suitable habitat areas were calculated for the current climate and projected for the future climate. For each species we estimated gained habitat as the areas that will become suitable for a species in future under that scenario, lost habitat as the areas currently predicted as suitable now but projected to become unsuitable under future climatic change, and stable habitat as the areas predicted as suitable now which will remain suitable into the future.We then assessed the vulnerability of each species by estimating the projected change in its range over the next 50 years and using a criteria-based approach, which combined the mean of the suitable habitat area (interpreted as equivalent to extent of occurrence) in the three models and a simplified version of the IUCN Red List criteria51. For 2070, we modified criterion A3(c) as follows; Extinct (Ex) species are projected to lose 100% of suitable habitat by 2070, Critically Endangered (CR) species are projected to lose over 80%, Endangered (EN) species are projected to lose 50–80%; Vulnerable (VU) species are projected to lose 30–50%, Near Threatened (NT) species are projected to lose  More

Collection of wolf hair samplesHair samples were collected by researchers from opportunistically found-dead wolves upon standard necropsy (all the Alpine and part of the Iberian samples) or in the field (all the Dinaric-Balkan and most of the Iberian samples), or from legally harvested wolves (only in the Scandinavian population). At the time of sample collection, wolves were legally harvested in Sweden, Slovenia, and Spain, and under total protection in Portugal and Italy. Hair samples were collected from four body regions, when possible: lumbar (n = 133), dorsal cervical (n = 66), tail (n = 33) and ventral thorax (n = 27) (Tables S1 and S2). The hair was cut as close as possible to the skin with scissors to avoid collecting hair follicles, but in some samples, hairs were pulled from the carcass. Samples were stored at room temperature in paper envelopes. Age, sex, date, and cause of death/capture, geographical location, body mass, and total length were obtained for most of the wolves.Age was estimated by the dental eruption and wear or cementum age analysis and classified as ‘juveniles’ ( 2 years)40, or ‘unknown’. Sex was assessed by inspection of genitalia. Causes of death were classified as ‘acute’, likely lasting minutes to hours (vehicle accident and legal or illegal shooting); ‘subacute’, likely lasting hours to days (drowning, poisoning, trapping and intraspecific aggression); ‘chronic’, likely lasting several weeks (infectious diseases—canine distemper, canine parvovirosis, leptospirosis; sarcoptic mange; or neoplastic diseases) or ‘unknown’. Total length was obtained by measuring with metric tape (1 mm precision) the distance from snout to the distal end of the last tail vertebrae. The body mass was measured with 100 g precision with scales.The detailed protocol for the handling of wolves live trapped in the scope of ecological and conservation studies (n = 7, all from the Iberian population) has been previously described5. Traps were monitored twice every day, in the early morning and late afternoon, hence the duration of restraint after capture was unknown for 8 wolves, potentially up to 12 h. Trap-alarms were deployed in the capture of 2 wolves, with 41 and 70 min intervals between activation of the alarm and administration of the drugs. Live trapping was conducted under permits issued by the nature conservation authorities of Portugal (Instituto de Conservação da Natureza e das Florestas: 338/2007/CAPT, 258/2008/CAPT, 286/2008/CAPT, 260/2009/CAPT, 332/2010/MANU, 333/2010/CAPT, 336/2010/MANU, 26/2012/MANU, and 72/2014/CAPT) and Spain (Dirección Xeral de Conservación da Naturaleza, Xunta de Galicia: E-0020/13-PNPE, 095/2013; Consejería de Medio Ambiente, Principado de Asturias: 31/08/2017-BOPA 05/09/17) and according to European Union directives on the protection of animals used for scientific purposes (Directive 2010/63/EU) and international wildlife standards41,42. The study was undertaken in compliance with the ARRIVE guidelines43.Cortisol extractionThe protocol for the extraction of cortisol from the hair was adapted from previously described procedures15,27. Forty mg of guard hairs were separated from the undercoat and placed in 15 ml falcon tubes. Hair follicles were cut whenever found in the sample. For each sample, the length of three intact hairs was recorded. The samples were washed twice with 40 µl of distilled water/mg hair and three times with the same amount of isopropanol. In each washing step, the samples and washing solution were vortexed, the supernatant discarded, and the hair dried using clean paper towels. After the final wash, samples were dried overnight at room temperature and 30 mg of hair cut into a 2 ml polypropylene screw cap plastic tube with five 4 mm steel beads added to each tube.The hair was ground to a fine powder in a FastPrep sample homogenizer (MP Biomedicals, USA) for four times 1 min at 6.0 m/s. 50 µl methanol/mg hair were added to each sample and sonicated for 30 min at 50 Hz at 50 °C. The samples were incubated for 18 h at 50 °C in an orbital shaker at 160 rpm, centrifuged for 15 min at 14,000g at 20 °C, and 1000 µl of supernatant was collected to a screw cap glass chromatography vial and dried at room temperature in a gentle stream of nitrogen gas. Due to restrictions on laboratory use during the SARS-Cov-2 pandemic, some batches of samples were instead evaporated overnight on a suction hood. This unexpected change in the methanol evaporation protocol was recorded and accounted for in the statistical analysis.Cortisol quantificationA commercial competitive ELISA kit (Cortisol free in Saliva ELISA, Demeditec, Germany) was used to quantify the concentration of cortisol, following the manufacturer’s instructions. The kit plate wells are provided coated with polyclonal rabbit antibody against cortisol, and cortisol-horseradish peroxidase was used as conjugate. According to the manufacturer, the cross-reactivity of the test to selected steroids is low (Table S3), the intra-assay variation is 3.8–5.8% and the inter-assay variation is 6.2–6.4%. Samples, standards, and controls were tested in duplicate.The 4-parameter standard curve was calculated from the log-transformed cortisol concentration of the standard solutions and their measured OD45044. Standard curves were estimated using the software GraphPad Prism 6.04 (GraphPad Software, La Jolla, California USA), and yielded an average R2adjusted = 0.991 (range 0.968–0.999). The cortisol concentration of the reconstituted samples was estimated from the standard curve and converted to cortisol concentration as picograms (pg) of cortisol/mg of guard hair.Intra and inter-assay coefficients of variation were estimated for six ELISA assays of 37–40 samples each. The low and high controls included in the kit were used to estimate the inter-assay coefficient of variation and the duplicate runs of each sample were used to estimate the intra-assay coefficient of variation. Linearity was assessed by two-fold dilutions (1:1, 1:2, 1:4 and 1:8) of 4 extracted samples, comparing the expected and observed concentrations. Recovery was assessed by spiking 6 ground hair samples with known concentrations of cortisol (50, 25, 12.5, 6.25 pg/mg, and no spiking), comparing the expected and observed concentrations.The intra-assay coefficient of variation of the ELISA assays ranged from 6.50 to 9.97% (average 7.66%). The inter-assay coefficient of variation was 11.54% for the low concentration controls and 9.08% for the high concentration controls (average 10.31%). Assay linearity was 91% for the 1:2 dilution, 103% for 1:4, and 117% for 1:8 (average 103%). The recovery of cortisol averaged 94%, being 73% for the 50 pg/mg spiked samples, 74% for 25 pg/mg, 95% for 12.5 pg/mg, and 113% for 6.25 pg/mg.Determinants of hair cortisol concentrationThe potential determinants of HCC investigated included wolf intrinsic variables: sex, age, body condition, body structural size, month of death/capture, and wolf population. The scaled mass index was selected as a measure of body condition45 and estimated from the log-transformed body weight (g) and total length (mm). Log-transformed total length was used as an indicator of body structural size46. Samples were assigned to the Iberian, Alpine, Dinaric-Balkan, or Scandinavian wolf populations16 from the geographical location of the death or live-trapping sites (Fig. 1).The relationship between HCC and additional variables related to the sampling procedure or to the work conducted in the laboratory (length of hair used for cortisol extraction, sample storage time, body region, cause of death/capture, and methanol evaporation protocol), herein referred to as methodological variables, was also investigated as potential confounding variables. Sample storage time was the period in months between death/capture and cortisol extraction. In those samples for which only the year of death was available, 30 June was assigned as the date of death, solely to estimate storage time. All continuous variables were standardized to their z-scores.Statistical analysisFirst, the effect of body region was investigated by a linear mixed model with HCC as the dependent variable, and the independent variables body region, as a categorical fixed effect, and individual wolf, as a random effect. The lumbar region was set as the reference class as it was the most represented in our sample (Table S1). Data from 27 wolves for which samples were available from all 4 body regions were used in this analysis. Four outliers in the dataset violated the assumption of normality in the residuals of the model comparing HCC across body regions (Fig. S1A) and were excluded from this model’s dataset (Fig. S1B).Second, the effect of intrinsic and methodological variables on HCC from the lumbar body region was investigated by another linear mixed model with sex, age, body condition, body structural size (standardized log-transformed total length), cause of death/capture, wolf population, hair length, sample storage time, and methanol evaporation protocol as fixed effect independent variables. The month of death/capture was included as a random effect. Reference classes for the categorical variables were set as female, adult, acute death, Iberian population, and methanol evaporation by nitrogen gas stream. Two outliers in the dataset violated the assumption of normality in the residuals of the model (Full model, Table S4) and were excluded from this analysis (Fig. S1C,D).The goal of this analysis was to assess the relationship between HCC and wolf intrinsic variables, controlling for the potential confounding effect of the methodological variables. Starting from the full model (Table S4), models including all possible combinations of variables were ranked by their AICc using the package “MuMIn”47 in R 3.6.148. The most supported model was selected for inference and models with ΔAICc  More