Philippa Kaur

Study sites and data collectionDuring 2017 and 2018, we carried out 14 experiments in rivers located in temperate, tropical, and subarctic biomes to capture a gradient of river productivity and climatic characteristics (Table 1, Fig. 1). Apart from the Mekong and Sekong rivers in Cambodia that were impacted by plantations, rice cultivation, grassland, and urban areas (56% impacted land cover in the Mekong and 38% in the Sekong), the selected rivers were predominantly in pristine areas (impacted land-use ≤ 8%), although two rivers in Mongolia were affected by livestock grazing (with 26% of land cover at the Khovd and 59% in the two Zavkhan rivers).We conducted traditional O2 concentration metabolic assessments, assessments of isotopic fractionation, and 24 h characterization of δ18O2 at each site. We measured changes in dissolved O2 concentrations and temperature every 10 min over at least 24 h with at least one MiniDOT logger (PME, Vista, California, USA). We calibrated for drift using the average measurement values made in 100% saturated water for at least 30 min before and after each deployment to allow adjustment to temperature and placed sensors in the river for at least 30 min prior to using data to allow equilibration to temperature (following methods detailed in ref. 52).We collected δ18O2 samples by hand every 2 h during the same 24-h period of the O2 concentration measurements in pre-evacuated 100 mL vials loaded with 50 µl HgCl2 as a preservative and sealed with septum stoppers (Bellco Glass Inc., Supelco, Vineland NJ). We analyzed samples for δ18O2 at the Nevada Stable Isotope Lab of the University of Nevada, Reno with a Micromass Isoprime (Middlewich, UK) stable isotope ratio mass spectrometer. We followed the method described by ref. 17 and injected 1.0–2.5 mL of headspace gas taken from the serum bottles using a gastight syringe (SGE, Australia) into a Eurovector (Pavia, Italy) elemental analyzer equipped with a septum injector port, and a 1.5 m long molecular sieve gas chromatography column. Water-δ18O was also collected at each site every 2 h and analyses were performed using a Picarro L2130-i cavity ringdown spectrometer at the Nevada Stable Isotope Lab of the University of Nevada, Reno. δ18O2 values are reported in the usual δ notation vs. VSMOW in units of ‰, with an analytical uncertainty of ±0.2‰ for δ18O2, or an analytical uncertainty of ±0.1‰ for water-δ18O.We characterized physical characteristics at each site to provide parameters to estimate whole-system metabolism. We measured conductivity, slope, and flow velocity and depth at ten transects using a flow meter when wadeable or with an Acoustic Doppler Velocimeter (Sontek, Xylem, San Diego, CA) when rivers were not wadeable. At each site, we measured light as photosynthetically active radiation (PAR) every 10 min, using Odyssey PAR loggers (Data Flow Systems, Christchurch, New Zealand) calibrated with a Li-Cor PAR sensor (Lincoln, Nebraska, USA).At each site, we also directly measured biofilm ash-free dry mass (AFDM) from 8 to 12 rocks (53). The material was scrubbed from the rocks, agitated, filtered (Whatman glass microfiber GF/F filters). Rock area was estimated with calibrated pictures processed with the ImageJ processing program (National Institutes of Health and the Laboratory for Optical and Computational Instrumentation LOCI, University of Wisconsin). For AFDM analyses, samples were dried, and weighed before and after combustion.Additionally, we collected data on the percentage of impacted land use in the watershed above each sampling site: for the Mekong and the Sekong we used Landsat satellite imagery from ref. 54, for the US and Mongolian sites land use characteristics were derived from the National Land Cover Database55 and for Patagonia we used the Chilean national land use inventory maps from ref. 56.δ18O2 stable isotope fractionation during respiration in sealed recirculating chambersModels based on oxygen isotopes are sensitive to the oxygen isotope fractionation factor (αR) during respiration used; αR can vary widely among sites and is influenced by temperature and water velocity30. We used in our models the range of αR values measured by30 using sealed Plexiglas recirculating chambers as in ref. 57. These measurements were done at the same time as the 24 h δ18O2 sample collections in the rivers of this study. We placed rocks, sediment, macrophytes (macrophytes dominated in the Zavkhan 1 site) inside the chambers, depending on the site’s dominant substrata (see ref. 30 for more details on chamber measurements). We collected water samples in the chambers for δ18O2 analyses before and after the incubations and the O2 isotope fractionation factor was calculated using Eq. (2).$$delta =(delta i+1000){F}^{left(alpha -1right)}-1000$$
(2)
where δ is the O2 isotopic composition of dissolved oxygen at the end of the dark incubation, δi is the O2 isotopic composition of dissolved oxygen at the beginning of the dark incubation, F the fractional abundance of O2 concentration remaining at the end of the dark incubation, and α is the isotopic fractionation factor during respiration.Ecosystem metabolism O2 single station modelingWe modeled metabolism as a function of GPP, ER, and reaeration with the atmosphere, using the single-station open-channel metabolism method4 using the same approach as15, given in Eq. (3).$${O}_{{2}_{(t)}}={O}_{{2}_{(t-1)}}+left(left(frac{{GPP}}{z}xfrac{{{PPFD}}_{left(t-1right)}}{sum {{PPFD}}_{24h}}right)+frac{{ER}}{z}+{K}_{{O}_{2}}left({O}_{{2}_{{sat}left(t-1right)}}-{O}_{{2}_{left(t-1right)}}right)right)triangle t$$
(3)
where GPP is gross primary production in g O2 m−2 d−1, ER is ecosystem respiration in g O2 m−2 d−1, ({K}_{{O}_{2}}) is the reaeration coefficient (d−1). PPFD is photosynthetic photon flux density (µmol m−2 s−1), z is mean stream depth (m), and ∆t is time increment between logging intervals (d). We used Bayesian inverse modeling approach to estimate the probability distribution of parameters GPP and ER that produce the best model fit between observed and modeled O2 data. We fixed site-specific ({K}_{{O}_{2}}) estimates using K600 (d−1) (normalized beyond gas-specific Schmidt number conversions among gases58) based on prior work characterizing K using BASE59, and converted these prior estimates of K600 to ({K}_{{O}_{2}})using appropriate temperature corrections. We estimated daily GPP and ER from diel O2 data only (Eq. (3)) to be used as prior estimates of daily GPPO2 and ERO2 in the coupled O2 and δ18O2 model (Eqs. (4a) and (4b))15, where the mean and SD of GPP and ER from the O2 _only method were used as prior estimates of GPPO2 and ERO2 in the dual O2 and δ18O2 model described below.Ecosystem metabolism: Diel δ18O2 modelingWe also modeled metabolism using an updated version of the model developed by ref. 15 coupling high-frequency O2 concentration data with δ18O2 collected every 2 h throughout the same 24 h period of the O2 concentration measurements. With this model, daily rates of ecosystem metabolism are derived from diel changes in δ18O2 and O2, where values of δ18O2 are converted to g 18O m−3 (18O2 in Eq. 4b) and modeled as a function of water isotope values, isotope fractionation, reaeration with the atmosphere, ER, and GPP. As with Eq. 3, the ratio of light at the previous logging time (({{PPFD}}_{left(t-1right)})) relative to the sum of light over 24 h (({sum {PPFD}}_{24h})) is used to characterize times when GPP is zero and only ER is taking place (Eqs. (4a) and (4b)):$${O}_{{2}_{left(tright)}}= , {O}_{{2}_{left(t-1right)}}+left(frac{{{GPP}}_{O2}}{z}xfrac{{{PPFD}}_{left(t-1right)}}{sum {{PPFD}}_{24h}}right)+left(frac{{{ER}}_{O2},xtriangle t}{z}right)\ +left({K}_{{O}_{2}}xleft({O}_{{2}_{{sat}left(t-1right)}}-{O}_{{2}_{left(t-1right)}}right)xtriangle tright)$$
(4a)
$${18O}_{{2}_{(t)}}=, {18O}_{{2}_{(t-1)}}+left(frac{left({{GPP}}_{O2}+{dielMET}right)}{z}xfrac{{{PPFD}}_{left(t-1right)}}{{sum {PPFD}}_{24h}}x,{alpha }_{P},x,{{AF}}_{W}right)\ +left(frac{{{ER}}_{O2},xtriangle t}{z}x,{alpha }_{R},x,{{AF}}_{{DO}}left(t-1right)right)\ +left(frac{left(-{dielMET}right)}{z}xfrac{{{PPFD}}_{left(t-1right)}}{sum {{PPFD}}_{24h}}x,{alpha }_{R},x,{{AF}}_{{DO}}left(t-1right)right)\ +left({K}_{{O}_{2}}x,{alpha }_{g}xtriangle t,xleft(left({O}_{{2}_{{sat}left(t-1right)}}x,{alpha }_{g},x,{{AF}}_{{atm}}right)-{18O}_{{2}_{(t-1)}}right)right)$$
(4b)
Where GPPO2 and ERO2 (g O2 m−2 d−1) refer to the values obtained from diel O2 only, dielMET (g O2 m−2 d−1) is the diel metabolism term that allows for the estimation of diel ER and GPP from 18O2, KO2 is the O2 gas exchange rate (d−1), z is mean stream depth (m), PPFD is photosynthetic photon flux density (µmol m−2 s−1), Δt is time step between measurements (d), 18O2 is the concentration of 18O in dissolved O2 (g 18O m−3), AFDO is atomic fraction of dissolved O2 (mol18O:mol O2, measured), AFw is atomic fraction of H2O (mol 18O:mol O2, measured), AFatm is atomic fraction of atmospheric air (mol18O:mol O2, literature), αg is the fractionation factor during air–water gas exchange (0.9972, from ref. 60), αR is the fractionation factor during respiration measured in the chambers (varied by site30; Fig. 1), αp is the fractionation factor during photosynthesis (1.0000 from ref. 60).The inverse modeling approach finds the best estimates of parameters to match measured and modeled dissolved O2. The model assumes that the measured changes in O2 concentration represent the actual net diel changes in O2 concentration and uses an additional parameter, dielMET, that is a function of the isotopic enrichment occurring during respiration, derived from diel 18O2. This parameter increases daily ERO2 and GPPO2 of the same amount, adding and subtracting dielMET, to obtain daily δ18O2-ER and δ18O2-GPP, respectively.We estimated the posterior distributions of unknown parameters (ERO2, GPPO2, and dielMET) using a Bayesian inverse modeling approach15 and Markov chain Monte Carlo sampling with the R metrop function in the mcmc package61,62. Each model was run for at least 200,000 iterations using nominally informative priors based on the range of ERO2 and GPPO2. For dielMET, we used a minimally informative uniform prior distribution (0–100 g O2 m−2 d−1). We removed the first 10,000 iterations of model burn-in and assessed quality of model fit. Model runs using the minimum, average, and maximum αR values measured in the field recirculating chambers were also compared, and we selected the αR and report associated model metabolism estimates that generated the lowest sum of squared differences between the observed and modeled O2 and 18O2 diel values.Temperature-normalized comparisonsTo test the effect of temperature from the daily δ18O2-ER and δ18O2-GPP rates and account for daily variations in temperature, we normalized estimates from models to 20 °C (and report them as 20δ18O2-ER and 20δ18O2-GPP) for comparison with O2-derived metabolism estimates following33 with Eq. (5):$${rate},{at},20,{}^circ C=frac{{2.523* e}^{(0.0552* 20)}}{{2.523* e}^{(0.0552* {t}_{1})},* {rate},{at},{t}_{1}}$$
(5)
Where t1 is site temperature and rate is the measured rate (i.e., GPP or ER) at t1.Statistical analysesWe used multiple linear regression to find the best predictor of the magnitude of diel 20δ18O2-ER and differences between sites. To select the best model, we performed a stepwise variable selection and selected the best model based on the lowest AIC. Tested variables included percentage of impacted land use (%), 20δ18O2-GPP (g O2 m−2 d−1), conductivity (µS/cm), ash-free dry mass (AFDM, g), slope (%), water depth (m), and flow velocity (m/s) measured in the field. We used ANOVA to test the relative contribution of each variable selected with the AIC to total variance. Analyses were run with the R software61.Reporting summaryFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article. More

This might look like an ordinary rock formation, but the black material is actually preserved faeces and urine from a small mammal called a rock hyrax (Procavia capensis).Hyraxes, which are common in Africa and the Middle East, look like groundhogs but are more closely related to manatees and elephants. They live in crevasses and pick one spot to use as a latrine. The use of the same spot over tens of thousands of years creates a layered refuse heap known as a midden that scientists can mine for palaeoclimatic data. I specialize in examining the pollen in these dungheaps for information about the vegetation and climate of the past.Our team found this site in May, in the Cape Fold Belt mountains of South Africa, using a drone to help investigate crevasses. We were excited when we saw the extent of this midden; we think it covers at least 20,000 years. We came back after the winter to take a sample. This photograph was taken in September. My colleague and project leader Brian Chase, who has rock-climbing skills, used a circular saw to extract a wedge that we brought back to the lab for analysis.The team will first look at radioactive carbon to determine the age of the midden layers. Then, we will analyse the stable carbon isotopes to learn what plants the hyraxes were eating, which in turn provides clues to the climate of that time. When I examine the samples, I look for pollen grains, which enter the midden both in the hyrax’s urine and faeces and by being blown in by the wind. I’ll also look for charcoal, to tell how many wildfires occurred in the region over time, and fungal spores, which can reveal which animals were nearby.We now have a much more nuanced and detailed view of climate changes in southern Africa. The fieldwork is very demanding, requiring long days of hiking, but I love it. More

Soil sampling and determination of soil physical properties and synoptic dataSoil samples were taken from two coastal deserts in the north and south of Iran. Details of their geographic distribution and eco-physiological characterization were shown in Table 1. A total of 2 kg of soil samples were collected from 2 distinct sampling locations ranging in depth from 0 to 30 cm, and the samples were dried for 3 days at room temperature and in the dark before sifting. The soil samples were sieved using a 2 mm sieve to remove stones and other inert material before being stored in zip-top bags. Table 1 lists the soil samples’ physical characteristics, including soil texture (sand 2–0.02 mm; silt 0.02–0.002 mm; clay 0.002 mm), pH, and the proportions of clay, silt, and sand. Synoptic data from the past 10 years (2009–2019), including the average annual temperature, maximum temperature, minimum temperature, average rainfall, average annual wind speed, and maximum wind speed, were obtained from the I.R.OF Iran Meteor (http://www.irimo.ir/far/index.php).Bacterial isolation and effect of manure-based medium on their growthAccording to Chen et al. 2005, the soil-borne bacteria were isolated using direct-spreading method. For this essence soil samples were treated through a series of dilutions. The mixture of 1 g of soil sample was vortexed for 1 min after being suspended in 2 ml of sterile physiological saline (0.9% w/v NaCl). The mixture was then diluted serially (typically 10–1 to 10–7), and level 100 μl of the diluted soil samples were scattered on the surface of solidified plates using glass spreaders. The samples were then incubated for 1 to 3 days at 30 °C in an inverted posture without light. For bacterial isolation, we used eleven culture media including Nutrient Agar (NA), Nutrient Agar plus MnSO4 (NA + MnSO4), LB, Moller Hinton Agar (MHA), Acidithiobacillus (APH) medium, Violet Red Bile Lactose (VRB) agar medium, GYM Streptomyces medium, DPM medium, Azospirillum medium, Azotobacter medium and Manure based medium (MB).To prepare MB medium, dry animal manure and distilled water (1:6 w/v) were combined to create MB medium, which was then let to sit at room temperature for 16 h. The resulting mixture was then centrifuged at 5000 rcf for 30 min after being filtered twice. The next stage involved adding Hoagland salts (10% w/v) to the final extract, adjusting the medium’s pH to 5.8 ± 0.02, and autoclaving it for 20 min at 121 °C and 1.5 kPa. Before sterilization, bacteriological agar (1.5 w/v) was employed as a gelling agent to solidify the medium.After bacterial isolation on NA, NA+ MnSO4, LB, MHA, APH, VRB, GYM, DPM, and Azospibrillum media, the growth of all isolates was evaluated on an MB medium. To investigate isolates biomass in the same condition, we elected MB medium. First, the bacteria were grown in the liquid form of NA, NA+ MnSO4, LB, MHA, APH, VRB, GYM, DPM, and Azospirillum and Azotobacter media at 30 °C for 48 h, then 103 cells of each isolate were transferred to 48 wells plates containing MB medium, and plates were incubated at 30 °C for 10 h. Then, the growth of bacteria was read at an optical density (OD) of 630 nm 10 h after inoculation, the experiment was performed with three replicates. In the following step, CFU/ml equivalent to each OD was obtained by inoculating the uniform amount of liquid culture of the isolates on the solid form of MB medium at 30 °C for 16 h.Phenotypic characterization and biochemical identification of bacterial isolatesThe morphological analysis of the cell shape, colony (i.e., shape, color, and size), and biochemical tests were used to identify the bacterial isolates. Biochemical characterization was carried out By using gram staining, KOH27, oxidase, and catalase tests. For this essence, following Bartholomew’s method28, gram staining of bacteria was studied 48 h after inoculation on MHA, and the non-staining KOH method was used to confirm the results. Using 0.5 ml of a 10% hydrogen peroxide solution, a catalase test was conducted, and the generation of gas bubbles was monitored. Using biochemical oxidase discs, the oxidative activity of 27 isolates was investigated.Effect of abiotic stresses on bacterial isolatesTo determine the effect of abiotic stresses on isolates alkaline (MH medium with pH  10), salinity (MH medium supplemented with the final concentration of 100 mM NaCl), osmotic [MH medium supplemented with 25% polyethylene glycol (PEG) Mn6000], and thermal stresses (MH medium incubated at 15 °C for cold stress and 60 °C for heat stress) were screened. For all experiments, the incubation period was 15 h, and plates were kept in a dark condition.MALDI-TOF MS identification of isolatesSoil bacterial isolates were subcultured twice on MHA and incubated at 30 °C for 24 h before MALDI-TOF MS measurement. Then ∼0.1 µg of cell material was directly transferred from a bacterial colony or smear of colonies to a MALDI target spot. After drying at laboratory temperature, sample spots were overlaid with 1 μl of matrix solution (10 mg/mL a-cyano-4-hydroxycinnamic acid in 50% acetonitrile and 2.5% trifluoroacetic acid) and each measurement was carried out in triplicate (technical replicates). MS analysis was performed on an Autoflex MALDI-TOF mass spectrometer (Bruker Daltonics, Germany) using Flex Control 3.4 software (Bruker Daltonics, Germany). Calibration was carried out with the use of the Bacterial Test Standard (Bruker Daltonics, Germany). Soil isolates with a valid MALDI-TOF MS score of 2 were undoubtedly assigned to the genus/species level. For bacterial classification and identification, BioTyper 3.1 software (Bruker Daltonics, Germany) equipped with MBT 6903 MPS Library (released in April 2016), the MALDI Biotyper Preprocessing Standard Method, and the MALDI Biotyper MSP Identification Standard Method adjusted by the manufacturer (Bruker Daltonics, Germany) were used. Only the highest score value of all mass spectra belonging to individual cultures (biological and technical replicates) was recorded25. The score between 2.3 and 3.00 shows highly probable species-level identification and between 2.0 and 2.29 represents genus-level identification and probable species level of identification. A score between 1.7 and 1.99 indicates probable genus-level identification29.Effects of bacterial isolates on plants growthThe Seed and Plant Improvement Institute of Karaj (Karaj, Iran; http://www.spii.ir/homepage.aspx?site=DouranPortal&tabid=1&lang=faIR) provided the maize, canola, and wheat seeds (Zea mays. Var Kosha; Brassica napus Var Nima; Triticum aestivum Var Kalate). In greenhouse trials, 2 × 103 cells/seed of soil-borne isolates cultured in a manure-based medium were inoculated to maize, canola, and wheat plants. During the studies, sand that had been acid washed and autoclaved was used for planting. For three weeks, seedlings were kept under a 16/8 h day/night photoperiod with a 25 °C temperature. Three replications of a complete randomized block design were used for the colonization experiment’s treatments. Under the bacterial treatments, measurements were made of the plant growth parameters including shoot dry biomass (mg), root dry biomass (mg), shoot length (cm), root length (cm), shoot density (mg/cm), root density (mg/cm), and shoot/root weight (mg). Samples were dried at 60 °C for three days to measure dry biomass.Statistical analysisStatistical analysis was done by R software (version 4.1.3). One-way analysis of variance (ANOVA) was used to determine the significance of the experiment, and Fisher’s protected Least Significant Difference (LSD) test with a P-value of 0.01 was performed to separate the means. Furthermore, PCA analysis has been carried out based on the Clustvis package and the SVD imputation approach.Ethics approval and consent to participateAll authors agree to the ethics and consent to participate in this article and declare that this submission follows the policies of Scientific Reports. Accordingly, the material is the author’s original work, which has not been previously published elsewhere. The paper is not being considered for publication elsewhere. All authors have been personally and actively involved in substantial work leading to the paper and will take public responsibility for its content.Ethics for research involving plantsAll authors confirmed that experimental research and field studies on plants, including receiving the seeds from the Seed and Plant Improvement Institute of Karaj, complied with relevant institutional, national, and international guidelines and legislation. Furthermore, methods were conducted according to the relevant guidelines and regulations. More

Here we assessed how species and functional diversity components of wild bee assemblages responded to increasing urbanization levels, using a large dataset encompassing recent surveys gathering 838 sampling sites located in natural, semi-natural and urban habitats of France, Belgium and Switzerland.We found a weak, but significant negative effect of the proportion of impervious surfaces in a 500 m radius around each site on local species richness of bee communities. Thus, sites with high soil sealing tended to host less species than those with low soil sealing. However, this trend was not observed when using human population density as an urbanization metric: sites with denser human populations hosted on average the same number of species as less densely populated sites.Concerning taxonomic homogenization of communities, we did not record any effects of urbanization, both in terms of impervious surfaces or human population density.Analyses of occurrence rates of bee functional traits revealed significant differences between poorly and highly urbanized communities, for both urbanization metrics. With higher human population density, probabilities of occurrence of above-ground nesters, generalist and small species increased, and a higher probability of occurrence of above-ground nesters, generalists and social bees were recorded in areas with high soil sealing.Therefore, we found overall consistent results linking urbanization and wild bees taxonomic as well as functional trait diversity, even though analyses stemmed from a combination of many independent studies covering a broad range of anthropized and natural aeras from western Europe. This further highlights the greater generalizability of those ecological trends throughout European temperate biomes compared to other studies typically focusing on a single city and its immediate vicinity.Two complementary metrics of urbanization intensityTo quantify urbanization, we used two variables: soil sealing12,16,19,36 in a 500 m radius, and the mean human population density, also in a 500 m radius, the latter variable being used only recently to assess pollinator responses to urban environments37,38. These two variables return different but complementary information concerning urban environments. Indeed, if soil sealing gives an idea as to how human activities impact land use, human population density helps distinguish between very dense urban areas and very impervious areas with lower densities of buildings. High human population density areas are usually associated with high levels of soil sealing, but the contrary is not true. Similarly, areas with low soil sealing are usually associated with low human population densities, but again, the opposite is not always true. Therefore, we found it informative to consider both variables when analyzing the response of wild bee assemblages to urbanization.Note that some specific habitat types, for example business districts, are exceptions to the rule. These places are indeed very densely urbanized, but with very low population density. However, no inventories have been carried out in these places, and thus will not be a problem for our study.Response of bee community species richness to urbanizationOne of our goals was to position this study in the context of the contrasting findings on pollinator communities and urbanization. Whereas no consistent trend is reported in literature15, our large dataset reveals that high soil sealing is detrimental to wild bee species richness. This offers a unified view of a trend that has been unequally evidenced from studies focusing on a single or few cities only. High proportions of soil sealing reduce the availability of nesting sites for ground-nesting bee species. This may in turn lower the species diversity of local assemblages, by filtering out ground-nesting bees, leaving mainly cavity-nesting bees. Furthermore, high levels of soil sealing can lead to depletion of floral resources, of extreme importance for bees, especially in highly disturbed environments such as cities39,40. Note that several previous studies report the opposite, with high local species richness of wild bees in urbanized habitats. However, these positive effects are often associated with intermediate levels of urbanization15,16, where private gardens and other green spaces may supply abundant floral resources, in conjunction with intermediate levels of soil sealing16,17,18,19,20,24.On the contrary, there was no significant relationship between local species richness and human population density. Recently, two recent studies have used this metric to analyze how urbanization impacts local diversity of bee, hoverfly37 or butterfly38 assemblages, and both studies report negative impacts of human population density. However, high levels of human population density do not necessarily correlate with low availability of floral resources or nesting sites for pollinating insects. Several studies show that densely-populated urban environments may be adequate habitats for pollinating insects, due to alternative management practices of urban green space41 and the year-round availability of ornamental flowers42,43. Here, the absence of a clear effect of human population density on local bee species richness masks a change in the species composition of the communities, as shown by the increasing proportion of cavity nesters, compared with ground nesters. Indeed, despite the lower availability of nesting resources for ground-nesters, cavity-nesters take over in high-density areas, where more concrete structures and buildings are present15, thus they may compensate for the loss of ground-nesting bee species.Wild bee community homogenization and urbanizationWe did not observe any relationship between mean pairwise β-diversity and the two metrics of urbanization. This result contrasts with those of Banaszak-Cibicka and Żmihorski (2020)44 who found more homogeneous wild bee communities in urban environments compared to non-urban ones. Similar results have been reported for bees, with homogenization of urban pollinator communities compared to rural ones28,45. Biotic homogenization in urban environments has also been reported for other taxa, for example birds46.In our study, when considering urbanization levels, either in terms of soil sealing or human population density, urban wild bee communities are not more or less taxonomically homogeneous than non-urban ones. It is important to note that this result does not imply that urban and non-urban wild bee communities are similar, but that the homogenization of wild bee communities is constant throughout the urbanization gradient. In other words, urban communities are as dissimilar as non-urban ones. Here, the β diversity values are quite high (ranging from 0.68 to 0.96), emphasizing that even urban areas have quite dissimilar communities when compared to each other. This high level of dissimilarity among wild bee communities in urban environments can be explained by the large range of biogeographical regions encompassed in our dataset (Fig. 5), as each of these regions harbors a specific wild bee fauna34.Local factors in cities might also explain these high levels of dissimilarity. We know for example that green space connectivity has effects on species richness, with more wild bee species and abundance in cities with more connected green spaces47. Another local explanation might come from contrasting green space management practices among cities. Not all cities have the same policies, and urban green space management is crucial to the establishment and sustainability of diverse pollinator communities14,15,48. Thus, we expect more dissimilar wild bee communities among cities with differing green space layout and management.Figure 5Grouped sampling sites (n = 532) in France, Belgium and Switzerland, with the biogeographical regions. In total, 238 sites belong to the Continental region, 178 to the Atlantic, 106 to de Mediterranean and 10 to the Alpine. This figure was generated using QGIS software, v3.10.13 (https://www.qgis.org/).Full size imageFunctional responses of bee communities to urbanizationSeveral studies have already shown trends on how urban areas filter wild bee communities based on their functional traits (see30 and49 for reviews). However, as for taxonomic diversity, it is often difficult to identify clear variation patterns50. Using our large dataset, we could identify typical wild bee functional traits that are favored in urban environments, thus informing on the average functional profiles of wild bee species that may thrive in cities. We found urban wild bees in general to be typically above-ground nesters and generalists, while different trends were established for their body size and sociality, depending on the considered urbanization metric (Fig. 6).Figure 6Summary picture of an urban bee community, compared to a non-urban one. This figure was generated using Inkscape v1.2 (https://inkscape.org/).Full size imageNesting habitsAbove-ground nesting species were more frequent with increasing urbanization than below-ground nesting ones, and this result was recorded with both urbanization metrics.This result is consistent with what was previously reported in the literature16,49,51,52. Indeed, cities, with high proportions of impervious surfaces and buildings, offer fewer nesting habitats to ground-nesting species15, nesting sites becoming a limiting factor39. On the other hand, above-ground nesters can do well in cities with the presence of man-made structures, depending on their ability to use them and on their availability53.The presence of green areas in cities can help ground-nesting bee species by offering more nesting opportunities and resources17. Several studies highlight the importance of parks and gardens in supporting bee biodiversity in cities12,18,31,54, which otherwise are constraining environments due to soil sealing.DietGeneralist species were more frequent in more urbanized sites than specialist ones, and this was recorded for both urbanization metrics.This is in accordance with what was previously found in the literature32,50,51,52,54,55, as specialist bee species depend on the presence of their host plants to complete their life-cycle, which are often scarce due to the rarefaction of native flowering resources. As one can find many exotic flowers in cities, especially in residential gardens and urban parks56, we expect to detect less oligolectic bee species in densely urbanized habitats57.Notwithstanding, Banaszak-Cibicka et al. (2018)20 found more oligolectic species in urban parks of Poznań (Poland) compared to a national park. Thus, urban areas are not always depleted of specialist species, and well-managed parks with preserved native floral resources can obviously support specialist wild bee species in cities58.Additionally, it is important to emphasize that the presence of an exotic plant species may concomitantly support an associated specialist bee species. In Poland, for instance, the spread of Bryonia dioica in urban environments also brought the Andrena florea wild bee species, specialized on this plant59.Body sizeWe recorded contrasting effects of the two urbanization metrics on wild bee body size: small species were more frequent in relation to higher human population density compared to large species, but we found no difference with the proportion of impervious surfaces. Contrasting impacts of urbanization on bee body size are also reported in the literature, with some studies finding little to no effect32,50, and some finding that urbanization often favors smaller bee species12,30,60. Bee body size is of particular importance because it is related to the foraging range of individuals61,62. In fragmented habitats, such as dense urban environments, distances between suitable nesting and feeding habitats may select for smaller species that can remain on small green spaces and rarely need to commute across several green spaces. Furthermore, small bees may be favored given that they need fewer floral resources than large bees, even though large bees can fly further62.This might also explain the difference in the response of bee body size to the two urbanization metric results. In densely populated cities, it is harder to fly between suitable habitats, even for larger bees, as higher buildings and structures may act as barriers to their movement. Indeed, it has been recently shown that the 3D structure of cities impacts wild bee community composition63. Thus, being able to fly further might no longer be an advantage, and larger bees, requiring more floral resources than smaller ones, might be selected against. On the contrary, very impervious areas do not always host high building density (for example, as in the case of parking lots), thus making it easier for large wild bees to fly between bare soil areas.Densely populated areas might also exhibit warmer temperatures due to the urban heat island effect, and this could, in turn, result in the selection of smaller individuals, as we know that in cities, higher temperature results in smaller body sizes64.SocialityWe also recorded contrasting effects of the two urbanization metrics on sociality: social species were more frequent in relation to higher proportion of impervious surface compared to solitary ones, but no effect was recorded with human population density. This is in agreement with a recent literature review that reports on no consensus concerning the response of this trait to urbanization30.However, some urban habitats are shown to host more social species than rural habitats20,32, which may be linked to better reproductive success in cities compared to rural habitats such as agricultural environments65, an explanation that is consistent with our results on the soil sealing—sociality relationship.Conclusion, limits & future directionsOverall, our findings suggest that urban environment filters wild bee communities based on their functional traits. Our results also underscore different impacts of urbanization metrics on local species diversity, with a significant negative impact of soil sealing. On the contrary, both soil sealing and human population densities create strong functional filtering of trait assemblages.These results are particularly relevant since they arise from a range of independent studies, thus providing a general view on the wild bee communities in urban environments from western Europe. Since this study covers different biogeographical zones, it further underlines its applicability to other temperate countries. We therefore expect similar patterns to shape wild bee communities in urbanized areas from other temperate regions, but further confirmatory studies would be welcome.Our study also delivers a clear message concerning wild bee communities in urban environments. Urban environments cannot compare with non-urban ones in terms of species richness and trait diversities of bee communities. However, simple management practices of urban green spaces, such as differentiated management, or simply low management66, may help in maintaining this diversity. Indeed, not all green spaces are equally valuable in supporting wild bees, and pollinator assemblages in general49. For example, it has been shown that pollinator richness was positively influenced by green space size, but also by management measures such as mowing67. Increasing the quantity of floral resources and their spatio-temporal availability and diversity40,68 could also help conserving pollinator communities and pollination function in cities69, as long as these resources are native or attractive to pollinators.We can then hypothesize that changes in managing practices could help increase functional diversity of bees in cities, with specialist and ground-nesting species being found more frequently in these low-managed urban areas.Finally, if managing urban green space is of great importance to protect biodiversity in cities, it is crucial to involve all stakeholders, especially residents70 to achieve efficient and socially-accepted measures.In the future, it will be important to consider intra-city landscape variation, and see how urban characteristics might influence taxonomic and trait diversity. This will surely allow us to better understand the dynamics shaping wild bee communities in urban environments. More

Pacifici, M., Di Marco, M. & Watson, J. E. M. Protected areas are now the last strongholds for many imperiled mammal species. Conserv. Lett. 13, 1–7 (2020).
Google Scholar 
Cardillo, M. et al. Human population density and extinction risk in the world’s carnivores. PLoS Biol. 2, 909–914 (2004).CAS 

Google Scholar 
Steffen, W., Broadgate, W., Deutsch, L., Gaffney, O. & Ludwig, C. The trajectory of the anthropocene: the great acceleration. Anthr. Rev. 2, 81–98 (2015).
Google Scholar 
Wolf, C. & Ripple, W. J. Range contractions of the world’s large carnivores. R. Soc. Open Sci. 4, 170052 (2017).ADS 
PubMed 
PubMed Central 

Google Scholar 
Wolf, C. & Ripple, W. J. Prey depletion as a threat to the world’s large carnivores. R. Soc. Open Sci. 3, 160252 (2016).ADS 
PubMed 
PubMed Central 

Google Scholar 
Ripple, W. J. et al. Status and ecological effects of the world’s largest carnivores. Science 343, 1241484 (2014).PubMed 

Google Scholar 
Geldmann, J. et al. Effectiveness of terrestrial protected areas in reducing habitat loss and population declines. Biol. Conserv. 161, 230–238 (2013).
Google Scholar 
Benítez-López, A. et al. The impact of hunting on tropical mammal and bird populations. Science 356, 180–183 (2017).ADS 
PubMed 

Google Scholar 
Di Marco, M., Ferrier, S., Harwood, T. D., Hoskins, A. J. & Watson, J. E. M. Wilderness areas halve the extinction risk of terrestrial biodiversity. Nature 573, 582–585 (2019).ADS 
PubMed 

Google Scholar 
Rija, A. A., Critchlow, R., Thomas, C. D. & Beale, C. M. Global extent and drivers of mammal population declines in protected areas under illegal hunting pressure. PLoS One 15, 1–14 (2020).
Google Scholar 
Bamford, A. J., Ferrol-Schulte, D. & Wathan, J. Human and wildlife usage of a protected area buffer zone in an area of high immigration. Oryx 48, 504–513 (2014).
Google Scholar 
Snyder, K. D., Mneney, P. B. & Wittemyer, G. Predicting the risk of illegal activity and evaluating law enforcement interventions in the western Serengeti. Conserv. Sci. Pract. 1, 1–13 (2019).
Google Scholar 
Woodroffe, R. & Ginsberg, J. R. Edge effects and the extinction of populations inside protected areas. Science 280, 2126–2128 (1998).ADS 
CAS 
PubMed 

Google Scholar 
Lynagh, F. M. & Urich, P. B. A critical review of buffer zone theory and practice: A Philippine case study. Soc. Nat. Resour. 15, 129–145 (2002).
Google Scholar 
Paolino, R. M. et al. Buffer zone use by mammals in a Cerrado protected area. Biota Neotrop. 16, (2016).
Mills, K. L. et al. Comparable space use by lions between hunting concessions and national parks in West Africa. J. Appl. Ecol. 57, 975–984 (2020).ADS 

Google Scholar 
Lindsey, P. A. et al. The performance of African protected areas for lions and their prey. Biol. Conserv. 209, 137–149 (2017).
Google Scholar 
Tyrrell, P., Russell, S. & Western, D. Seasonal movements of wildlife and livestock in a heterogenous pastoral landscape: Implications for coexistence and community based conservation. Glob. Ecol. Conserv. 12, 59–72 (2017).
Google Scholar 
Veldhuis, M. P. et al. Cross-boundary human impacts compromise the Serengeti-Mara ecosystem. Science 363, 1424–1428 (2019).ADS 
CAS 
PubMed 

Google Scholar 
Everatt, K. T., Andresen, L. & Somers, M. J. The influence of prey, pastoralism and poaching on the hierarchical use of habitat by an apex predator. Afr. J. Wildl. Res. 45, 187–196 (2015).
Google Scholar 
Oriol-Cotterill, A., Macdonald, D. W., Valeix, M., Ekwanga, S. & Frank, L. G. Spatiotemporal patterns of lion space use in a human-dominated landscape. Anim. Behav. 101, 27–39 (2015).
Google Scholar 
Schuette, P., Creel, S. & Christianson, D. Coexistence of African lions, livestock, and people in a landscape with variable human land use and seasonal movements. Biol. Conserv. 157, 148–154 (2013).
Google Scholar 
Beattie, K., Olson, E. R., Kissui, B., Kirschbaum, A. & Kiffner, C. Predicting livestock depredation risk by African lions (Panthera leo) in a multi-use area of northern Tanzania. Eur. J. Wildl. Res. 66, 1–4 (2020).
Google Scholar 
Loveridge, A. J., Hemson, G., Davidson, Z. & Macdonald, D. W. African lions on the edge: Reserve boundaries as ‘attractive sinks’. In Biology and Conservation of Wild Felids (eds. Macdonald, D. W. & Loveridge, A. J.) 283–304 (Oxford University Press, 2010).Boyers, M., Parrini, F., Owen-Smith, N., Erasmus, B. F. N. & Hetem, R. S. How free-ranging ungulates with differing water dependencies cope with seasonal variation in temperature and aridity. Conserv. Physiol. 7, 1–12 (2019).
Google Scholar 
Abade, L. et al. The relative effects of prey availability, anthropogenic pressure and environmental variables on lion (Panthera leo) site use in Tanzania’s Ruaha landscape during the dry season. J. Zool. 310, 135–144 (2020).
Google Scholar 
Hopcraft, J. G. C., Sinclair, A. R. E. & Packer, C. Planning for success: Serengeti lions seek prey accessibility rather than abundance. J. Anim. Ecol. 74, 559–566 (2005).
Google Scholar 
Treves, A. & Karanth, K. U. Human-carnivore conflict and perspectives on carnivore management worldwide. Conserv. Biol. 17, 1491–1499 (2003).
Google Scholar 
Kisingo, A. W. Governance of Protected Areas in the Serengeti Ecosystem, Tanzania (University of Victoria, 2013).UNEP-WCMC & IUCN. Protected planet: the world database on protected areas (WDPA). www.protectedplanet.net (2020).Zella, A. Y. The management of protected areas in Serengeti ecosystem: A case study of Ikorongo and Grumeti Game Reserves (IGGRs). Int. J. Eng. Sci. 6, 22–50 (2016).
Google Scholar 
IUCN. Ngorongoro Conservation Area conservation outlook assessment. The IUCN World Heritage Outlook https://worldheritageoutlook.iucn.org/explore-sites/wdpaid/2010 (2020).Kittle, A. M., Bukombe, J. K., Sinclair, A. R. E., Mduma, S. A. R. & Fryxell, J. M. Landscape-level movement patterns by lions in western Serengeti: Comparing the influence of inter-specific competitors, habitat attributes and prey availability. Mov. Ecol. 4, 1–18 (2016).
Google Scholar 
Packer, C. et al. Ecological change, group territoriality, and population dynamics in Serengeti lions. Science 307, 390–393 (2005).ADS 
CAS 
PubMed 

Google Scholar 
Mwampeta, S. B. et al. Lion and spotted hyena distributions within a buffer area of the Serengeti-Mara ecosystem. Sci. Rep. 11, 1–8 (2021).
Google Scholar 
Grumeti Fund. Protecting wildlife and human lives in the western corridor of the Serengeti. https://www.grumetifund.org/blog/updates/protecting-wildlife-and-human-lives-in-the-western-corridor-of-the-serengeti/ (2020).IUCN. Serengeti National Park conservation outlook assessment. The IUCN World Heritage Outlook https://worldheritageoutlook.iucn.org/explore-sites/wdpaid/2575 (2017).Veldhuis, M. P. et al. Data from: Cross-boundary human impacts compromise the Serengeti-Mara ecosystem. Dryad https://doi.org/10.5061/dryad.b303788 (2021).Larsen, F. et al. Wildebeest migration drives tourism demand in the Serengeti. Biol. Conserv. 248, 108688 (2020).
Google Scholar 
Norton-Griffiths, M., Herlocker, D. & Pennycuick, L. The patterns of rainfall in the Serengeti Ecosystem, Tanzania. Afr. J. Ecol. 13, 347–374 (1975).
Google Scholar 
McNaughton, S. J. Serengeti grassland ecology: The role of composite environmental factors and contingency in community organization. Ecol. Monogr. 53, 291–320 (1983).
Google Scholar 
Buchhorn, M. et al. Copernicus global land service: land cover 100m: version 3 globe 2015-2019. Copernicus Global Land Operations. Zenodo. https://doi.org/10.5281/zenodo.3938963.Boone, R. B., Thirgood, S. J. & Hopcraft, J. G. C. Serengeti wildebeest migratory patterns modeled from rainfall and new vegetation growth. Ecology 87, 1987–1994 (2006).PubMed 

Google Scholar 
Ogutu, J. O. & Dublin, H. T. The response of lions and spotted hyaenas to sound playbacks as a technique for estimating population size. Afr. J. Ecol. 36, 83–95 (1998).
Google Scholar 
Fyumagwa, R. D. et al. Comparison of anaesthesia and cost of two immobilization protocols in free-ranging lions. S. Afr. J. Wildl. Res. 42, 67–70 (2012).
Google Scholar 
Rija, A. A. Spatial Pattern of Illegal Activities and the Impact on Wildlife Populations in Protected Areas in the Serengeti Ecosystem. (University of York, 2017).Kideghesho, J. R. Wildlife Conservation and Local Land Use Conflicts in Western Serengeti Corridor, Tanzania (Norwegian University of Science and Technology, 2006).Holmern, T., Muya, J. & Røskaft, E. Local law enforcement and illegal bushmeat hunting outside the Serengeti National Park, Tanzania. Environ. Conserv. 34, 55–63 (2007).
Google Scholar 
Schmitt, J. A. Improving Conservation Efforts in the Serengeti Ecosystem, Tanzania: An Examination of Knowledge, Benefits, Costs, and Attitudes (University of Minnesota, 2010).Kaaya, E. & Chapman, M. Micro-credit and community wildlife management: Complementary strategies to improve conservation outcomes in Serengeti National Park, Tanzania. Environ. Manag. 60, 464–475 (2017).ADS 

Google Scholar 
Kideghesho, J. R., Røskaft, E. & Kaltenborn, B. P. Factors influencing conservation attitudes of local people in Western Serengeti, Tanzania. Biodivers. Conserv. 16, 2213–2230 (2007).
Google Scholar 
Kegamba, J. J., Sangha, K. K., Wurm, P. & Garnett, S. T. A review of conservation-related benefit-sharing mechanisms in Tanzania. Glob. Ecol. Conserv. 33, e01955 (2022).
Google Scholar 
Rija, A. A. & Kideghesho, J. R. Poachers’ strategies to surmount anti-poaching efforts in Western Serengeti, Tanzania. In Protected Areas in Northern Tanzania (eds. Durrant, J. O. et al.) 91–112 (Springer Nature Switzerland AG, 2020).Mfunda, I. M. & Røskaft, E. Wildlife or crop production: The dilemma of conservation and human livelihoods in Serengeti, Tanzania. Int. J. Biodivers. Sci. Ecosyst. Serv. Manag. 7, 39–49 (2011).
Google Scholar 
Kideghesho, J. R. Reversing the trend of wildlife crime in Tanzania: Challenges and opportunities. Biodivers. Conserv. 25, 427–449 (2016).
Google Scholar 
Sisya, E., Frankfurt Zoological Society & Tanzania National Parks Authority. Serengeti Park Roads. Serengeti GIS and Data Centre and ArcGIS Online. ArcGIS online https://www.arcgis.com/home/item.html?id=f8d9e2cb6ab24b92bd6d645a0d659963. (2018).Maliti, H., von Hagen, C., Frankfurt Zoological Society, Tanzania National Parks Authority & Hopcraft, J. G. C. Serengeti Park rivers. https://serengetidata.weebly.com/rivers-and-lakes.html (2008).Worldpop & Center for International Earth Science Information Network. The spatial distribution of population density in 2018, Tanzania. https://doi.org/10.5258/SOTON/WP00674 (2018).Gilbert, M. et al. Global cattle distribution in 2010 (5 minutes of arc). Harvard Dataverse, Version 3. https://doi.org/10.7910/DVN/GIVQ7 (2018).Gilbert, M. et al. [dataset] Global goat distribution in 2010 (5 minutes of arc). Harvard Dataverse, Version 3. https://doi.org/10.7910/DVN/OCPH42 (2018).Gilbert, M. et al. Global sheep distribution in 2010 (5 minutes of arc). Harvard Dataverse, Version 3. https://doi.org/10.7910/DVN/BLWPZN (2018).Gilbert, M. et al. Global distribution data for cattle, buffaloes, horses, sheep, goats, pigs, chickens and ducks in 2010. Sci. Data 5, 1–11 (2018).
Google Scholar 
Swihart, R. K. & Slade, N. A. Testing for independence of observations in animal movements. Ecology 66, 1176–1184 (1985).
Google Scholar 
Seaman, D. E. & Powell, R. A. An evaluation of the accuracy of kernel density estimators for home range analysis. Ecology 77, 2075–2085 (1996).
Google Scholar 
Calenge, C. The package adehabitat for the R software: Tool for the analysis of space and habitat use by animals. Ecol. Model. 197, 516–519 (2006).
Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Version 4.0.4. https://www.r-project.org/ (2021).Dormann, C. F. et al. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography (Cop.) 36, 27–46 (2013).
Google Scholar 
Thomas, D. L. & Taylor, E. J. Study designs and tests for comparing resource use and availability II. J. Wildl. Manag. 70, 324–336 (2006).
Google Scholar 
Swets, J. A. Measuring the accuracy of diagnostic systems. Science 240, 1285–1293 (1988).ADS 
MathSciNet 
CAS 
PubMed 
MATH 

Google Scholar 
Sommer, S. & Huggins, R. M. Variables selection using the Wald test and a robust CP. J. R. Stat. Soc. 45, 15–29 (1996).MATH 

Google Scholar 
Burnham, K. P. & Anderson, D. D. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (Springer, 2002).Lenth, R. emmeans: Estimated Marginal Means, aka Least-Squares Means. (2020).Ogutu, J. O. & Dublin, H. T. Demography of lions in relation to prey and habitat in the Maasai Mara National Reserve, Kenya. Afr. J. Ecol. 40, 120–129 (2002).
Google Scholar 
Henschel, P. et al. Determinants of distribution patterns and management needs in a critically endangered lion (Panthera leo) population. Front. Ecol. Evol. 4, 1–14 (2016).
Google Scholar 
Melubo, K. & Lovelock, B. Living inside a UNESCO World Heritage Site: The perspective of the Maasai community in Tanzania. Tour. Plan. Dev. 16, 197–216 (2019).
Google Scholar 
Makupa, E. E. Conservation Efforts and Local Livelihoods in Western Serengeti, Tanzania: Experiences from Ikona Community Wildlife Management Area (University of Victoria, 2013).Ndibalema, V. G. & Songorwa, A. N. Illegal meat hunting in serengeti: Dynamics in consumption and preferences. Afr. J. Ecol. 46, 311–319 (2008).
Google Scholar 
Geldmann, J., Joppa, L. N. & Burgess, N. D. Mapping change in human pressure globally on land and within protected areas. Conserv. Biol. 28, 1604–1616 (2014).PubMed 

Google Scholar 
Tuqa, J. H. et al. Impact of severe climate variability on lion home range and movement patterns in the Amboseli ecosystem, Kenya. Glob. Ecol. Conserv. 2, 1–10 (2014).
Google Scholar 
Blackburn, S., Hopcraft, J. G. C., Ogutu, J. O., Matthiopoulos, J. & Frank, L. Human–wildlife conflict, benefit sharing and the survival of lions in pastoralist community-based conservancies. J. Appl. Ecol. 53, 1195–1205 (2016).
Google Scholar 
Thirgood, S. et al. Can parks protect migratory ungulates? The case of the Serengeti wildebeest. Anim. Conserv. 7, 113–120 (2004).
Google Scholar 
Wittemyer, G., Elsen, P., Bean, W. T., Burton, A. C. O. & Brashares, J. S. Accelerated human population growth at protected area edges. Science 321, 123–126 (2008).ADS 
CAS 
PubMed 

Google Scholar 
Hayward, M. W. & Kerley, G. I. H. Prey preferences and dietary overlap amongst Africa’s large predators. S. Afr. J. Wildl. Res. 38, 93–108 (2008).
Google Scholar 
Mkonyi, F. J., Estes, A. B., Lichtenfeld, L. L. & Durant, S. M. Large carnivore distribution in relationship to environmental and anthropogenic factors in a multiple-use landscape of northern Tanzania. Afr. J. Ecol. 56, 972–983 (2018).
Google Scholar 
Hill, J. E., De Vault, T. L. & Belant, J. L. A review of ecological factors promoting road use by mammals. Mamm. Rev. 51, 214–227 (2021).
Google Scholar 
Hägerling, H. G. & Ebersole, J. J. Roads as travel corridors for mammals and ground birds in Tarangire National Park, Tanzania. Afr. J. Ecol. 55, 701–704 (2017).
Google Scholar 
Bateman, P. W. & Fleming, P. A. Are negative effects of tourist activities on wildlife over-reported? A review of assessment methods and empirical results. Biol. Conserv. 211, 10–19 (2017).
Google Scholar 
de Boer, W. F. et al. Spatial distribution of lion kills determined by the water dependency of prey species. J. Mammal. 91, 1280–1286 (2010).
Google Scholar 
Loveridge, A. J., Valeix, M., Elliot, N. B. & Macdonald, D. W. The landscape of anthropogenic mortality: How African lions respond to spatial variation in risk. J. Appl. Ecol. 54, 815–825 (2017).
Google Scholar 
Suraci, J. P. et al. Behavior-specific habitat selection by African lions may promote their persistence in a human-dominated landscape. Ecology 100, 1–11 (2019).
Google Scholar 
Snyman, A., Raynor, E., Chizinski, C., Powell, L. & Carroll, J. African lion (Panthera leo) space use in the Greater Mapungubwe Transfrontier Conservation Area. Afr. J. Wildl. Res. 48, 023001 (2018).
Google Scholar 
Mwakaje, A. G. et al. Community-based conservation, income governance, and poverty alleviation in Tanzania: The case of the Serengeti Ecosystem. J. Environ. Dev. 22, 51–73 (2013).
Google Scholar 
Everatt, K. T., Moore, J. F. & Kerley, G. I. H. Africa’s apex predator, the lion, is limited by interference and exploitative competition with humans. Glob. Ecol. Conserv. 20, e00758 (2019).
Google Scholar  More

Schimel, D. S. Drylands in the Earth system. Science 327, 418–419 (2010).Article 
CAS 

Google Scholar 
Whitford, W. G. Ecology of Desert Systems (Academic Press, 2002).D’Odorico, P., Porporato, A. & Runyan, C. W. Dryland Ecohydrology Vol. 9 (Springer, 2019). A comprehensive introduction to dryland ecohydrology.Lal, R. Carbon cycling in global drylands. Curr. Clim. Change Rep. 5, 221–232 (2019).Article 

Google Scholar 
Ahlström, A. et al. The dominant role of semi-arid ecosystems in the trend and variability of the land CO2 sink. Science 348, 895–899 (2015). Illustrates the role drylands play in determining the variability and long-term trend of the terrestrial CO2 sink.Article 

Google Scholar 
Poulter, B. et al. Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle. Nature 509, 600–603 (2014). Illustrates the role drylands play in determining the variability of the terrestrial CO2 sink.Maestre, F. T. et al. Structure and functioning of dryland ecosystems in a changing world. Annu. Rev. Ecol. Evol. Syst. 47, 215–237 (2016). A comprehensive review of dryland structure and functioning.Article 

Google Scholar 
Wang, L., Kaseke, K. F. & Seely, M. K. Effects of non-rainfall water inputs on ecosystem functions. WIREs Water 4, e1179 (2017). Highlights the often-ignored role of non-rainfall water inputs to dryland ecosystem dynamics.Article 

Google Scholar 
Li, C. et al. Drivers and impacts of changes in China’s drylands. Nat. Rev. Earth Environ. 2, 858–873 (2021).Article 

Google Scholar 
Thornton, P. K., Ericksen, P. J., Herrero, M. & Challinor, A. J. Climate variability and vulnerability to climate change: a review. Glob. Change Biol. 20, 3313–3328 (2014).Article 

Google Scholar 
IPCC Climate Change 2022: Impacts, Adaptation, and Vulnerability (eds Pörtner, H.-O. et al.) (Cambridge Univ. Press, 2022).Gonsamo, A. et al. Greening drylands despite warming consistent with carbon dioxide fertilization effect. Glob. Change Biol. 27, 3336–3349 (2021).Article 

Google Scholar 
Kaptué, A. T., Prihodko, L. & Hanan, N. P. On regreening and degradation in Sahelian watersheds. Proc. Natl Acad. Sci. USA 112, 12133–12138 (2015).Article 

Google Scholar 
Brookshire, E. J., Stoy, P. C., Currey, B. & Finney, B. The greening of the Northern Great Plains and its biogeochemical precursors. Glob. Change Biol. 26, 5404–5413 (2020).Article 

Google Scholar 
Song, X.-P. et al. Global land change from 1982 to 2016. Nature 560, 639–643 (2018).Article 
CAS 

Google Scholar 
Ravi, S. et al. Biological invasions and climate change amplify each other’s effects on dryland degradation. Glob. Change Biol. 28, 285–295 (2022).Article 
CAS 

Google Scholar 
Allen, C. D., Breshears, D. D. & McDowell, N. G. On underestimation of global vulnerability to tree mortality and forest die-off from hotter drought in the Anthropocene. Ecosphere https://doi.org/10.1890/ES15-00203.1 (2015).Yu, K. et al. The competitive advantage of a constitutive CAM species over a C4 grass species under drought and CO2 enrichment. Ecosphere 10, e02721 (2019).Article 

Google Scholar 
Fensholt, R. et al. in Remote Sensing Time Series (eds Kuenzer, C. et al.) 183–292 (Springer, 2015).Andela, N., Liu, Y., Van Dijk, A., De Jeu, R. & McVicar, T. Global changes in dryland vegetation dynamics (1988-2008) assessed by satellite remote sensing: comparing a new passive microwave vegetation density record with reflective greenness data. Biogeosciences 10, 6657–6676 (2013).Article 

Google Scholar 
Lu, X., Wang, L. & McCabe, M. F. Elevated CO2 as a driver of global dryland greening. Sci. Rep. 6, 20716 (2016).Article 
CAS 

Google Scholar 
Venter, Z., Cramer, M. & Hawkins, H.-J. Drivers of woody plant encroachment over Africa. Nat. Commun. 9, 2272 (2018).Article 
CAS 

Google Scholar 
Ukkola, A. M. et al. Annual precipitation explains variability in dryland vegetation greenness globally but not locally. Glob. Change Biol. 27, 4367–4380 (2021).Article 
CAS 

Google Scholar 
Zhang, W., Brandt, M., Tong, X., Tian, Q. & Fensholt, R. Impacts of the seasonal distribution of rainfall on vegetation productivity across the Sahel. Biogeosciences 15, 319–330 (2018).Article 

Google Scholar 
Fensholt, R. & Rasmussen, K. Analysis of trends in the Sahelian ‘rain-use efficiency’ using GIMMS NDVI, RFE and GPCP rainfall data. Remote Sens. Environ. 115, 438–451 (2011).Article 

Google Scholar 
Zhang, W. et al. Ecosystem structural changes controlled by altered rainfall climatology in tropical savannas. Nat. Commun. 10, 671 (2019).Article 
CAS 

Google Scholar 
Brandt, M. et al. Reduction of tree cover in West African woodlands and promotion in semi-arid farmlands. Nat. Geosci. 11, 328–333 (2018).Article 
CAS 

Google Scholar 
Hufkens, K. et al. Productivity of North American grasslands is increased under future climate scenarios despite rising aridity. Nat. Clim. Change 6, 710–714 (2016).Article 

Google Scholar 
Choler, P., Sea, W., Briggs, P., Raupach, M. & Leuning, R. A simple ecohydrological model captures essentials of seasonal leaf dynamics in semi-arid tropical grasslands. Biogeosciences 7, 907–920 (2010).Article 

Google Scholar 
Huang, J., Yu, H., Dai, A., Wei, Y. & Kang, L. Drylands face potential threat under 2 °C global warming target. Nat. Clim. Change 7, 417–422 (2017).Article 

Google Scholar 
Huang, J., Yu, H., Guan, X., Wang, G. & Guo, R. Accelerated dryland expansion under climate change. Nat. Clim. Change 6, 166–171 (2016).Article 

Google Scholar 
Lian, X. et al. Multifaceted characteristics of dryland aridity changes in a warming world. Nat. Rev. Earth Environ. 2, 232–250 (2021). Provides a comprehensive analysis on the dryland expansion debates.Article 

Google Scholar 
Fatichi, S. et al. Partitioning direct and indirect effects reveals the response of water-limited ecosystems to elevated CO2. Proc. Natl Acad. Sci. USA 113, 12757–12762 (2016).Article 
CAS 

Google Scholar 
Daramola, M. T. & Xu, M. Recent changes in global dryland temperature and precipitation. Int. J. Climatol. 42, 1267–1282 (2022).Article 

Google Scholar 
Berg, A. & McColl, K. A. No projected global drylands expansion under greenhouse warming. Nat. Clim. Change 11, 331–337 (2021).Article 

Google Scholar 
Berg, A. & Sheffield, J. Climate change and drought: the soil moisture perspective. Curr. Clim. Change Rep. 4, 180–191 (2018).Article 

Google Scholar 
Jiao, W. et al. Observed increasing water constraint on vegetation growth over the last three decades. Nat. Commun. 12, 3777 (2021). This study found that vegetation growth in the Northern Hemisphere is becoming increasingly water limited.Article 
CAS 

Google Scholar 
Gherardi, L. A. & Sala, O. E. Effect of interannual precipitation variability on dryland productivity: a global synthesis. Glob. Change Biol. 25, 269–276 (2019).Article 

Google Scholar 
D’Odorico, P. & Bhattachan, A. Hydrologic variability in dryland regions: impacts on ecosystem dynamics and food security. Phil. Trans. R. Soc. B 367, 3145–3157 (2012).Article 

Google Scholar 
Hou, E. et al. Divergent responses of primary production to increasing precipitation variability in global drylands. Glob. Change Biol. 27, 5225–5237 (2021).Article 
CAS 

Google Scholar 
Ritter, F., Berkelhammer, M. & Garcia-Eidell, C. Distinct response of gross primary productivity in five terrestrial biomes to precipitation variability. Commun. Earth Environ. 1, 34 (2020).Article 

Google Scholar 
Ridolfi, L., D’Odorico, P. & Laio, F. Noise-Induced Phenomena in the Environmental Sciences (Cambridge Univ. Press, 2011).Zeng, N. & Neelin, J. D. The role of vegetation–climate interaction and interannual variability in shaping the African savanna. J. Clim. 13, 2665–2670 (2000).Article 

Google Scholar 
Borgogno, F., D’Odorico, P., Laio, F. & Ridolfi, L. Mathematical models of vegetation pattern formation in ecohydrology. Rev. Geophysics 47, RG1005 (2009).Article 

Google Scholar 
van de Koppel, J. & Rietkerk, M. Spatial interactions and resilience in arid ecosystems. Am. Nat. 163, 113–121 (2004).Article 

Google Scholar 
Lefever, R. & Lejeune, O. On the origin of tiger bush. Bull. Math. Biol. 59, 263–294 (1997).Article 

Google Scholar 
Gherardi, L. A. & Sala, O. E. Enhanced precipitation variability decreases grass- and increases shrub-productivity. Proc. Natl Acad. Sci. USA 112, 12735–12740 (2015). Highlights the role of precipitation varibility in plant community composition in drylands.Article 
CAS 

Google Scholar 
Cleland, E. E. et al. Sensitivity of grassland plant community composition to spatial vs. temporal variation in precipitation. Ecology 94, 1687–1696 (2013).Article 

Google Scholar 
Good, S. P. & Caylor, K. K. Climatological determinants of woody cover in Africa. Proc. Natl Acad. Sci. USA 108, 4902–4907 (2011).Article 
CAS 

Google Scholar 
Lu, X., Wang, L., Pan, M., Kaseke, K. F. & Li, B. A multi-scale analysis of Namibian rainfall over the recent decade—comparing TMPA satellite estimates and ground observations. J. Hydrol. Reg. Stud. 8, 59–68 (2016).Article 

Google Scholar 
Franz, T., Caylor, K., Nordbotten, J., Rodriguez-Itubre, I. & Celia, M. An ecohydrological approach to predicting regional woody species distribution patterns in dryland ecosystems. Adv. Water Res. 33, 215–230 (2010).Article 

Google Scholar 
Knapp, A. K., Chen, A., Griffin-Nolan, R. J., Baur, L. E. & Smith, M. Resolving the Dust Bowl paradox of grassland responses to extreme drought. Proc. Natl Acad. Sci. USA 117, 201922030 (2020).Article 

Google Scholar 
Ukkola, A. M. et al. Reduced streamflow in water-stressed climates consistent with CO2 effects on vegetation. Nat. Clim. Change 6, 75–78 (2016).Article 

Google Scholar 
Austin, A. T. et al. Water pulses and biogeochemical cycles in arid and semiarid ecosystems. Oecologia 141, 221–235 (2004). Illustrates the close linkage between water pulses and biogeochemical cycles in drylands.Article 

Google Scholar 
Schwinning, S. & Sala, O. E. Hierarchy of responses to resource pulses in arid and semi-arid ecosystems. Oecologia 141, 211–220 (2004).Article 

Google Scholar 
Collins, S. L. et al. A multiscale, hierarchical model of pulse dynamics in arid-land ecosystems. Annu. Rev. Ecol. Evol. Syst. 45, 397–419 (2014).Article 

Google Scholar 
Barnard, R. L., Blazewicz, S. J. & Firestone, M. K. Rewetting of soil: revisiting the origin of soil CO2 emissions. Soil Biol. Biochem. 147, 107819 (2020).Article 
CAS 

Google Scholar 
Manzoni, S. et al. Rainfall intensification increases the contribution of rewetting pulses to soil heterotrophic respiration. Biogeosciences 17, 4007–4023 (2020).Article 
CAS 

Google Scholar 
Leizeaga, A., Meisner, A., Rousk, J. & Bååth, E. Repeated drying and rewetting cycles accelerate bacterial growth recovery after rewetting. Biol. Fertil. Soils 58, 365–374 (2022).Article 
CAS 

Google Scholar 
Gao, D. et al. Responses of soil nitrogen and phosphorus cycling to drying and rewetting cycles: a meta-analysis. Soil Biol. Biochem. 148, 107896 (2020).Article 
CAS 

Google Scholar 
Homyak, P. M., Allison, S. D., Huxman, T. E., Goulden, M. L. & Treseder, K. K. Effects of drought manipulation on soil nitrogen cycling: a meta-analysis. J. Geophys. Res. Biogeosci. 122, 3260–3272 (2017).Article 
CAS 

Google Scholar 
Delgado-Baquerizo, M. et al. Decoupling of soil nutrient cycles as a function of aridity in global drylands. Nature 502, 672–676 (2013).Article 
CAS 

Google Scholar 
Nippert, J. B., Knapp, A. K. & Briggs, J. M. Intra-annual rainfall variability and grassland productivity: can the past predict the future? Plant Ecol. 184, 65–74 (2006).Article 

Google Scholar 
Kaseke, K. F., Wang, L. & Seely, M. K. Nonrainfall water origins and formation mechanisms. Sci. Adv. 3, e1603131 (2017).Article 

Google Scholar 
Dawson, T. E. & Goldsmith, G. R. The value of wet leaves. N. Phytol. 219, 1156–1169 (2018).Article 

Google Scholar 
Feng, T. et al. Dew formation reduction in global warming experiments and the potential consequences. J. Hydrol. 593, 125819 (2021).Article 

Google Scholar 
Gerlein-Safdi, C. et al. Dew deposition suppresses transpiration and carbon uptake in leaves. Agric. For. Meteorol. 259, 305–316 (2018).Article 

Google Scholar 
Tomaszkiewicz, M., Abou Najm, M., Beysens, D., Alameddine, I. & El-Fadel, M. Dew as a sustainable non-conventional water resource: a critical review. Environ. Rev. 23, 425–442 (2015).Article 

Google Scholar 
Fessehaye, M. et al. Fog-water collection for community use. Renew. Sustain. Energy Rev. 29, 52–62 (2014).Article 

Google Scholar 
Kidron, G. J. Angle and aspect dependent dew and fog precipitation in the Negev desert. J. Hydrol. 301, 66–74 (2005).Article 

Google Scholar 
Chiodi, A. M., Potter, B. E. & Larkin, N. K. Multi-decadal change in western US nighttime vapor pressure deficit. Geophys. Res. Lett. 48, e2021GL092830 (2021).Article 

Google Scholar 
Tomaszkiewicz, M. et al. Projected climate change impacts upon dew yield in the Mediterranean basin. Sci. Total Environ. 566, 1339–1348 (2016).Article 

Google Scholar 
Walker, B. H., Ludwig, D., Holling, C. S. & Peterman, R. N. Stability of semi-arid savanna grazing systems. J. Ecol. 69, 473–498 (1981).Article 

Google Scholar 
Schlesinger, W. H. et al. Biological feedbacks in global desertification. Science 247, 1043–1048 (1990).Article 
CAS 

Google Scholar 
D’Odorico, P., Bhattachan, A., Davis, K., Ravi, S. & Runyan, C. Global desertification: drivers and feedbacks. Adv. Water Res. 51, 326–344 (2013).Article 

Google Scholar 
Reynolds, J. F. et al. Global desertification: building a science for dryland development. Science 316, 847–851 (2007). Highlights the loss of ecosystem services as a result of dryland desertification.Article 
CAS 

Google Scholar 
Eldridge, D. J. et al. Impacts of shrub encroachment on ecosystem structure and functioning: towards a global synthesis. Ecol. Lett. 14, 709–722 (2011). Provides a compehenseive analysis of the shrub enrochment effects on dryland functions.Article 

Google Scholar 
IPCC Special Report on Climate Change and Land (eds Shukla, P. R. et al.) (IPCC, 2019).Yang, H. et al. Tropical expansion driven by poleward advancing midlatitude meridional temperature gradients. J. Geophys. Res. Atmos. 125, e2020JD033158 (2020).Article 

Google Scholar 
Berghuijs, W. R., Woods, R. A. & Hrachowitz, M. A precipitation shift from snow towards rain leads to a decrease in streamflow. Nat. Clim. Change 4, 583–586 (2014).Article 

Google Scholar 
Ayyad, M. A., Fakhry, A. M. & Moustafa, A.-R. A. Plant biodiversity in the Saint Catherine area of the Sinai peninsula. Egypt. Biodivers. Conserv. 9, 265–281 (2000).Article 

Google Scholar 
Global Land Outlook 2017 (UNCCD, 2017).Van Ittersum, M. K. et al. Can sub-Saharan Africa feed itself? Proc. Natl Acad. Sci. USA 113, 14964–14969 (2016).Article 

Google Scholar 
Redo, D., Aide, T. M. & Clark, M. L. Vegetation change in Brazil’s dryland ecoregions and the relationship to crop production and environmental factors: Cerrado, Caatinga, and Mato Grosso, 2001–2009. J. Land Use Sci. 8, 123–153 (2013).Article 

Google Scholar 
Meyfroidt, P., Lambin, E. F., Erb, K.-H. & Hertel, T. W. Globalization of land use: distant drivers of land change and geographic displacement of land use. Curr. Opin. Environ. Sustain. 5, 438–444 (2013).Article 

Google Scholar 
Rulli, M. C., Saviori, A. & D’Odorico, P. Global land and water grabbing. Proc. Natl Acad. Sci. USA 110, 892–897 (2013).Article 
CAS 

Google Scholar 
Müller, M. F. et al. Impact of transnational land acquisitions on local food security and dietary diversity. Proc. Natl Acad. Sci. USA 118, e2020535118 (2021).Article 

Google Scholar 
Chiarelli, D. D. et al. Competition for water induced by transnational land acquisitions for agriculture. Nat. Commun. 13, 505 (2022).Article 
CAS 

Google Scholar 
Dell’Angelo, J., D’Odorico, P., Rulli, M. C. & Marchand, P. The tragedy of the grabbed commons: coercion and dispossession in the global land rush. World Dev. 92, 1–12 (2017).Article 

Google Scholar 
Rosa, L. et al. Potential for sustainable irrigation expansion in a 3 °C warmer climate. Proc. Natl Acad. Sci. USA 117, 29526–29534 (2020).Article 
CAS 

Google Scholar 
Wang, L. & D’Odorico, P. The limits of water pumps. Science 321, 36–37 (2008).Article 
CAS 

Google Scholar 
OECD-FAO Agricultural Outlook 2021–2030 (OECD and FAO, 2021).Qi, J., Xin, X., John, R., Groisman, P. & Chen, J. Understanding livestock production and sustainability of grassland ecosystems in the Asian Dryland Belt. Ecol. Process. 6, 22 (2017).Article 

Google Scholar 
Godde, C. M. et al. Global rangeland production systems and livelihoods at threat under climate change and variability. Environ. Res. Lett. 15, 044021 (2020).Article 

Google Scholar 
Herrero, M. et al. Exploring future changes in smallholder farming systems by linking socio-economic scenarios with regional and household models. Glob. Environ. Change 24, 165–182 (2014).Article 

Google Scholar 
Bannari, A., Morin, D., Bonn, F. & Huete, A. A review of vegetation indices. Remote Sens. Rev. 13, 95–120 (1995).Article 

Google Scholar 
Qiu, B. et al. Dense canopies browning overshadowed by global greening dominant in sparse canopies. Sci. Total Environ. 826, 154222 (2022).Article 
CAS 

Google Scholar 
Burrell, A. L., Evans, J. P. & Liu, Y. Detecting dryland degradation using time series segmentation and residual trend analysis (TSS-RESTREND). Remote Sens. Environ. 197, 43–57 (2017).Article 

Google Scholar 
Bastin, J.-F. et al. The extent of forest in dryland biomes. Science 356, 635–638 (2017).Article 
CAS 

Google Scholar 
Griffith, D. M. et al. Comment on ‘The extent of forest in dryland biomes’. Science 358, eaao1309 (2017).Article 

Google Scholar 
Teckentrup, L. et al. Assessing the representation of the Australian carbon cycle in global vegetation models. Biogeosciences 18, 5639–5668 (2021).Article 
CAS 

Google Scholar 
MacBean, N. et al. Dynamic global vegetation models underestimate net CO2 flux mean and inter-annual variability in dryland ecosystems. Environ. Res. Lett. 16, 094023 (2021). Highlights the often-neglected uncertainties in the prediction of dryland productivity.Paschalis, A. et al. Rainfall manipulation experiments as simulated by terrestrial biosphere models: where do we stand? Glob. Change Biol. 26, 3336–3355 (2020).Article 

Google Scholar 
Whitley, R. et al. A model inter-comparison study to examine limiting factors in modelling Australian tropical savannas. Biogeosciences 13, 3245–3265 (2016).Article 

Google Scholar 
Hartley, A. J., MacBean, N., Georgievski, G. & Bontemps, S. Uncertainty in plant functional type distributions and its impact on land surface models. Remote Sens. Environ. 203, 71–89 (2017).Article 

Google Scholar 
MacBean, N. et al. Testing water fluxes and storage from two hydrology configurations within the ORCHIDEE land surface model across US semi-arid sites. Hydrol. Earth Syst. Sci. 24, 5203–5230 (2020).Article 
CAS 

Google Scholar 
Burrell, A., Evans, J., De & Kauwe, M. Anthropogenic climate change has driven over 5 million km2 of drylands towards desertification. Nat. Commun. 11, 3853 (2020).Article 
CAS 

Google Scholar 
De Kauwe, M. G., Medlyn, B. E. & Tissue, D. T. To what extent can rising [CO2] ameliorate plant drought stress? N. Phytol. 231, 2118–2124 (2021).Article 

Google Scholar 
Zhu, Z. et al. Greening of the Earth and its drivers. Nat. Clim. Change 6, 791–795 (2016).Article 
CAS 

Google Scholar 
Bernacchi, C. J. & VanLoocke, A. Terrestrial ecosystems in a changing environment: a dominant role for water. Annu. Rev. Plant Biol. 66, 599–622 (2015).Article 
CAS 

Google Scholar 
Roderick, M. L., Greve, P. & Farquhar, G. D. On the assessment of aridity with changes in atmospheric CO2. Water Resour. Res. 51, 5450–5463 (2015).Article 
CAS 

Google Scholar 
Anderegg, W. R., Trugman, A. T., Bowling, D. R., Salvucci, G. & Tuttle, S. E. Plant functional traits and climate influence drought intensification and land–atmosphere feedbacks. Proc. Natl Acad. Sci. USA 116, 14071–14076 (2019).Article 
CAS 

Google Scholar 
Zhou, S. et al. Land–atmosphere feedbacks exacerbate concurrent soil drought and atmospheric aridity. Proc. Natl Acad. Sci. USA 116, 18848–18853 (2019).Article 
CAS 

Google Scholar 
Dai, A. Increasing drought under global warming in observations and models. Nat. Clim. Change 3, 52–58 (2013).Article 

Google Scholar 
Abdelmoaty, H. M., Papalexiou, S. M., Rajulapati, C. R. & AghaKouchak, A. Biases beyond the mean in CMIP6 extreme precipitation: a global investigation. Earth’s Future 9, e2021EF002196 (2021).Article 

Google Scholar 
Dunkerley, D. L. Light and low-intensity rainfalls: a review of their classification, occurrence, and importance in landsurface, ecological and environmental processes. Earth Sci. Rev. 214, 103529 (2021).Article 

Google Scholar 
Zhu, Y. & Yang, S. Interdecadal and interannual evolution characteristics of the global surface precipitation anomaly shown by CMIP5 and CMIP6 models. Int. J. Climatol. 41, E1100–E1118 (2021).Article 

Google Scholar 
Cuthbert, M. O. et al. Observed controls on resilience of groundwater to climate variability in sub-Saharan Africa. Nature 572, 230–234 (2019).Article 
CAS 

Google Scholar 
Miguez-Macho, G. & Fan, Y. Spatiotemporal origin of soil water taken up by vegetation. Nature 598, 624–628 (2021).Article 

Google Scholar 
Potapov, P. et al. Global maps of cropland extent and change show accelerated cropland expansion in the twenty-first century. Nat. Food 3, 19–28 (2022).Article 

Google Scholar 
Trabucco, A. & Zomer, R. Global aridity index and potential evapotranspiration (ET0) climate database v.2. Figshare https://doi.org/10.6084/m9.figshare.7504448.v4 (2019).Paschalis, A., Fatichi, S., Katul, G. G. & Ivanov, V. Y. Cross-scale impact of climate temporal variability on ecosystem water and carbon fluxes. J. Geophys. Res. Biogeosci. 120, 1716–1740 (2015).Article 

Google Scholar  More

DeVault, T. L., Rhodes, O. E. Jr. & Shivik, J. A. Scavenging by vertebrates: Behavioral, ecological, and evolutionary perspectives on an important energy transfer pathway in terrestrial ecosystems. Oikos 102, 225–234 (2003).
Google Scholar 
Wilson, E. E. & Wolkovich, E. M. Scavenging: How carnivores and carrion structure communities. Trends Ecol. Evol. 26, 129–135 (2011).PubMed 

Google Scholar 
Barton, P. S., Cunningham, S. A., Lindenmayer, D. B. & Manning, A. D. The role of carrion in maintaining biodiversity and ecological processes in terrestrial ecosystems. Oecologia 171, 761–772 (2013).ADS 
PubMed 

Google Scholar 
Benbow, M. E. et al. Necrobiome framework for bridging decomposition ecology of autotrophically and heterotrophically derived organic matter. Ecol. Monogr. 89, e01331 (2019).
Google Scholar 
Carter, D. O., Yellowlees, D. & Tibbett, M. Cadaver decomposition in terrestrial ecosystems. Naturwissenschaften 94, 12–24 (2007).ADS 
PubMed 
CAS 

Google Scholar 
Bump, J. K., Peterson, R. O. & Vucetich, J. A. Wolves modulate soil nutrient heterogeneity and foliar nitrogen by configuring the distribution of ungulate carcasses. Ecology 90, 3159–3167 (2009).PubMed 

Google Scholar 
Beasley, J. C., Olson, Z. H. & DeVault, T. L. Ecological role of vertebrate scavengers. In Carrion Ecology, Evolution, and Their Applications (eds Benbow, E. M. et al.) 107–127 (CRC Press, 2015).
Google Scholar 
DeVault, T. L., Brisbin, I. L. Jr. & Rhodes, O. E. Jr. Factors influencing the acquisition of rodent carrion by vertebrate scavengers and decomposers. Can. J. Zool. 82, 502–509 (2004).
Google Scholar 
Moleón, M., Sánchez-Zapata, J. A., Sebastián-González, E. & Owen-Smith, N. Carcass size shapes the structure and functioning of an African scavenging assemblage. Oikos 124, 1391–1403 (2015).
Google Scholar 
Turner, K. L., Abernethy, E. F., Conner, L. M., Rhodes, O. E. & Beasley, J. C. Abiotic and biotic factors modulate carrion fate and vertebrate scavenging communities. Ecology 98, 2413–2424 (2017).PubMed 

Google Scholar 
Selva, N. The Role of Scavenging in the Predator Community of Białowieża Primeval Forest (E Poland) (Univeristy of Sevilla, 2004).
Google Scholar 
Moleón, M. et al. Carnivore carcasses are avoided by carnivores. J. Anim. Ecol. 86, 1179–1191 (2017).PubMed 

Google Scholar 
Selva, N. & Fortuna, M. A. The nested structure of a scavenger community. Proc. R. Soc. B Biol. Sci. 274, 1101–1108 (2007).
Google Scholar 
Abernethy, E. F. et al. Carcasses of invasive species are predominantly utilized by invasive scavengers in an island ecosystem. Ecosphere 7, e01496 (2016).
Google Scholar 
DeVault, T. L., Seamans, T. W., Linnell, K. E., Sparks, D. W. & Beasley, J. C. Scavenger removal of bird carcasses at simulated wind turbines: Does carcass type matter?. Ecosphere 8, e01994 (2017).
Google Scholar 
Olson, Z. H., Beasley, J. C. & Rhodes, O. E. Carcass type affects local scavenger guilds more than habitat connectivity. PLoS ONE 11, e0147798 (2016).PubMed 
PubMed Central 

Google Scholar 
Muñoz-Lozano, C. et al. Avoidance of carnivore carcasses by vertebrate scavengers enables colonization by a diverse community of carrion insects. PLoS ONE 14, e0221890 (2019).PubMed 
PubMed Central 

Google Scholar 
Peers, M. J. L. et al. Vertebrate scavenging dynamics differ between carnivore and herbivore carcasses in the northern boreal forest. Ecosphere 12, e03691 (2021).
Google Scholar 
Pfennig, D. W. Effect of predator-prey phylogenetic similarity on the fitness consequences of predation: A trade-off between nutrition and disease?. Am. Nat. 155, 335–345 (2000).PubMed 

Google Scholar 
Polis, G. A. The evolution and dynamics of intraspecific predation. Annu. Rev. Ecol. Syst. 12, 225–251 (1981).
Google Scholar 
Elgar, M. A. & Crespi, B. J. Cannibalism: Ecology and Evolution Among Diverse Taxa (Oxford University Press, 1992).
Google Scholar 
Fouilloux, C., Ringler, E. & Rojas, B. Cannibalism. Curr. Biol. 29, R1295–R1297 (2019).PubMed 
CAS 

Google Scholar 
Oliva-Vidal, P., Tobajas, J. & Margalida, A. Cannibalistic necrophagy in red foxes: Do the nutritional benefits offset the potential costs of disease transmission?. Mamm. Biol. https://doi.org/10.1007/s42991-021-00184-5 (2021).Article 

Google Scholar 
Mateo, J. M. Recognition systems and biological organization: The perception component of social recognition. Ann. Zool. Fenn. 41, 729745 (2004).
Google Scholar 
Dangles, O., Irschick, D., Chittka, L. & Casas, J. Variability in sensory ecology: Expanding the bridge between physiology and evolutionary biology. Q. Rev. Biol. 84, 51–74 (2009).PubMed 

Google Scholar 
Janzen, D. H. Why fruits rot, seeds mold, and meat spoils. Am. Nat. 111, 691–713 (1977).CAS 

Google Scholar 
Ogada, D. L., Torchin, M. E., Kinnaird, M. F. & Ezenwa, V. O. Effects of vulture declines on facultative scavengers and potential implications for mammalian disease transmission. Conserv. Biol. 26, 453–460 (2012).PubMed 
CAS 

Google Scholar 
Gonzálvez, M., Martínez-Carrasco, C., Sánchez-Zapata, J. A. & Moleón, M. Smart carnivores think twice: red fox delays scavenging on conspecific carcasses to reduce parasite risk. Appl. Anim. Behav. Sci. 243, 105462 (2021).PubMed 
PubMed Central 

Google Scholar 
Selva, N., Jedrzejewska, B., Jedrzejewski, W. & Wajrak, A. Scavenging on European bison carcasses in Bialowieza Primeval Forest (eastern Poland). Écoscience 10, 303–311 (2003).
Google Scholar 
Carr, W. J., Hirsch, J. T., Campellone, B. E. & Marasco, E. Some determinants of a natural food aversion in Norway rats. J. Comp. Physiol. Psychol. 93, 899–906 (1979).
Google Scholar 
Gaynor, K. M., Brown, J. S., Middleton, A. D., Power, M. E. & Brashares, J. S. Landscapes of fear: Spatial patterns of risk perception and response. Trends Ecol. Evol. 34, 355–368 (2019).PubMed 

Google Scholar 
Moleón, M. & Sánchez-Zapata, J. A. The role of carrion in the landscapes of fear and disgust: a review and prospects. Diversity 13, 28 (2021).
Google Scholar 
Hartig, F. DHARMa: Residual Diagnostics for Hierarchical (Multi-Level/Mixed) Regression Models (Springer, 2022).
Google Scholar 
Hothorn, T., Winell, H., Hornik, K., van de Wiel, M. A. & Zeileis, A. Coin: Conditional Inference Procedures in a Permutation Test Framework (Springer, 2021).
Google Scholar 
Owings, C. G., Gilhooly, W. P. & Picard, C. J. Blow fly stable isotopes reveal larval diet: A case study in community level anthropogenic effects. PLoS ONE 16, e0249422 (2021).PubMed 
PubMed Central 
CAS 

Google Scholar 
Matuszewski, S., Konwerski, S., Frątczak, K. & Szafałowicz, M. Effect of body mass and clothing on decomposition of pig carcasses. Int. J. Legal Med. 128, 1039–1048 (2014).PubMed 
PubMed Central 

Google Scholar 
Cunningham, C. X. et al. Top carnivore decline has cascading effects on scavengers and carrion persistence. Proc. R. Soc. B. 285, 1–10 (2018).
Google Scholar 
Huang, S., Bininda-Emonds, O. R. P., Stephens, P. R., Gittleman, J. L. & Altizer, S. Phylogenetically related and ecologically similar carnivores harbour similar parasite assemblages. J. Anim. Ecol. 83, 671–680 (2014).PubMed 

Google Scholar 
Hill, D. E., Chirukandoth, S. & Dubey, J. P. Biology and epidemiology of Toxoplasma gondii in man and animals. Anim. Health Res. Rev. 6, 41–61 (2005).PubMed 

Google Scholar 
Hill, D. E. et al. Trichinella murrelli in scavenging mammals from south-central Wisconsin, USA. J. Wildl. Dis. 44, 629–635 (2008).PubMed 
CAS 

Google Scholar 
Sandfoss, M., DePerno, C., Patton, S., Flowers, J. & Kennedy-Stoskopf, S. Prevalence of antibody to Toxoplasma gondii and Trichinella spp. in feral pigs (Sus scrofa) of eastern North Carolina. J. Wildl. Dis. 47, 338–343 (2011).PubMed 

Google Scholar 
Butler, J. R. A., du Toit, J. T. & Bingham, J. Free-ranging domestic dogs (Canis familiaris) as predators and prey in rural Zimbabwe: Threats of competition and disease to large wild carnivores. Biol. Conserv. 115, 369–378 (2004).
Google Scholar 
Mendenhall, I. H. et al. Evidence of canine parvovirus transmission to a civet cat (Paradoxurus musangus) in Singapore. One Health 2, 122–125 (2016).PubMed 
PubMed Central 

Google Scholar 
Han, B. A., Castellanos, A. A., Schmidt, J. P., Fischhoff, I. R. & Drake, J. M. The ecology of zoonotic parasites in the Carnivora. Trends Parasitol. 37, 1096–1110 (2021).PubMed 

Google Scholar 
Malmberg, J. L., White, L. A. & VandeWoude, S. Bioaccumulation of pathogen exposure in top predators. Trends Ecol. Evol. 36, 411–420 (2021).PubMed 

Google Scholar 
Mammal Diversity Database (Version 1.9). https://doi.org/10.5281/zenodo.6407053 (2022).Han, B. A., Kramer, A. M. & Drake, J. M. Global patterns of zoonotic disease in mammals. Trends Parasitol. 32, 565–577 (2016).PubMed 
PubMed Central 

Google Scholar 
Digby, Z. et al. Evolutionary loss of inflammasomes in the Carnivora and implications for the carriage of zoonotic infections. Cell Rep. 36, 109614 (2021).PubMed 
PubMed Central 
CAS 

Google Scholar 
Buck, J. C., Weinstein, S. B. & Young, H. S. Ecological and evolutionary consequences of parasite avoidance. Trends Ecol. Evol. 33, 619–632 (2018).PubMed 
CAS 

Google Scholar 
Hart, B. L. & Hart, L. A. How mammals stay healthy in nature: The evolution of behaviours to avoid parasites and pathogens. Philos. Trans. R. Soc. B 373, 20170205 (2018).
Google Scholar 
Brown, C. J. & Plug, I. Food choice and diet of the bearded vulture Gypaetus barbatus in southern Africa. S. Afr. J. Zool. 25, 169–177 (1990).
Google Scholar 
Rossi, L., Interisano, M., Deksne, G. & Pozio, E. The subnivium, a haven for Trichinella larvae in host carcasses. Int. J. Parasitol. Parasit. Wildl. 8, 229–233 (2019).
Google Scholar 
Micozzi, M. S. Experimental study of postmortem change under field conditions: Effects of freezing, thawing, and mechanical injury. J. Forensic Sci. 31, 953–961 (1986).PubMed 
CAS 

Google Scholar 
Mayntz, D. & Toft, S. Nutritional value of cannibalism and the role of starvation and nutrient imbalance for cannibalistic tendencies in a generalist predator. J. Anim. Ecol. 75, 288–297 (2006).PubMed 

Google Scholar 
Margalida, A. Bearded vultures (Gypaetus barbatus) prefer fatty bones. Behav. Ecol. Sociobiol. 63, 187–193 (2008).
Google Scholar 
Parmenter, R. R. & MacMahon, J. A. Carrion decomposition and nutrient cycling in a semiarid shrub–steppe ecosystem. Ecol. Monogr. 79, 637–661 (2009).
Google Scholar 
Evans, B. E., Mosby, C. E. & Mortelliti, A. Assessing arrays of multiple trail cameras to detect North American mammals. PLoS ONE 14, 1–18 (2019).
Google Scholar 
Ivan, J. S. & Newkirk, E. S. CPW Photo Warehouse: A custom database to facilitate archiving, identifying, summarizing and managing photo data collected from camera traps. Methods Ecol. Evol. 7, 499–504 (2016).
Google Scholar 
Therneau, T. M. & Grambsch, P. M. Modeling Survival Data: Extending the Cox Model (Springer, 2000).MATH 

Google Scholar 
Kassambara, A., Kosinski, M. & Biecek, P. survminer: Drawing Survival Curves Using ‘ggplot2’ (Springer, 2020).
Google Scholar 
Nenadic, O. & Greenacre, M. Correspondence analysis in R, with two- and three-dimensional graphics: the ca package. J. Stat. Softw. 20, 1–13 (2007).
Google Scholar 
Kassambara, A. & Mundt, F. factoextra: Extract and Visualize the Results of Multivariate Data Analyses (Springer, 2020).
Google Scholar 
Greenacre, M. The contributions of rare objects in correspondence analysis. Ecology 94, 241–249 (2013).PubMed 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2022).
Google Scholar  More

Measurement of colony strength traits and Varroa destructor loadHives located in the natural area (henceforth natural hives or colonies) weighed the most (hive weight, Kruskal–Wallis test, p  More