Ecology

Geography and climatology of study areaThe area of study, Ghana, is on the coastal edge of tropical West African, bounded in latitude 4.5° N and 11.5° N and longitude 3.5° W and 1.5° E, and characterized by a tropical monsoon climate system23,24. Figure 1 shows map of the study area indicating the selected twenty two (22) sunshine measurement stations distributed across the four main climatological zones and Table 1 summarizes the geographical positions of selected stations.Figure 1Adapted from Asilevi27.Map of the study area showing all twenty two (22) synoptic stations distributed in four main climatological zones countrywide.Full size imageTable 1 Geographical position and elevation for study sites.Full size tableAtmospheric clarity over the area is closely connected to cloud amount distribution and rainfall activities, largely determined by the oscillatory migration of the Inter-Tropical Discontinuity (ITD), accounting for the West African Monsoon (WAM)25,26.Owing to the highly variable spatiotemporal distribution of cloud amount vis-à-vis rainfall activities, resulting in contrasting climatic conditions in different parts of the region, the country is partitioned by the Ghana Meteorological Agency (GMet) into four main agro-ecological zones namely, the Savannah, Transition, Forest and Coastal zones as shown in Fig. 123. As a result, the region experiences an estimated Global solar radiation (GSR) intensity peaks in April–May and then in October–November, with the highest monthly average of 22 MJm−2 day−1 over the savannah climatic zone and the lowest monthly average of 13 MJm−2 day−1 over the forest climatic zone27.Research datasetsGround-based measurement dataDaily sunshine duration measurement datasets (n) spanning 1983–2018 where derived for estimating Global solar radiation (GSR). The measurements were taken by the Campbell-Stokes sunshine recorder, mounted at the 22 stations shown in Fig. 1, under unshaded conditions to ensure optimum sunlight exposure. The device concentrates sunlight onto a thin strip of sunshine card, which causes a burnt line representing the total period in hours during which sunshine intensity exceeds 120.0 Wm−2 according to World Meteorological Organization (WMO) recommendations27. The as-received daily records were quality control checked by ensuring 0 ≤ n ≤ N, where N is the astronomical day length representing the possible maximum duration of sunshine in hours determined by Eq. 1 from the latitude (ϕ) of the site of interest and the solar declination (δ) computed by Eq. 227:$$ {text{N}} = frac{2}{15}cos^{ – 1} left[ { – tan phi tan {updelta }} right] $$
(1)
$$ {updelta } = 23.45sin left[ {360^{{text{o}}} times frac{{284 + {text{J}}}}{365}} right] $$
(2)
where J represents the number for the Julian day of the year (first January is 1 and second January is 2).NASA-POWER Global solar radiation (GSR) reanalysis dataThe satellite-based Global solar radiation (GSR) dataset for specific longitudes and latitudes of all 22 stations, assessed in the study, were retrieved from the National Aeronautics and Space Administration-Prediction of Worldwide Energy Resources (NASA-POWER) reanalysis repository based on the Modern Era Retrospective-Analysis for Research and Applications (MERRA-2) assimilation model products, developed from Surface Radiation Budget, and spanning equal study period (1983–2018). The datasets are accessible on a daily and monthly temporal resolution scales at 0.5° × 0.5° spatial coverage via a user friendly web-based mapping portal: https://power.larc.nasa.gov/data-access-viewer/17. The advantage of the NASA-POWER reanalysis GSR, is the wide spatial coverage, and thus can be used to develop a high spatial resolution of solar radiation across the study area.The POWER Project analyzes, synthesizes and makes available surface radiation related parameters on a global scale, primarily from the World Climate Research Programme (WCRP), Global Energy and Water cycle Experiment (GEWEX), Surface Radiation Budget (SRB) project (Version 2.9), the Clouds and the Earth’s Radiant Energy System (CERES), FLASHFlux (Fast Longwave and Shortwave Radiative Fluxes from CERES and MODIS), and the Global Modeling and Assimilation Office (GMAO)17. Table 2 shows the source satellites and the corresponding temporal coverage used in the development of NASA-POWER GSR products.Table 2 Satellites providing the NASA-POWER GSR datasets20.Full size tableThe monthly average NASA-POWER all-sky shortwave surface radiation reanalysis products are statistically validated, showing reasonable biases of − 6.6–13%, against a global network of surface radiation measurement metadata in an integrated database from the Baseline Surface Radiation Network (BSRN) of the World Radiation Monitoring Center (WRMC)20,22. The datasets are widely used in renewable energy application16,22, agricultural modelling of crop yields28, crop simulation exercises29, and plant disease modelling30.Furthermore, in order to assess the suitability of the NASA-POWER surface solar radiation products for the study area, a synthetic sunshine duration based Global solar radiation (GSR) is developed from the Angstrom-Prescott sunshine duration model by Eq. 3 for comparisons27.$$ {text{GSR}} = left[ {{text{a}} + {text{b}}frac{{text{n}}}{{text{N}}}} right]{text{H}}_{{text{o}}} $$
(3)
were Ho (kWhm−2 day−1) is the daily extraterrestrial solar radiation on an horizontal surface, n is the daily sunshine duration measurements obtained from the Ghana Meteorological Agency (GMet), and N is the maximum possible daily sunshine duration or the day length in hours determined by Eq. 1. Generalized regression constants a = 0.25 and b = 0.5 for the study area were determined by Asilevi27 from experimental radiometric data based on correlation regression analysis between atmospheric clarity index (GSR/Ho) and atmospheric cloudlessness index (n/N), for estimating solar radiation over the study area, and compared with other satellite data retrieved from the National Renewable Energy Laboratory (NREL) and the German Aerospace Centre (DLR)27. Ho was calculated from astronomical parameters by Eq. 4:$$ {text{H}}_{0} = frac{{24{ } cdot { }60}}{pi } cdot {text{G}}_{{{text{sc}}}} cdot {text{d}}_{{text{r}}} left[ {omega_{{text{s}}} sin varphi sin delta + cos varphi cos delta sin omega_{{text{s}}} } right] $$
(4)
where Gsc is the Solar constant in MJm−2 min−1, dr is the relative Earth–Sun distance in meters (m), (omega_{s}) is the sunset hour angle (angular distance between the meridian of the observer and the meridian whose plane contains the sun), (delta) is the angle of declination in degrees (°) and (varphi) is the local latitude. A detailed presentation of the calculation was published in a previous work27.Statistical assessment analysisFor the purpose of assessing the NASA-POWER derived monthly mean GSR (GSRn) datasets in comparison with the estimated Global Solar Radiation (GSRe) datasets used in this paper, the following deviation and correlation methods in Eqs. 5–11, each showing a complimentary result were used: Standard deviation (({upsigma })), residual error (RE), Root mean square error (RMSE), Mean bias error (MBE), Mean percentage error (MPE), Pearson’s correlation coefficient (r), and Willmott index of agreement (d) for n observations31,32,33,34,35. GSRe, GSRn, and RE represent the estimated GSR, NASA-POWER GSR, and the residual error between GSRe and GSRn respectively. A positive RE indicates that sunshine-based estimated GSR is larger than the NASA-POWER reanalysis dataset, while a negative RE indicates that sunshine-based estimated GSR is smaller than the NASA-POWER reanalysis dataset. The arithmetic mean of any dataset is µ.The standard deviation (({upsigma })) was used to check the upper and lower limits of distribution around the mean deviations between GSRe and GSRn in order to ascertain violations between both datasets33. The RMSE is a standard statistical metric to quantify error margins in meteorology and climate research studies, and by definition is always positive, representing zero in the ideal case, plus a smaller value signifying a good marginal deviation31. The MBE is a good indicator for under-or overestimation in observations, with MBE values closest to zero being desirable. The MPE further indicates the percentage deviation between the GSRe and GSRn individual datasets35.$$ {upsigma } = sqrt {frac{1}{{{text{n}} – 1}}mathop sum limits_{{{text{i}} = 1}}^{{text{n}}} left( {{text{GSR}} – {upmu }} right)^{2} } $$
(5)
$$ {text{RE}} = {text{GSR}}_{{text{e}}} – {text{GSR}}_{{text{n}}} $$
(6)
$$ {text{RMSE}} = sqrt {frac{1}{{text{n}}}mathop sum limits_{{{text{i}} = 1}}^{{text{n}}} left( {{text{RE}}} right)^{2} } $$
(7)
$$ {text{MBE}} = frac{1}{{text{n}}}mathop sum limits_{{{text{i}} = 1}}^{{text{n}}} left( {{text{RE}}} right) $$
(8)
$$ {text{MPE}} = frac{1}{{text{n}}}mathop sum limits_{{{text{i}} = 1}}^{{text{n}}} left( {frac{{{text{RE}}}}{{{text{GSR}}_{{text{e}}} }} times 100{text{% }}} right) $$
(9)
$$ {text{r}} = frac{{mathop sum nolimits_{{{text{i}} = 1}}^{{text{n}}} left( {{text{GSR}}_{{text{e}}} – {upsigma }_{{text{e}}} } right)left( {{text{GSR}}_{{text{n}}} – {upsigma }_{{text{n}}} } right)}}{{left( {{text{n}} – 1} right){upsigma }_{{text{e}}} {upsigma }_{{text{n}}} }} $$
(10)
$$ {text{d}} = 1 – left[ {frac{{mathop sum nolimits_{{{text{i}} = 1}}^{{text{n}}} left( {{text{GSR}}_{{text{e}}} – {text{GSR}}_{{text{n}}} } right)^{2} }}{{mathop sum nolimits_{{{text{i}} = 1}}^{{text{n}}} left( {left| {{text{GSR}}_{{text{e}}} – {text{GSR}}_{{{text{nave}}}} left| + right|{text{GSR}}_{{text{n}}} – {text{GSR}}_{{{text{nave}}}} } right|} right)^{2} }}} right] $$
(11)
Further, as with other statistical studies in meteorology36, the Pearson’s correlation coefficient (r) was used to quantify the strength of correlation between GSRe and GSRn. Finally, the Willmott index of agreement (d) commonly used in meteorological literature computed from Eq. 7 is used to assess the degree of GSRe/GSRn agreement34. More

Soil sample collectionSoil samples were collected from three different crude oil reservoirs et al. Faisaliyyah, Al Sina’iyah, and Ghubairah located in Riyadh, Saudi Arabia. Briefly, 400 g of soil samples were collected at 0–10 cm depth, under aseptic conditions. Samples were sieved by 2.5 mm pore size sieves, homogenized, and stored at 4ºC until use.Sources of different hydrocarbonsDifferent samples of crude oil, kerosene, diesel, and used oil were collected in sterile flasks from the tankers of Saudi Aramco Company (Dammam, Saudi Arabia). Additionally, another flask was prepared by mixing 1% of each oil in MSM liquid media to make up the mixed oil. The oil samples were sterilized by Millex® Syringe Filters (Merck Millipore co., Burlington, MA, United States) and stored at 4 °C for further usage.Isolation and identification of fungal speciesThe fungal species in the soil contaminated by crude oil were identified using the dilution method. Briefly, 10% of each soil sample was dissolved in distilled water and vortexed thoroughly. Then, 0.2 ml of each sample was cultured on a sterile PDA plate incubated at 28 °C for three days until the growth of different fungal colonies. Carefully, each colony was isolated, re-cultured on new PDA McCartney bottles of PDA slant, and incubated at 28 °C for three days. The fungi were identified microscopically using standard taxonomic keys based on typical mycelia growth and morphological characteristics provided in the mycological keys54. Besides, the taxonomy of the isolated yeast strains was confirmed by the API 20 C AUX kit (Biomerieux Corp., Marcy-l’Étoile, France) (data not shown). The morphology of pure cultures was tested and identified under a light microscope as described before55.The incidence of each strain was calculated as follows:$$ Incidence ;(% ) = frac{{{text{Number }};{text{of }};{text{samples }};{text{showed }};{text{microbial }};{text{growth}}}}{{{text{Total }};{text{samples}}}} times 100 $$Hydrocarbon tolerance testThe growth rate of isolated strains was tested in a liquid medium of MSM mixed with 1% of either crude oil, used oil, diesel, kerosene, or mixed oil. Furthermore, a control sample of MSM liquid medium without any of the oils tested and all culture media were autoclaved at 121 °C for 30 min. After cooling, 1 ml of each isolate was inoculated with one of the above mixtures and incubated at 25 °C on an orbital shaker. The growth rate was measured every three days for a month for each treatment versus the control. All experiments were performed in triplicates.Scanning electron microscopy (SEM)The morphology of different strains of the isolated fungi was tested by SEM, as previously described56, with some modifications. Briefly, 1 ml of each growing strain, in the liquid media, was centrifuged at the maximum speed (14,000 rpm) for 1 min, followed by fixation with 2.5% glutaraldehyde, and overnight incubation at 5 °C. Later, the sample was pelleted, washed with distilled water, then dehydrated with different ascending concentrations of ethanol (30, 50, 70, 90, 100 (v/v)) for 15 min at room temperature. Finally, samples were examined in the Prince Naif Research Centre (King Saud University, Riyadh, Saudi Arabia) by the JEOL JEM-2100 microscope (JEOL, Peabody, MA, United States), according to the manufacturer instructions.Crude oil degradation assayA modified version of the DCPIP assay57 was employed to assess the oil-degrading ability of the fungal isolates. For each strain, 100 ml of the autoclaved MSM was mixed with 1% (V/V) of one of the hydrocarbons (crude oil, used oil, diesel, kerosene, or mixed oil), 0.1% (v/v) of Tween 80, and 0.6 mg/mL of the redox indicator (DCPIP). Then, 1–2 ml of different fungi growing in liquid media (24–48 h) add to the Crude Oil Degradation media, prepared previously, and incubated for two weeks in a shaking incubator at 25 °C. All flasks were covered and protected from light, aeration, or temperature exchanges to reduce the effects of oil weathering (evaporation, photooxidation). The surfactant Tween 80 was used for bio-stimulation and acceleration of the biosurfactant production by increasing metabolism58. A non-inoculated Crude Oil Degradation media was used as the negative control. Afterward, the colorimetric analysis for the change in DCPIP color was estimated, spectrophotometrically, at 420 nm. All experiments were performed in triplicates.Preparation of cell-free supernatant (CFS)To prepare the Cell-Free Supernatant (CFS), all isolates were grown in MSM broth medium with 1% of either crude oil, used oil, diesel, kerosene, or mixed oil for 30 days in a shaking incubator at 25 °C. After incubation, the cells were removed by centrifugation at 10,000 rpm for 30 min at 4 °C. The supernatant (CFS) was collected and filter-sterilized with a 0.45 μm pore size sterile membrane. CFS was screened for the production of different biosurfactants. All the experiments were carried out in triplicates, and the average values were calculated.Drop-Collapse assayThe Drop-Collapse assay was performed as previously described9, with some modifications. 100 µl of crude oil was applied on glass slides, then 10 µl of each CFS was added to the center of the slide surface and incubated for a minute at room temperature. The slides were imaged by a light microscope using the 10X objective lenses. The spreading on the soil surface was scored by either « + » to indicate the level of positive spreading, biosurfactant production, or «—» for negative spreading. Biosurfactant production was considered positive at the drop diameter ≥ 0.5 mm, compared to the negative control (treated with distilled water).Oil spreading assayAn amount of 20 ml of water was added to the Petri plate (size of 100 mm) and mixed with 20 µl of crude oil or mixed oil, which created a thin layer on the water surface. Then, 10 µl of CFS was delivered onto the surface of the oil, and the clear zone surrounding the CFS drop was observed. The results were compared to the negative control (without CFS) and positive control of 1% SDS41. We have measured the clear zones diameter from images and calculate the actual values in regards to the diameter of the Petri dish (10 cm). The assay was performed in triplicates.Emulsification activity assayThe emulsification activity of each isolate was assessed by mixing equal volumes of MSM broth medium of each isolate with different oils in separate tubes. The samples were homogenized by vortex at high speed for two minutes at room temperature (25 °C) and allowed to settle for 24 h. The tests were performed in duplicate. Then, the emulsification index was calculated as follows59:$$ Emulsification; activity; left( % right) = frac{{{text{Height }};{text{of }};{text{emulsion }};{text{layer}}}}{{{text{Total }};{text{height}}}} times 100 $$Recovery of biosurfactantsThe recovery of biosurfactants from CFS was tested through different assays:Acid precipitation assay3 ml of each CFS was adjusted by 6 N HCl to pH 2 and incubated for 24 h at 4 °C. Later, equal volumes of chloroform/methanol mixture (2:1 v/v) were added to each tube, vortexed, and incubated overnight at room temperature. Afterward, the samples were centrifuged for 30 min at 10,000 rpm (4 °C), the precipitate (Light brown colored paste) was air-dried in a fume hood, and weighed53.Solvent extraction assayThe CFS containing biosurfactant was treated with a mixture of extraction solvents (equal volumes of methanol, chloroform, and acetone). Then, the new mixture was incubated in a shaking incubator at 200 rpm, 30 °C for 5 h. The precipitate was separated into two layers, in which the lower layer (White) was isolated, dried, weighed, and stored60.Ammonium sulfate precipitation assayThe CFS containing biosurfactant was precipitated with 40% (w/v) ammonium sulfate and incubated overnight at 4 °C. The samples were centrifuged at 10,000 rpm for 30 min (4 °C). The precipitate was collected and extracted with an amount of acetone equal to the volume of the supernatant. After centrifugation, the precipitate (Creamy-white) was isolated, air-dried in a fume hood, and weighed53.Zinc sulfate precipitation methodSimilarly, 40% (w/v) zinc sulfate was mixed with the CFS containing biosurfactant. Then, the mixture was incubated at 4 °C, overnight. The precipitate (Light Brown) was collected by centrifugation at 10,000 rpm for 30 min (4 °C), air-dried in a fume hood, and weighed53.Statistical analysisAll experiments were performed in triplicate, and the results were expressed as the mean values ± standard deviation (SD). One-way ANOVA and Dunnett’s tests were used to estimate the significance levels at P  More

Birenboim, A. The influence of urban environments on our subjective momentary experiences. Environ. Plan. B-Urban Anal. CIty Sci. 45, 915–932. https://doi.org/10.1177/2399808317690149 (2018).Article 

Google Scholar 
Flores, A., Pickett, S. T. A., Zipperer, W. C., Pouyat, R. V. & Pirani, R. Adopting a modern ecological view of the metropolitan landscape: The case of a greenspace system for the New York City region. Landsc. Urban Plan. 39, 295–308. https://doi.org/10.1016/S0169-2046(97)00084-4 (1998).Article 

Google Scholar 
Weijs-Perrée, M., Dane, G., Berg, P. V. D. & Dorst, M. V. A multi-level path analysis of the relationships between the momentary experience characteristics, satisfaction with urban public spaces, and momentary- and long-term subjective wellbeing. Int. J. Environ. Res. Public Health. https://doi.org/10.3390/ijerph16193621 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Paulin, M. J. et al. Application of the natural capital model to assess changes in ecosystem services from changes in green infrastructure in Amsterdam. Ecosyst. Serv. 43, 101114. https://doi.org/10.1016/j.ecoser.2020.101114 (2020).Article 

Google Scholar 
Derkzen, M. L., van Teeffelen, A. J. A., Verburg, P. H. & Diamond, S. Quantifying urban ecosystem services based on high-resolution data of urban green space: An assessment for Rotterdam, the Netherlands. J. Appl. Ecol. 52, 1020–1032. https://doi.org/10.1111/1365-2664.12469 (2015).Article 

Google Scholar 
Leiva, M. A., Santibanez, D. A., Ibarra, S., Matus, P. & Seguel, R. A five-year study of particulate matter (PM2.5) and cerebrovascular diseases. Environ. Pollut. 181, 1–6. https://doi.org/10.1016/j.envpol.2013.05.057 (2013).CAS 
Article 

Google Scholar 
Venkataramanan, V. et al. Knowledge, attitudes, intentions, and behavior related to green infrastructure for flood management: A systematic literature review. Sci. Total Environ. 720, 137606. https://doi.org/10.1016/j.scitotenv.2020.137606 (2020).CAS 
Article 
PubMed 

Google Scholar 
Wang, G. Z., Han, Q. & De Vries, B. The multi-objective spatial optimization of urban land use based on low-carbon city planning. Ecol. Indic. 125, 107540. https://doi.org/10.1016/j.ecolind.2021.107540 (2021).CAS 
Article 

Google Scholar 
Cameron, R. W. F. et al. The domestic garden—Its contribution to urban green infrastructure. Urban For. Urban Green. 11, 129–137. https://doi.org/10.1016/j.ufug.2012.01.002 (2012).Article 

Google Scholar 
De la Sota, C., Ruffato-Ferreira, V. J., Ruiz-Garcia, L. & Alvarez, S. Urban green infrastructure as a strategy of climate change mitigation. A case study in northern Spain. Urban For. Urban Green. 40, 145–151. https://doi.org/10.1016/j.ufug.2018.09.004 (2019).Article 

Google Scholar 
Pongsakorn, S., Jiang, X. R. & Sullivan, W. C. Green infrastructure, green stormwater infrastructure, and human health a review. Curr. Landscape. Ecol. Rep. 2, 96–110. https://doi.org/10.1007/s40823-017-0028-y (2017).Article 

Google Scholar 
Liu, O. Y. & Russo, A. Assessing the contribution of urban green spaces in green infrastructure strategy planning for urban ecosystem conditions and services (Sust. Cities Soc., 2021). https://doi.org/10.1016/j.scs.2021.102772.Book 

Google Scholar 
McMahon, E. T. Green infrastructure. Plan. Commission. J. (2000).Mell, I. C. Green Infrastructure Concepts, Perceptions and Its Use in Spatial Planning. Doctor of Philosophy Thesis (Planning and Landscape Newcastle University, 2010).
Google Scholar 
Wang, J. X. & Banzhaf, E. Towards a better understanding of green infrastructure: A critical review. Ecol. Indic. 85, 758–772. https://doi.org/10.1016/j.ecolind.2017.09.018 (2018).Article 

Google Scholar 
Young, R., Zanders, J., Lieberknecht, K. & Fassman-Beck, E. A comprehensive typology for mainstreaming urban green infrastructure. J. Hydrol. 519, 2571–2583. https://doi.org/10.1016/j.jhydrol.2014.05.048 (2014).Article 

Google Scholar 
Wang, J. X., Xu, C., Pauleit, S., Kindler, A. & Banzhaf, E. Spatial patterns of urban green infrastructure for equity: A novel exploration. J. Clean Prod. 238, 117858. https://doi.org/10.1016/j.jclepro.2019.117858 (2019).Article 

Google Scholar 
Cook, E. A. Landscape structure indices for assessing urban ecological networks. Landsc. Urban Plan. 58, 269–280 (2002).Article 

Google Scholar 
Vogt, P. & Riitters, K. GuidosToolbox: Universal digital image object analysis. Eur. J. Remote Sens. 50, 352–361. https://doi.org/10.1080/22797254.2017.1330650 (2017).Article 

Google Scholar 
Vogt, P., Riitters, K. H., Estreguil, C., Kozak, J. & Wade, T. G. Mapping spatial patterns with morphological image processing. Landsc. Ecol. 22, 171–177. https://doi.org/10.1007/s10980-006-9013-2 (2007).Article 

Google Scholar 
Kuttner, M., Hainz-Renetzeder, C., Hermann, A. & Wrbka, T. Borders without barriers—Structural functionality and green infrastructure in the Austrian-Hungarian transboundary region of Lake Neusiedl. Ecol. Indic. 31, 59–72. https://doi.org/10.1016/j.ecolind.2012.04.014 (2013).Article 

Google Scholar 
Ma, Q. W., Li, Y. H. & Xu, L. H. Identification of green infrastructure networks based on ecosystem services in a rapidly urbanizing area. J. Clean Prod. 300, 126945. https://doi.org/10.1016/j.jclepro.2021.126945 (2021).Article 

Google Scholar 
Furberg, D., Ban, Y. & Mörtberg, U. Monitoring urban green infrastructure changes and impact on habitat connectivity using high-resolution satellite data. Remote Sens. 12, 3072. https://doi.org/10.3390/rs12183072 (2020).Article 

Google Scholar 
Barbati, A., Corona, P., Salvati, L. & Gasparella, L. Natural forest expansion into suburban countryside: Gained ground for a green infrastructure?. Urban For. Urban Green. 12, 36–43. https://doi.org/10.1016/j.ufug.2012.11 (2013).Article 

Google Scholar 
Fluhrer, T., Chapa, F. & Hack, J. A methodology for assessing the implementation potential for retrofitted and multifunctional urban green infrastructure in public areas of the global south. Sustainability https://doi.org/10.3390/su13010384 (2021).Article 

Google Scholar 
Carroll, C., McRae, B. H. & Brookes, A. Use of linkage mapping and centrality analysis across habitat gradients to conserve connectivity of gray wolf populations in western North America. Conserv. Biol. 26, 78–87. https://doi.org/10.1111/j.1523-1739.2011.01753.x (2012).Article 
PubMed 

Google Scholar 
Saura, S. & Torne, J. Conefor Sensinode 2.2: A software package for quantifying the importance of habitat patches for landscape connectivity. Environ. Modell. Softw. 24, 135–139 (2009).Article 

Google Scholar 
Jaworek-Jakubska, J., Filipiak, M., Michalski, A. & Napierała-Filipiak, A. Spatio-temporal changes of urban forests and planning evolution in a highly dynamical urban area: The case study of Wrocław, Poland. Forests 11, 17. https://doi.org/10.3390/f11010017 (2019).Article 

Google Scholar 
Ren, Z. B., He, X. Y., Zheng, H. F. & Wei, H. X. Spatio-temporal patterns of urban forest basal area under China’s rapid urban expansion and greening: Implications for urban green infrastructure management. Forests 9, 272. https://doi.org/10.3390/f9050272 (2018).Article 

Google Scholar 
Elliott, R. M. et al. Identifying linkages between urban green infrastructure and ecosystem services using an expert opinion methodology. Ambio 49, 569–583. https://doi.org/10.1007/s13280-019-01223-9 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
García, A. M., Santé, I., Loureiro, X. & Miranda, D. Green infrastructure spatial planning considering ecosystem services assessment and trade-off analysis. Application at landscape scale in Galicia region (NW Spain). Ecosyst. Serv. 43, 101115. https://doi.org/10.1016/j.ecoser.2020.101115 (2020).Article 

Google Scholar 
Tiwari, A. & Kumar, P. Integrated dispersion-deposition modelling for air pollutant reduction via green infrastructure at an urban scale. Sci. Total Environ. 723, 138078. https://doi.org/10.1016/j.scitotenv.2020.138078 (2020).CAS 
Article 
PubMed 

Google Scholar 
Zhang, Y. Q. et al. Unexpected air quality impacts from implementation of green infrastructure in urban environments: A Kansas City case study. Sci. Total Environ. 744, 140960. https://doi.org/10.1016/j.scitotenv.2020.140960 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Alizadehtazi, B., Gurian, P. L. & Montalto, F. A. Observed variability in soil moisture in engineered urban green infrastructure systems and linkages to ecosystem services. J. Hydrol. 590, 125381. https://doi.org/10.1016/j.jhydrol.2020.125381 (2020).Article 

Google Scholar 
Dennis, M., Cook, P. A., James, P., Wheater, C. P. & Lindley, S. J. Relationships between health outcomes in older populations and urban green infrastructure size, quality and proximity. BMC Public Health https://doi.org/10.1186/s12889-020-08762-x (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Van Oijstaeijen, W., Van Passel, S. & Cools, J. Urban green infrastructure: A review on valuation toolkits from an urban planning perspective. J. Environ. Manag. 267, 110603. https://doi.org/10.1016/j.jenvman.2020.110603 (2020).Article 

Google Scholar 
Majekodunmi, M., Emmanuel, R. & Jafry, T. A spatial exploration of deprivation and green infrastructure ecosystem services within Glasgow city. Urban For. Urban Green. 52, 126698. https://doi.org/10.1016/j.ufug.2020.126698 (2020).Article 

Google Scholar 
Liberalesso, T., Oliveira Cruz, C., Matos Silva, C. & Manso, M. Green infrastructure and public policies: An international review of green roofs and green walls incentives. Land Use Pol. 96, 104693. https://doi.org/10.1016/j.landusepol.2020.104693 (2020).Article 

Google Scholar 
Lin, H. Y., Qian, J., Yan, L. J. & Huang, S. R. Analysis of spatial-temporal pattern and scenario simulation of green infrastructure in Wuyi County based on morphological spatial pattern analysis and CA-Markov model. Acta Agricult. Zhejiangensis. https://doi.org/10.3969/j.issn.1004-1524.2019.07.21 (2019).Article 

Google Scholar 
Mitsova, D., Shuster, W. & Wang, X. H. A cellular automata model of land cover change to integrate urban growth with open space conservation. Landsc. Urban Plan. 99, 141–153. https://doi.org/10.1016/j.landurbplan.2010.10.001 (2011).Article 

Google Scholar 
Dennis, M. et al. Mapping urban green infrastructure: A novel landscape-based approach to incorporating land use and land cover in the mapping of human-dominated systems. Land 7, 17. https://doi.org/10.3390/land7010017 (2018).Article 

Google Scholar 
Hu, Y. J. et al. Urban expansion and farmland loss in Beijing during 1980–2015. Sustainability 10, 3927. https://doi.org/10.3390/su10113927 (2018).Article 

Google Scholar 
Li, W. J., Wang, Y., Xie, S. Y., Sun, R. H. & Cheng, X. Impacts of landscape multifunctionality change on landscape ecological risk in a megacity, China: A case study of Beijing. Ecol. Indic. 117 (2020).Song, W., Pijanowski, B. C. & Tayyebi, A. Urban expansion and its consumption of high-quality farmland in Beijing, China. Ecol. Indic. 54, 60–70. https://doi.org/10.1016/j.ecolind.2015.02.015 (2015).Article 

Google Scholar 
Li, Z. Z., Cheng, X. Q. & Han, H. R. Future impacts of land use change on ecosystem services under different scenarios in the ecological conservation area, Beijing, China. Forests https://doi.org/10.3390/f11050584 (2020).Article 

Google Scholar 
Liu, D. Y. et al. Interoperable scenario simulation of land-use policy for Beijing-Tianjin-Hebei region, China. Land Use Pol. 75, 155–165. https://doi.org/10.1016/j.landusepol.2018.03.040 (2018).Article 

Google Scholar 
Mo, W. B., Wang, Y., Zhang, Y. X. & Zhuang, D. F. Impacts of road network expansion on landscape ecological risk in a megacity, China: A case study of Beijing. Sci. Total Environ. 574, 1000–1011. https://doi.org/10.1016/j.scitotenv.2016.09.048 (2017).CAS 
Article 
PubMed 

Google Scholar 
Melgani, F. & Bruzzone, L. Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans. Geosci. Remote Sens. 42, 1778–1790. https://doi.org/10.1109/Tgrs.2004.831865 (2004).Article 

Google Scholar 
Zhang, C., Wang, T. J., Atkinson, P. M., Pan, X. & Li, H. P. A novel multi-parameter support vector machine for image classification. Int. J. Remote Sens. 36, 1890–1906. https://doi.org/10.1080/01431161.2015.1029096 (2015).CAS 
Article 

Google Scholar 
Peterson, L. K., Bergen, K. M., Brown, D. G., Vashchuk, L. & Blam, Y. Forested land-cover patterns and trends over changing forest management eras in the Siberian Baikal region. For. Ecol. Manag. 257, 911–922. https://doi.org/10.1016/j.foreco.2008.10.037 (2009).Article 

Google Scholar 
Sang, L. L., Zhang, C., Yang, J. Y., Zhu, D. H. & Yun, W. J. Simulation of land use spatial pattern of towns and villages based on CA-Markov model. Math. Comput. Model. 54, 938–943. https://doi.org/10.1016/j.mcm.2010.11.019 (2011).Article 

Google Scholar 
Liu, D. Y., Zheng, X. Q. & Wang, H. B. Land-use Simulation and Decision-Support system (LandSDS): Seamlessly integrating system dynamics, agent-based model, and cellular automata. Ecol. Model. 417, 108924. https://doi.org/10.1016/j.ecolmodel.2019.108924 (2020).Article 

Google Scholar 
Kazak, J. K. The use of a decision support system for sustainable urbanization and thermal comfort in adaptation to climate change actions-The case of the Wroclaw larger urban zone (Poland). Sustainability https://doi.org/10.3390/su10041083 (2013).Article 

Google Scholar 
Sonnenberg, F. A. & Beck, J. R. Markov-models in medical decision-making—A practical guide. Med. Decis. Mak. 13, 322–338. https://doi.org/10.1177/0272989×9301300409 (1993).CAS 
Article 

Google Scholar 
Nadoushan, M. A., Soffianian, A. & Alebrahim, A. Modeling land use/cover changes by the combination of Markov chain and cellular automata Markov CA-Markov models. Int. J. Environ. Health Res. https://doi.org/10.4103/WKMP-0092.159922 (2015).Article 

Google Scholar 
Mansour, S., Al-Belushi, M. & Al-Awadhi, T. Monitoring land use and land cover changes in the mountainous cities of Oman using GIS and CA-Markov modelling techniques. Land Use Pol. 91, 104414. https://doi.org/10.1016/j.landusepol.2019.104414 (2020).Article 

Google Scholar 
Karimi, H., Jafarnezhad, J., Khaledi, J. & Ahmadi, P. Monitoring and prediction of land use/land cover changes using CA-Markov model: A case study of Ravansar County in Iran. Arab. J. Geosci. https://doi.org/10.1007/s12517-018-3940-5 (2018).Article 

Google Scholar 
Mondal, M. S., Sharma, N. C. P. K. G. & Kappas, M. Statistical independence test and validation of CA Markov land use land cover (LULC) prediction results. Egypt. J. Remote Sens. Space Sci. https://doi.org/10.1016/j.ejrs.2016.08.001 (2016).Article 

Google Scholar 
Liu, Q. et al. Multi-scenario simulation of land use change and its eco-environmental effect in Hainan Island based on CA-Markov model. Ecol. Environ. Sci. 30, 1522–1531. https://doi.org/10.16258/j.cnki.1674-5906.2021.07.021 (2021).Article 

Google Scholar 
Pontius, R. G. Statistical methods to partition effects of quantity and location during comparison of categorical maps at multiple resolutions. Photogramm. Eng. Remote Sens. 68, 1041–1049 (2002).
Google Scholar 
Soille, P. & Vogt, P. Morphological segmentation of binary patterns. Pattern Recognit. Lett. 30, 456–459 (2009).Article 

Google Scholar 
Chang, Q., Liu, X. W., Wu, J. S. & He, P. MSPA-based urban green infrastructure planning and management approach for urban sustainability: Case study of Longgang in China. J. Urban Plan. Dev. https://doi.org/10.1061/(asce)up.1943-5444.0000247 (2015).Article 

Google Scholar 
Li, K. M. et al. Spatiotemporal evolution characteristics of urban green infrastructure in central Liaoning urban agglomeration during the past 20 years based on landscape ecology and morphology. Acta Ecol. Sin. https://doi.org/10.5846/stxb202007221918 (2021).Article 

Google Scholar 
Ning, J. et al. Spatiotemporal patterns and characteristics of land-use change in China during 2010–2015. J. Geogr. Sci. 28, 547–562. https://doi.org/10.1007/s11442-018-1490-0 (2018).Article 

Google Scholar 
Sawyer, S. C., Epps, C. W. & Brashares, J. S. Placing linkages among fragmented habitats: Do least-cost models reflect how animals use landscapes?. J. Appl. Ecol. 48, 668–678. https://doi.org/10.1111/j.1365-2664.2011.01970.x (2011).Article 

Google Scholar 
Yin, G. Y., Liu, L. M. & Jiang, X. L. The sustainable arable land use pattern under the tradeoff of agricultural production, economic development, and ecological protection—An analysis of Dongting Lake basin, China. Environ. Sci. Pollut. Res. 24, 25329–25345. https://doi.org/10.1007/s11356-017-0132-x (2017).Article 

Google Scholar  More

Shiklomanov, I. A. & Rodda, J. C. World water resources at the beginning of the twenty-first century. (Cambridge University Press, 2003).Biggs, J., von Fumetti, S. & Kelly-Quinn, M. The importance of small waterbodies for biodiversity and ecosystem services: implications for policy makers. Hydrobiologia 793, 3–39 (2017).Article 

Google Scholar 
Heino, J. et al. Lakes in the era of global change: moving beyond single-lake thinking in maintaining biodiversity and ecosystem services. Biol. Rev. 96, 89–106 (2021).PubMed 
Article 

Google Scholar 
Janssen, A. B. G. et al. Shifting states, shifting services: linking regime shifts to changes in ecosystem services of shallow lakes. Freshw. Biol. 66, 1–12 (2021).Article 

Google Scholar 
Knoll, L. B. et al. Consequences of lake and river ice loss on cultural ecosystem services. Limnol. Oceanogr. Lett. 4, 119–131 (2019).Article 

Google Scholar 
Sterner, R. W. et al. Ecosystem services of Earth’s largest freshwater lakes. Ecosyst. Serv. 41, 101046 (2020).Article 

Google Scholar 
Reynaud, A. & Lanzanova, D. A global meta-analysis of the value of ecosystem services provided by lakes. Ecol. Econ. 137, 184–194 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Cooley, S. W., Ryan, J. C. & Smith, L. C. Human alteration of global surface water storage variability. Nature 591, 78–81 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Downing, J. A. Global limnology: up-scaling aquatic services and processes to planet Earth. SIL Proceedings, 1922–2010 30, 1149–1166 (2009).Article 

Google Scholar 
Tranvik, L. J., Cole, J. J. & Prairie, Y. T. The study of carbon in inland waters—from isolated ecosystems to players in the global carbon cycle. Limnol. Oceanogr. Lett. 3, 41–48 (2018).Article 

Google Scholar 
Balsamo, G. et al. On the contribution of lakes in predicting near-surface temperature in a global weather forecasting model. Tellus A Dyn. Meteorol. Oceanogr. 64, 15829 (2012).Article 

Google Scholar 
DelSontro, T., Beaulieu, J. J. & Downing, J. A. Greenhouse gas emissions from lakes and impoundments: upscaling in the face of global change. Limnol. Oceanogr. Lett. 3, 64–75 (2018).CAS 
Article 

Google Scholar 
Beaulieu, J. J. et al. Methane and carbon dioxide emissions from reservoirs: controls and upscaling. J. Geophys. Res. Biogeosciences 125, e2019JG005474 (2020).ADS 
CAS 
Article 

Google Scholar 
Slater, J. A. et al. The SRTM data “finishing” process and products. Photogramm. Eng. Remote Sens. 72, 237–247 (2006).Article 

Google Scholar 
Pekel, J.-F., Cottam, A., Gorelick, N. & Belward, A. S. High-resolution mapping of global surface water and its long-term changes. Nature 540, 418–422 (2016).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Verpoorter, C., Kutser, T., Seekell, D. A. & Tranvik, L. J. A global inventory of lakes based on high-resolution satellite imagery. Geophys. Res. Lett. 41, 6396–6402 (2014).ADS 
Article 

Google Scholar 
Pickens, A. H. et al. Mapping and sampling to characterize global inland water dynamics from 1999 to 2018 with full Landsat time-series. Remote Sens. Environ. 243, 111792 (2020).ADS 
Article 

Google Scholar 
Messager, M. L., Lehner, B., Grill, G., Nedeva, I. & Schmitt, O. Estimating the volume and age of water stored in global lakes using a geo-statistical approach. Nat. Commun. 7, 13603 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tickner, D. et al. Bending the curve of global freshwater biodiversity loss: an emergency recovery plan. Bioscience 70, 330–342 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Downing, J. A., Polasky, S., Olmstead, S. M. & Newbold, S. C. Protecting local water quality has global benefits. Nat. Commun. 12, 1–6 (2021).Article 
CAS 

Google Scholar 
Hill, R. A., Weber, M. H., Debbout, R. M., Leibowitz, S. G. & Olsen, A. R. The Lake-Catchment (LakeCat) Dataset: characterizing landscape features for lake basins within the conterminous USA. Freshw. Sci. 37, 208–221 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Soranno, P. A. et al. LAGOS-NE: a multi-scaled geospatial and temporal database of lake ecological context and water quality for thousands of US lakes. Gigascience 6, 1–22 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Toptunova, O., Choulga, M. & Kurzeneva, E. Status and progress in global lake database developments. Adv. Sci. Res. 16, 57–61 (2019).Article 

Google Scholar 
Meyer, M. F., Labou, S. G., Cramer, A. N., Brousil, M. R. & Luff, B. T. The global lake area, climate, and population dataset. Sci. Data 7, 174 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Kling, G. W., Kipphut, G. W., Miller, M. M. & O’Brien, W. J. Integration of lakes and streams in a landscape perspective: the importance of material processing on spatial patterns and temporal coherence. Freshw. Biol. 43, 477–497 (2000).Article 

Google Scholar 
Fergus, C. E. et al. The freshwater landscape: lake, wetland, and stream abundance and connectivity at macroscales. Ecosphere 8, e01911 (2017).Article 

Google Scholar 
Lehner, B., Messager, ML., Korver, MC. & Linke, S. LakeATLAS Version 1.0, figshare, https://doi.org/10.6084/m9.figshare.19312001 (2022).Linke, S. et al. Global hydro-environmental sub-basin and river reach characteristics at high spatial resolution. Sci. data 6, 283 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Fergus, C. E. et al. National framework for ranking lakes by potential for anthropogenic hydro-alteration. Ecol. Indic. 122, 107241 (2021).Article 

Google Scholar 
Bracht-Flyr, B., Istanbulluoglu, E. & Fritz, S. A hydro-climatological lake classification model and its evaluation using global data. J. Hydrol. 486, 376–383 (2013).ADS 
Article 

Google Scholar 
Soranno, P. A. et al. Using landscape limnology to classify freshwater ecosystems for multi-ecosystem management and conservation. Bioscience 60, 440–454 (2010).Article 

Google Scholar 
McCullough, I. M., Skaff, N. K., Soranno, P. A. & Cheruvelil, K. S. No lake left behind: how well do U.S. protected areas meet lake conservation targets? Limnol. Oceanogr. Lett. 4, 183–192 (2019).Article 

Google Scholar 
Stanley, E. H. et al. Biases in lake water quality sampling and implications for macroscale research. Limnol. Oceanogr. 64, 1572–1585 (2019).ADS 
CAS 
Article 

Google Scholar 
Hanson, P. C., Weathers, K. C. & Kratz, T. K. Networked lake science: how the Global Lake Ecological Observatory Network (GLEON) works to understand, predict, and communicate lake ecosystem response to global change. Inl. Waters 6, 543–554 (2016).Article 

Google Scholar 
Lottig, N. R. & Carpenter, S. R. Interpolating and forecasting lake characteristics using long-term monitoring data. Limnol. Oceanogr. 57, 1113–1125 (2012).ADS 
Article 

Google Scholar 
Filazzola, A. et al. A database of chlorophyll and water chemistry in freshwater lakes. Sci. Data 2020 71 7, 1–10 (2020).
Google Scholar 
Lehner, B. & Messager, M. L. HydroLAKES – Technical Documentation Version 1.0. https://data.hydrosheds.org/file/technical-documentation/HydroLAKES_TechDoc_v10.pdf (2016).Natural Resources Canada. CanVec Hydrography: Waterbody Features. Version 12.0. https://ftp.maps.canada.ca/pub/nrcan_rncan/vector/canvec (2013).Lehner, B., Verdin, K. & Jarvis, A. New global hydrography derived from spaceborne elevation data. Eos, Trans. AGU 89, 93–94 (2008).ADS 
Article 

Google Scholar 
Farr, T. G. & Kobrick, M. Shuttle radar topography mission produces a wealth of data. Eos, Trans. AGU 81, 583–585 (2000).ADS 
Article 

Google Scholar 
Müller Schmied, H. et al. The global water resources and use model WaterGAP v2.2d: model description and evaluation. Geosci. Model Dev. 14, 1037–1079 (2021).ADS 
Article 

Google Scholar 
Beck, H. E. et al. Global evaluation of runoff from 10 state-of-the-art hydrological models. Hydrol. Earth Syst. Sci. 21, 2881–2903 (2017).ADS 
Article 

Google Scholar 
Alcamo, J. et al. Development and testing of the WaterGAP 2 global model of water use and availability. Hydrol. Sci. J. 48, 317–338 (2003).Article 

Google Scholar 
Döll, P., Kaspar, F. & Lehner, B. A global hydrological model for deriving water availability indicators: model tuning and validation. J. Hydrol. 270, 105–134 (2003).ADS 
Article 

Google Scholar 
Lehner, B. & Grill, G. Global river hydrography and network routing: baseline data and new approaches to study the world’s large river systems. Hydrol. Process. 27, 2171–2186 (2013).ADS 
Article 

Google Scholar 
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

Google Scholar 
Hengl, T. et al. SoilGrids250m: Global gridded soil information based on machine learning. PLoS One 12, e0169748 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Zhang, X. et al. GLC_FCS30: Global land-cover product with fine classification system at 30 m using time-series Landsat imagery. Earth Syst. Sci. Data 13, 2753–2776 (2021).ADS 
Article 

Google Scholar 
Buchhorn, M. et al. Copernicus Global Land Service: Land Cover 100m: Collection 3: epoch 2019: Globe, Zenodo, https://doi.org/10.5281/zenodo.3939050 (2020).ESRI. ArcGIS Desktop: Release 10.4.1 (Environmental Systems Research Institute, Redlands, CA, USA, 2016).Soranno, P. A., Cheruvelil, K. S., Wagner, T., Webster, K. E. & Bremigan, M. T. Effects of land use on lake nutrients: the importance of scale, hydrologic connectivity, and region. PLoS One 10, e0135454 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Su, Z. H., Lin, C., Ma, R. H., Luo, J. H. & Liang, Q. O. Effect of land use change on lake water quality in different buffer zones. Appl. Ecol. Environ. Res. 13, 639–653 (2015).
Google Scholar 
Brakebill, J. W., Schwarz, G. E. & Wieczorek, M. E. An enhanced hydrologic stream network based on the NHDPlus medium resolution dataset. Scientific Investigations Report https://doi.org/10.3133/sir20195127 (2020).Carroll, M., Townshend, J., DiMiceli, C., Noojipady, P. & Sohlberg, R. Global raster water mask at 250 meter spatial resolution, Collection 5: MOD44W MODIS Water Mask. College Park, Maryland: University of Maryland (2009).Carroll, M. L., Townshend, J. R., DiMiceli, C. M., Noojipady, P. & Sohlberg, R. A. A new global raster water mask at 250 m resolution. Int. J. Digit. Earth 2, 291–308 (2009).ADS 
Article 

Google Scholar 
European Environment Agency (EEA). European Catchments and Rivers Network System (ECRINS), https://www.eea.europa.eu/data-and-maps/data/european-catchments-and-rivers-network (2012).Ouellet Dallaire, C., Lehner, B., Sayre, R. & Thieme, M. A multidisciplinary framework to derive global river reach classifications at high spatial resolution. Environ. Res. Lett. 14, 024003 (2019).ADS 
Article 

Google Scholar 
Global Runoff Data Centre (GRDC). River discharge data. Federal Institute of Hydrology, 56068 Koblenz, Germany, https://www.bafg.de/GRDC (2014).Openshaw, S. The modifiable areal unit problem. In Quantitative Geography: A British View (eds. Wrigley, N. & Bennett, R.) 60–69 (Routledge and Kegan Paul, Andover, 1981).United States Census Bureau. 2010 Census. ftp://ftp2.census.gov/geo/tiger (2010).Center for International Earth Science Information Network (CIESIN) & NASA Socioeconomic Data and Applications Center (SEDAC). Gridded Population of the World, Version 4 (GPWv4): Population Count and Density. https://doi.org/10.7927/H4JW8BX5 (2016).Grill, G. et al. Mapping the world’s free-flowing rivers. Nature 569, 215–221 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Allen, D. J. et al. The Diversity of Life in African Freshwaters: Under Water, Under Threat: an Analysis of the Status and Distribution of Freshwater Species Throughout Mainland Africa. (IUCN, 2011).Markovic, D. et al. Europe’s freshwater biodiversity under climate change: distribution shifts and conservation needs. Divers. Distrib. 20, 1097–1107 (2014).Article 

Google Scholar 
Fluet-Chouinard, E., Lehner, B., Rebelo, L.-M., Papa, F. & Hamilton, S. K. Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data. Remote Sens. Environ. 158, 348–361 (2015).ADS 
Article 

Google Scholar 
Lehner, B. et al. High‐resolution mapping of the world’s reservoirs and dams for sustainable river‐flow management. Front. Ecol. Environ. 9, 494–502 (2011).Article 

Google Scholar 
Fan, Y., Li, H. & Miguez-Macho, G. Global patterns of groundwater table depth. Science 339, 940–943 (2013).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Robinson, N., Regetz, J. & Guralnick, R. P. EarthEnv-DEM90: A nearly-global, void-free, multi-scale smoothed, 90m digital elevation model from fused ASTER and SRTM data. ISPRS J. Photogramm. Remote Sens. 87, 57–67 (2014).ADS 
Article 

Google Scholar 
Metzger, M. J. et al. A high-resolution bioclimate map of the world: a unifying framework for global biodiversity research and monitoring. Glob. Ecol. Biogeogr. 22, 630–638 (2013).Article 

Google Scholar 
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).Article 

Google Scholar 
Zomer, R. J., Trabucco, A., Bossio, D. A. & Verchot, L. V. Climate change mitigation: a spatial analysis of global land suitability for clean development mechanism afforestation and reforestation. Agric. Ecosyst. Environ. 126, 67–80 (2008).Article 

Google Scholar 
Trabucco, A., Zomer, R. J., Bossio, D. A., van Straaten, O. & Verchot, L. V. Climate change mitigation through afforestation/reforestation: a global analysis of hydrologic impacts with four case studies. Agric. Ecosyst. Environ. 126, 81–97 (2008).Article 

Google Scholar 
Trabucco, A. & Zomer, R. J. Global soil water balance geospatial database. CGIAR Consortium for Spatial Information, https://cgiarcsi.community/data/global-high-resolution-soil-water-balance (2010).Hall, D. K., Riggs, G. A. & Salomonson, V. MODIS/Terra snow cover daily L3 global 500m grid, version 5, 2002–2015, https://doi.org/10.5067/MODIS/MOD10A1.006 (2016).Bartholomé, E. & Belward, A. S. GLC2000: a new approach to global land cover mapping from Earth observation data. Int. J. Remote Sens. 26, 1959–1977 (2005).Article 

Google Scholar 
Ramankutty, N. & Foley, J. A. Estimating historical changes in global land cover: Croplands from 1700 to 1992. Global Biogeochem. Cycles 13, 997–1027 (1999).ADS 
CAS 
Article 

Google Scholar 
Lehner, B. & Döll, P. Development and validation of a global database of lakes, reservoirs and wetlands. J. Hydrol. 296, 1–22 (2004).ADS 
Article 

Google Scholar 
Ramankutty, N., Evan, A. T., Monfreda, C. & Foley, J. A. Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000. Global Biogeochem. Cycles 22, (2008).Siebert, S. et al. A global data set of the extent of irrigated land from 1900 to 2005. Hydrol. Earth Syst. Sci. 19, 1521–1545 (2015).ADS 
Article 

Google Scholar 
GLIMS & NSIDC. Global land ice measurements from space (GLIMS) glacier database, v1. National Snow and Ice Data Center (NSIDC), https://doi.org/10.7265/N5V98602 (2012).Gruber, S. Derivation and analysis of a high-resolution estimate of global permafrost zonation. Cryosphere 6, 221–233 (2012).ADS 
Article 

Google Scholar 
UNEP-WCMC & IUCN. The World Database on Protected Areas, http://www.protectedplanet.net (2014).Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. Bioscience 67, 534–545 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Abell, R. et al. Freshwater ecoregions of the world: a new map of biogeographic units for freshwater biodiversity conservation. Bioscience 58, 403–414 (2008).Article 

Google Scholar 
Hengl, T. et al. SoilGrids1km—global soil information based on automated mapping. PLoS One 9, e105992 (2014).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hartmann, J. & Moosdorf, N. The new global lithological map database GLiM: a representation of rock properties at the Earth surface. Geochem. Geophys. Geosyst. 13, Q12004 (2012).ADS 
Article 

Google Scholar 
Williams, P. W. & Ford, D. C. Global distribution of carbonate rocks. Zeitschrift für Geomorphologie Suppl. 147, 1–2 (2006).
Google Scholar 
Borrelli, P. et al. An assessment of the global impact of 21st century land use change on soil erosion. Nat. Commun. 8, 1–13 (2017).ADS 
CAS 
Article 

Google Scholar 
Pesaresi, M. & Freire, S. GHS Settlement grid following the REGIO model 2014 in application to GHSL Landsat and CIESIN GPW v4-multitemporal (1975-1990-2000-2015). European Commission, Joint Research Centre (JRC), https://data.europa.eu/data/datasets/jrc-ghsl-ghs_smod_pop_globe_r2016a (2016).Doll, C. N. H. CIESIN thematic guide to night-time light remote sensing and its applications. CIESIN http://sedac.ciesin.columbia.edu/binaries/web/sedac/thematic-guides/ciesin_nl_tg.pdf (2008).Meijer, J. R., Huijbregts, M. A. J., Schotten, K. C. G. J. & Schipper, A. M. Global patterns of current and future road infrastructure. Environ. Res. Lett. 13, 64006 (2018).Article 

Google Scholar 
Venter, O. et al. Global terrestrial Human Footprint maps for 1993 and 2009. Sci. data 3, 160067 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
University of Berkeley. Database of global administrative areas (GADM). University of Berkeley, Museum of Vertebrate Zoology and the International Rice Research Institute, http://www.gadm.org (2012).Kummu, M., Taka, M. & Guillaume, J. H. A. Gridded global datasets for gross domestic product and Human Development Index over 1990–2015. Sci. data 5, 180004 (2018).PubMed 
PubMed Central 
Article 

Google Scholar  More

Study speciesCane toads (Rhinella marina) are large (to  > 1 kg) bufonids (Fig. 1a). Although native to north-eastern South America, these toads have been translocated to many countries worldwide to control insect pests12. Adult cane toads forage at night for insect prey and retreat to moist shelter-sites per day13. Small body size (and thus, high desiccation rate) restricts young toads to the margins of natal ponds14, but adult toads can survive even in highly arid habitats if they have access to water13,15. Cane toads prefer open habitats for foraging12, and thus can thrive in post-fire landscapes16,17. Cane toads in post-fire landscapes tend to have lower parasite burdens, probably because free-living larvae of their lungworm parasites cannot survive either the fire or the more sun-exposed post-fire landscape18.Figure 1taken from study sites between Casino, Grafton, and surrounds, NSW, by S.W. Kaiser.The cane toad Rhinella marina (a), and unburned, (b) and burned (c) habitats in which toads were collected and radio-tracked. Photographs were Full size imageStudy areaEast of the Great Dividing Range, near-coastal Clarence Dry Sclerophyll Forests of north-eastern New South Wales (NSW) are dominated by Spotted gum (Corymbia variegata) and Pink bloodwood (Corymbia intermedia)19. Fires are common, but typically cover relatively small areas before they are extinguished. In the summer of 2019–2020, however, prolonged drought followed by an unusually hot summer resulted in massive fires across this region, burning almost 100,000 km2 of vegetation9. In the current study, the toads we measured and dissected came from several sites within 75 km of the city of Casino (for site locations, see Fig. 2, Table 1, and18). The impacts of fire on faunal abundance and attributes shift with time since fire; for example, the abundance of a particular species may be reduced by fire (due to mortality from flames) but then increase as individuals from surrounding areas migrate to the recently-burned site to exploit new ecological opportunities provided by that landscape8. We chose to study this system 1-year post-fire, to allow time for such longer-term effects to be manifested.Figure 2Sampling sites relative to fire history. Sample sites are burned (red circles), and unburned (green squares). See Table 1 for key to sites. The legend shows the extent of burn a year prior to our study. Map created in QGIS 3.22.3. Fire history available from https://datasets.seed.nsw.gov.au/dataset/fire-extent-and-severity-mapping-fesm CC BY 4.0.Full size imageTable 1 Sampling sites and sample sizes for dissected and radio-tracked cane toads (Rhinella marina) in New South Wales, Australia.Full size tableSurveys of toad abundanceTo quantify toad abundance in burned and unburned sites, one observer (MJG) walked 100-m transects along roads at night (N = 23 and 8 respectively), recording all toads and native frogs (both adult and juvenile). The smaller number of unburned sites reflects the massive spatial scale of the wildfires, which made it difficult to find unburned areas. The transect sites were not the same as those sampled by “toad-busters” (below). We sampled both burned and unburned sites on each night, to de-confound effects of weather conditions with fire treatment. We scored frogs as well as toads to provide an estimate of overall anuran abundance and activity, and so that we could examine toad abundance relative to frog abundance as well as absolute toad numbers.“Toad-buster” sampleBecause of their ecological impact on native fauna, cane toads are culled by community groups as well as by government authorities12,20. We asked “toad-buster” groups to record whether the sites at which they collected toads had been burned during the 2019–2020 fires, or had remained unburned (Table 1). The toads were humanely euthanized (cooled-then-pithed: see21). The euthanasia method is brief (a few hours in the refrigerator, followed by pithing) and thus should not have affected any of the traits that we measured. For all of these toads, we measured body length (snout-urostyle length = SUL) and mass, and determined sex based on external morphology (skin colour and rugosity, nuptial pads: see22). A subset of toads (chosen to provide relatively equal numbers of males and females, and with equal numbers from burned and unburned sites) was dissected to provide data on mass of internal organs (fat bodies, liver, ovaries), reproductive condition (state of ovarian follicle development) and diet (mass and identity of prey items). To select the subsample of toads for dissection, we took relatively equal numbers of male and female toads from each bag of toads that was provided to us by the “toad-busters”. For logistical reasons, we were unable to dissect all of the toads that had been collected. Overall, we obtained data on morphology, diets and other traits from 481 fully dissected and 1443 partially dissected cane toads.Radio-trackingTo explore habitat use and movement patterns, we radio-tracked 57 toads over the course of two fieldtrips (0900–1800 h from 20 Nov 2021 to 6 Dec 2021 and 25 Jan 2022 to 10 Feb 2022). We selected seven sites (4 burned, 3 unburned) within 28 km of Tabbimoble, NSW (see Table 1 for locations and sample sizes of tracked toads). We hand-captured toads found active at night. These were measured, and their sex determined by external morphology (see above) and behaviour (release calls, given only by males: see23). We then fitted the toads with radio-transmitters (PD-2; Holohil Systems, Ontario, Canada; weighing ≤ 3.8 g) on cotton waist-belts, and released them at the site of capture. Tracked toads were 88.2–160.9 mm SUL (mass 70.1–546.3 g); thus, transmitters weighed  20 mm thick) within the quadrat, and estimated exposure of the toad within its refuge (the percentage of the animal’s body exposed to the naked eye). We then selected a compass bearing at random and walked 20 m in that direction where we rescored all of the above habitat attributes, to quantify habitat features in the broader environment (i.e., not just in microhabitats used by toads). We used those “random” sites to quantify overall habitat attributes of burned and unburned sites. Temperature was recorded by directing a temperature gun (Digitech QM7221) on (or otherwise close-to) toads and at a random point on the ground for random replicates. In total, we gathered radio-tracking data on movements and habitat variables from 57 cane toads, each of which was tracked for 5 days. Recaptured toads were euthanized by cooling-then-pithing.Morphological traitsTo obtain an index of body condition of toads, we regressed ln mass against ln SUL, and used the residual scores from that general linear regression as our estimate of body condition. Negative residual scores show an individual that weighs less-than-expected based on its body length. Likewise, we regressed mass of the fat bodies, liver and stomach against body mass to obtain indices of energy stores and stomach-content volumes relative to body mass. We scored male secondary sexual characteristics using the system of Bowcock et al.22. In their system, three sexually dimorphic traits (nuptial pad size, skin roughness and skin colouration) are scored from 0 to 2, and the scores from those three traits are summed to create a final value (on a 6-point scale) for the degree of elaboration of male-specific secondary sexual characteristics. We scored reproductive condition in adult female toads based on whether or not egg masses were visible during dissection, based on dissected toads from both “toad-buster” and telemetry samples.Statistical methodsData were analysed in R version 4.2.025. We used Linear Mixed Models (LMMs), Generalised Linear Mixed Models (GLMMs) and logistic regressions for our analyses. The R packages ‘tidyverse’26, ‘lmerTest’27, and ‘performance’28 were used.Habitat dataWe compared habitat variables between burned and unburned sites, and attributes of toads in burned versus unburned sites, using GLMMs (with negative binomial distribution) for count data (models were checked for overdispersion29) and LMMs on distance data, using ln-transformations where required to achieve normality. LMMs were used on non-normal percentage data, which were ln- and then logit-transformed (using log[(P + e)/(1 − P + e)], where e is the lowest non-zero number, halved)30. We used toad id, site (sampling location) and sampling trip (2019 versus 2020) as random factors.Anuran transect dataCounts of toads in burned versus unburned areas were compared both directly via GLMMs with a negative binomial distribution and relative to the numbers of frogs sighted along the same transects (binding the columns in R as ‘number of toads, number of amphibians – number of toads’ and using a GLMM with a binomial distribution). We used site as a random factor.Telemetry dataFor telemetry data, we analysed response variables via LMMs, and ln-transformed data where relevant to achieve normality.Dissection dataWe used LMMs for SUL, body mass, body condition and organ mass residuals (e.g., fat body mass relative to body mass). For prey item data, we used a poisson distribution with row number as a random factor, as the negative binomial and beta distribution GLMMs were overdispersed (see31). We used LMM for number of prey items and number of prey groups, with site as a random factor. Where models failed to converge, we reduced or removed the error term(s). Analyses were restricted to toads ≥ 70 mm SUL, because animals below this size were difficult to sex. We also performed nominal logistic regression to explore variation in sex ratio and male secondary sexual traits.Reproductive conditionWe used LMM for male secondary sexual characteristic display, using site as a random factor. For ovary presence, we used a binomial GLMM with a logit link, using site as a random factor. We used a LMM of the residual values from ovary mass relative to body mass (ln-transformed), using site as a random factor.Ethics declarationsAll procedures were performed in accordance with the relevant guidelines and regulations approved by Macquarie University Animal Ethics Committee (ARA Number: 2019/040-2) and in accordance with ARRIVE guidelines. More

Deforestation, in places such as the Amazon, contributes to biodiversity loss.Credit: Ivan Valencia/Bloomberg/Getty

Researchers are relieved that a pivotal summit to finalize a new global agreement to save nature will go ahead this year, after two-years of delays because of the pandemic. But they say the hard work of negotiating an ambitious deal lays ahead.The United Nations Convention on Biological Diversity (CBD) announced yesterday that the meeting will move from Kunming in China to Montreal in Canada. The meeting of representatives from almost 200 member states of the CBD — known as COP15 — will now run from 5 to 17 December. China will continue as president of the COP15 and Huang Runqiu, China’s minister of ecology and environment, will continue as chairman.Conservation and biodiversity scientists were growing increasingly concerned that China’s strict ‘zero COVID’ strategy, which uses measures such as lockdowns to quash all infections, would force the host nation to delay the meeting again. Researchers warned that another setback to the agreement, which aims to halt the alarming rate of species extinctions and protect vulnerable ecosystems, would be disastrous for countries’ abilities to meet ambitious targets to protect biodiversity over the next decade.“We are relieved and thankful that we have a firm date for these critically important biodiversity negotiations within this calendar year,” says Andrew Deutz, an expert in biodiversity law and finance at the Nature Conservancy, a conservation group in Virginia, US. “The global community is already behind in agreeing, let alone implementing, a plan to halt and reverse biodiversity loss by 2030,” he says.With the date now set, Anne Larigauderie, executive secretary of the Intergovernmental Platform on Biodiversity and Ecosystem Services, says the key to success in Montreal will be for the new global biodiversity agreement to focus on the direct and indirect drivers of nature loss, and the behaviors that underpin them. “Policy should be led by science, action adequately resourced and change should be transformative,” she adds.New locationThe decision to move the meeting came about after representatives of the global regions who make up the decision-making body of the COP reached a consensus to shift it to Montreal. China and Canada then thrashed out the details of how the move would work. The CBD has provisions that if a host country is unable to hold a COP, the meeting shifts to the home of the convention’s secretariat, Montreal.Announcing the decision, Elizabeth Mrema, executive secretary of the CBD, said in a statement, “I want to thank the government of China for their flexibility and continued commitment to advancing our path towards an ambitious post 2020 Global Biodiversity Framework.”In a statement, Runqiu said, “China would like to emphasize its continued strong commitment, as COP president, to ensure the success of the second part of COP 15, including the adoption of an effective post 2020 Global Biodiversity Framework, and to promote its delivery throughout its presidency.”China also agreed to pay for ministers from the least developed countries and small Island developing states to travel to Montreal to participate in the meeting.Work aheadPaul Matiku, an environmental scientist and head of Nature Kenya, a conservation organization in Nairobi, Kenya, says the move “is a welcome decision” after “the world lost patience after a series of postponements”.But he says that rich nations need to reach deeper into their pockets to help low- and middle-income countries — which are home to much of the world’s biodiversity — to implement the deal, including meeting targets such as protecting at least 30% of the world’s land and seas and reducing the rate of extinction. Disputes over funding already threaten to stall the agreement. At a meeting in Geneva in March, nations failed to make progress on the new deal because countries including Gabon and Kenya argued that the US$10 billion of funding per year proposed in the draft text of the agreement was insufficient. They called for $100 billion per year in aid.“The extent to which the CBD is implemented will depend on the availability of predictable, adequate financial flows from developed nations to developing country parties,” says Matiku.Talks on the agreement are resuming in Nairobi from 21-26 June, where Deutz hopes countries can find common ground on key issues such as financing before heading to Montreal. Having a firm date set for the COP15 will help push negotiations forward, he says.“Negotiators only start to compromise when they are up against a deadline. Now they have one,” he says. More

Foote, M. The evolution of morphological diversity. Annu. Rev. Ecol. Syst. 28, 129–152 (1997).Article 

Google Scholar 
Félix, M. A. Phenotypic evolution with and beyond genome evolution. Curr. Top. Dev. Biol. 119, 291–347 (2016).PubMed 
Article 
CAS 

Google Scholar 
Carroll, S. B. Evo-devo and an expanding evolutionary synthesis: A genetic theory of morphological evolution. Cell 134, 25–36 (2008).CAS 
PubMed 
Article 

Google Scholar 
Harvey, P. & Pagel, M. The Comparative Method in Evolutionary Biology. (Oxford University Press, 1991).Huxley, J. S. & Teissier, G. Terminology of relative growth. Nature 137, 780–781 (1936).ADS 
Article 

Google Scholar 
Klingenberg, C. P. Size, shape, and form: Concepts of allometry in geometric morphometrics. Dev. Genes Evol. 226, 113–137 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Russell, E. S. Form and Function: A Contribution to the History of Animal Morphology. (John Murray, 1916).Goswami, A. & Polly, P. D. Methods for studying morphological integration and modularity. Paleontol. Soc. Pap. 16, 213–243 (2010).Article 

Google Scholar 
Vidal-García, M., Byrne, P. G., Roberts, J. D. & Keogh, J. S. The role of phylogeny and ecology in shaping morphology in 21 genera and 127 species of Australo-Papuan myobatrachid frogs. J. Evol. Biol. 27, 181–192 (2014).PubMed 
Article 

Google Scholar 
Erwin, D. H. Disparity: Morphological pattern and developmental context. Palaeontology 50, 57–73 (2007).Article 

Google Scholar 
Fišer, C., Robinson, C. T. & Malard, F. Cryptic species as a window into the paradigm shift of the species concept. Mol. Ecol. 27, 613–635 (2018).PubMed 
Article 

Google Scholar 
Wilson, D. E. & Mittermeier, R. A. Handbook of the Mammals of the World: Volume 8: Insectivores. vol. 8 (Lynx Edicions, 2018).Jacquet, F. et al. Phylogeography and evolutionary history of the Crocidura olivieri complex (Mammalia, Soricomorpha): From a forest origin to broad ecological expansion across Africa. BMC Evol. Biol. 15, 71. https://doi.org/10.1186/s12862-015-0344-y (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Ceríaco, L. M. P. et al. Description of a new endemic species of shrew (Mammalia, Soricomorpha) from PrÍncipe Island (Gulf of Guinea). Mammalia 79, 325–341 (2015).Article 

Google Scholar 
Nicolas, V. et al. Multilocus phylogeny of the Crocidura poensis species complex (Mammalia, Eulipotyphla): Influences of the palaeoclimate on its diversification and evolution. J. Biogeogr. 46, 871–883 (2019).Article 

Google Scholar 
Konečný, A., Hutterer, R., Meheretu, Y. & Bryja, J. Two new species of Crocidura (Mammalia: Soricidae) from Ethiopia and updates on the Ethiopian shrew fauna. J. Vertebr. Biol. 69, 20064.1. https://doi.org/10.25225/jvb.20064 (2020).Article 

Google Scholar 
Couvreur, T. L. P. et al. Tectonics, climate and the diversification of the tropical African terrestrial flora and fauna. Biol. Rev. 96, 16–51 (2021).PubMed 
Article 

Google Scholar 
Mayr, E. & O’Hara, R. J. The biogeographic evidence supporting the Pleistocene forest refuge hypothesis. Evolution 40, 55–67 (1986).PubMed 
Article 

Google Scholar 
Wiens, J. J. & Graham, C. H. Niche conservatism: Integrating evolution, ecology, and conservation biology. Annu. Rev. Ecol. Evol. Syst. 36, 519–539 (2005).Article 

Google Scholar 
Smith, T. B., Wayne, R. K., Girman, D. J. & Bruford, M. W. A role for ecotones in generating rainforest biodiversity. Science 276, 1855–1857 (1997).CAS 
Article 

Google Scholar 
Needham, A. E. & Hardy, A. C. The form-transformation of the abdomen of the female pea-crab, Pinnotheres pisum Leach. Proc. R Soc. Lond. Ser. B Biol. Sci. 137, 115–136 (1950).ADS 
CAS 

Google Scholar 
Hanken, J. & Hall, B. K. The Skull, Volume 3: Functional and Evolutionary Mechanisms. (University of Chicago Press, 1993).Hautier, L., Lebrun, R. & Cox, P. G. Patterns of covariation in the masticatory apparatus of hystricognathous rodents: Implications for evolution and diversification. J. Morphol. 273, 1319–1337 (2012).PubMed 
Article 

Google Scholar 
Aristide, L. et al. Multiple factors behind early diversification of skull morphology in the continental radiation of New World monkeys. Evolution 72, 2697–2711 (2018).PubMed 
Article 

Google Scholar 
Hardin, G. The competitive exclusion principle. Science 131, 1292–1297 (1960).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Denys, C. et al. Shrews (Mammalia, Eulipotyphla) from a biodiversity hotspot, Mount Nimba (West Africa), with a field identification key to species. Zoosystema 43, 729–757 (2021).Article 

Google Scholar 
Estevo, C. A., Nagy-Reis, M. B. & Nichols, J. D. When habitat matters: Habitat preferences can modulate co-occurrence patterns of similar sympatric species. PLoS One 12, e0179489. https://doi.org/10.1371/journal.pone.0179489 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Spaeth, P. A. Morphological convergence and coexistence in three sympatric North American species of Microtus (Rodentia: Arvicolinae). J. Biogeogr. 36, 350–361 (2009).Article 

Google Scholar 
Adams, D. C., Berns, C. M., Kozak, K. H. & Wiens, J. J. Are rates of species diversification correlated with rates of morphological evolution?. Proc. R. Soc. B Biol. Sci. 276, 2729–2738 (2009).Article 

Google Scholar 
Caumul, R. & Polly, P. D. Phylogenetic and environmental components of morphological variation: Skull, mandible, and molar shape in marmots (marmota, Rodentia). Evolution 59, 2460–2472 (2005).PubMed 
Article 

Google Scholar 
Da Silva, F. O. et al. The ecological origins of snakes as revealed by skull evolution. Nat. Commun. 9, 376. https://doi.org/10.1038/s41467-017-02788-3 (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Hirano, T., Kameda, Y., Kimura, K. & Chiba, S. Substantial incongruence among the morphology, taxonomy, and molecular phylogeny of the land snails Aegista, Landouria, Trishoplita, and Pseudobuliminus (Pulmonata: Bradybaenidae) occurring in East Asia. Mol. Phylogenet. Evol. 70, 171–181 (2014).PubMed 
Article 

Google Scholar 
Ge, D., Yao, L., Xia, L., Zhang, Z. & Yang, Q. Geometric morphometric analysis of skull morphology reveals loss of phylogenetic signal at the generic level in extant lagomorphs (Mammalia: Lagomorpha). Contrib. Zool. 84, 267–284 (2015).Article 

Google Scholar 
Zou, Z. & Zhang, J. Morphological and molecular convergences in mammalian phylogenetics. Nat. Commun. 7, 12758. https://doi.org/10.1038/ncomms12758 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Ananjeva, N. B. Current state of the problems in the phylogeny of squamate reptiles (Squamata, Reptilia). Biol. Bull. Rev. 9, 119–128 (2019).Article 

Google Scholar 
Revell, L. J., Harmon, L. J. & Collar, D. C. Phylogenetic signal, evolutionary process, and rate. Syst. Biol. 57, 591–601 (2008).PubMed 
Article 

Google Scholar 
Klingenberg, C. P. & Marugán-Lobón, J. Evolutionary covariation in geometric morphometric data: Analyzing integration, modularity, and allometry in a phylogenetic context. Syst. Biol. 62, 591–610 (2013).PubMed 
Article 

Google Scholar 
Cardini, A. & Polly, P. D. Larger mammals have longer faces because of size-related constraints on skull form. Nat. Commun. 4, 2458. https://doi.org/10.1038/ncomms3458 (2013).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Esquerré, D., Sherratt, E. & Keogh, J. S. Evolution of extreme ontogenetic allometric diversity and heterochrony in pythons, a clade of giant and dwarf snakes. Evolution 71, 2829–2844 (2017).PubMed 
Article 

Google Scholar 
Marroig, G. & Cheverud, J. M. Size as a line of least evolutionary resistance: Diet and adaptive morphological radiation in New World monkeys. Evolution 59, 1128–1142 (2005).PubMed 
Article 

Google Scholar 
Cornette, R., Tresset, A., Houssin, C., Pascal, M. & Herrel, A. Does bite force provide a competitive advantage in shrews? The case of the greater white-toothed shrew. Biol. J. Linn. Soc. 114, 795–807 (2015).Article 

Google Scholar 
Rodgers, G. M., Downing, B. & Morrell, L. J. Prey body size mediates the predation risk associated with being “odd”. Behav. Ecol. 26, 242–246 (2015).Article 

Google Scholar 
Damuth, J. Population density and body size in mammals. Nature 290, 699–700 (1981).ADS 
Article 

Google Scholar 
Verschuren, D. Decadal and century-scale climate variability in tropical Africa during the past 2000 years. In Past Climate Variability Through Europe and Africa (eds. Battarbee, R. W., Gasse, F. & Stickley, C. E.) 139–158 (Springer Netherlands, 2004). https://doi.org/10.1007/978-1-4020-2121-3_8.Smith, T. B., Schneider, C. J. & Holder, K. Refugial isolation versus ecological gradients. Genetica 112, 383–398 (2001).PubMed 
Article 

Google Scholar 
Brown, W. L. Jr. & Wilson, E. O. Character displacement. Syst. Biol. 5, 49–64 (1956).
Google Scholar 
Vogel, P. et al. Genetic identity of the critically endangered Wimmer’s shrew Crocidura wimmeri. Biol. J. Linn. Soc. 111, 224–229 (2014).Article 

Google Scholar 
Esselstyn, J. A. et al. Fourteen new, endemic species of shrew (genus Crocidura) from Sulawesi reveal a spectacular island radiation. Bull. Am. Mus. Nat. Hist. 454, 1–108 (2021).Article 

Google Scholar 
Evin, A., Bonhomme, V. & Claude, J. Optimizing digitalization effort in morphometrics. Biol. Methods Protoc. 5, bpaa023. https://doi.org/10.1093/biomethods/bpaa023 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Blomberg, S. P., Garland, T. & Ives, A. R. Testing for phylogenetic signal in comparative data: Behavioral traits are more labile. Evolution 57, 717–745 (2003).PubMed 
Article 

Google Scholar 
Adams, D. C. A generalized K statistic for estimating phylogenetic signal from shape and other high-dimensional multivariate data. Syst. Biol. 63, 685–697 (2014).PubMed 
Article 

Google Scholar 
Kembel, S. W. et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics 26, 1463–1464 (2010).CAS 
PubMed 
Article 

Google Scholar 
Revell, L. J. phytools: Phylogenetic Tools for Comparative Biology (and Other Things). (2021).Fick, S. E. & Hijmans, R. J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

Google Scholar 
Pebesma, E. Simple features for R: Standardized support for spatial vector data. R J. 10, 439 (2018).Article 

Google Scholar 
Oksanen, J. et al. vegan: Community Ecology Package. (2020).Dray, S., Legendre, P. & Peres-Neto, P. R. Spatial modelling: A comprehensive framework for principal coordinate analysis of neighbour matrices (PCNM). Ecol. Model. 196, 483–493 (2006).Article 

Google Scholar 
Borcard, D., Gillet, F. & Legendre, P. Numerical Ecology with R. (Springer, 2018).Dray, S. et al. adespatial: Multivariate Multiscale Spatial Analysis. (2021).Collyer, M. & Adams, D. RRPP: Linear Model Evaluation with Randomized Residuals in a Permutation Procedure. (2021).Kassambara, A. rstatix: Pipe-Friendly Framework for Basic Statistical Tests. (2021).Borcard, D., Legendre, P. & Drapeau, P. Partialling out the spatial component of ecological variation. Ecology 73, 1045–1055 (1992).Article 

Google Scholar 
Rohlf, F. J. & Corti, M. Use of two-block partial least-squares to study covariation in shape. Syst. Biol. 49, 740–753 (2000).CAS 
PubMed 
Article 

Google Scholar 
Schlager, S., Jefferis, G. & Ian, D. Morpho: Calculations and Visualisations Related to Geometric Morphometrics. (2020). More

Study areaThe NCA encompasses an area of 8292 km2 and in 2020 had approximately 87,000 inhabitants23, who are primarily dependent on livestock for their livelihoods. It is a multiple-use area where people coexist with wildlife and livestock, and practise pastoralism with transhumance, characterised by seasonal movements of livestock for accessing resources such as grazing areas and water. The NCA comprises eleven administrative wards: Alailelai, Endulen, Eyasi, Laitole, Kakesio, Misigiyo, Ngorongoro, Naiyobi, Nainokanoka, Ngoile and Olbalbal (Fig. 1). The NCA was chosen for our study as it is known to be hyperendemic for anthrax4,17,20. In addition, informal consultations we held prior to the study, as well as tailored data collection at the community and household level, indicated that local communities have a good understanding of the disease in humans and animals, and of practices around carcass and livestock management that increase risks, particularly in certain locations and periods of the year24.Figure 1Locations of participatory mapping. Map showing the 11 administrative wards of the Ngorongoro Conservation Area in northern Tanzania and the locations where participatory mapping sessions took place (red dots). The maps were produced in QGIS 2.18.2 using data from the National Bureau of Statistics, Tanzania (http://www.nbs.go.tz/).Full size imageEthics approval and consent to participateThe study received approval from the National Institute for Medical Research, Tanzania, with reference number NIMRJHQ/R.8a/Vol. IX/2660; the Tanzania Commission for Science and Technology (numbers 2016-94-NA-2016-88 (O. R. Aminu), 2016-95-NA-2016-45 (T. L. Forde) and 2018-377-NA-2016-45 (T. Lembo)); Kilimanjaro Christian Medical University College Ethics Review Committee (certificate No. 2050); and the University of Glasgow College of Medical Veterinary & Life Sciences Ethics Committee (application number 200150152). Approval and permission to access communities and participants were also obtained from relevant local authorities. Written informed consent was obtained from all participants involved in the study. All data collected were analysed anonymously, ensuring the confidentiality of participants. All research activities were performed in accordance with relevant guidelines and regulations.Participatory mappingA participatory mapping approach based on methodology previously tested in East Africa25 was employed to define areas of anthrax risk for animals in the NCA based on community knowledge. Georeferenced maps of the NCA were produced using data from Google and DigitalGlobe (2016). The maps used datum Arc 1960/UTM zone 36S and grid intervals of 1000 km and were produced at 1:10,000 and 1:50,000 scales, in order to provide participants with a choice. Ten participatory mapping focus groups were held at ward administrative level (Fig. 1) in order to identify areas in the NCA that communities perceive as posing a high risk of anthrax. One mapping exercise was held in each ward. Ngoile and Olbalbal wards were covered at the same time and treated as one, as they had only recently (in 2015) been split from one ward (Olbalbal). Each session had between ten and thirteen participants, who consisted of village and ward administrators, animal health professionals (including community animal health workers and livestock field officers), community leaders, and selected community members. These participants represented members of the community concerned with animal health and owning livestock and, as such, were likely to hold in-depth knowledge relating to community experience of animal health and disease, including anthrax. Participants were recruited by consulting with animal health professionals as well as village and ward administrators, who gave permission to conduct the mapping sessions.The mapping sessions were conducted in Swahili and translated into English by an interpreter. Participants’ general knowledge of the area was first verified by testing whether they could correctly identify popular locations such as health centres, places of worship, markets and schools. Subsequently, participants discussed among themselves and came to a consensus about areas they considered to be at high risk of anthrax. Specifically, we asked them to identify locations they perceived as areas where they considered their animals to be at risk of being exposed to anthrax. These areas were drawn on the maps provided (Fig. 2). While they did not locate areas where the animals had succumbed to disease, we also asked for generic information on locations where anthrax outbreaks had occurred in the past to define areas that could be targeted for active surveillance of cases. In order to improve the fidelity of the data, participants defined risk areas in relation to their own locality (ward) and locations where their animals access resources. Therefore, the areas were not defined by administrative boundaries, as communities may access locations outside their wards, for instance for grazing or watering. The resulting maps were scanned, digitised and analysed as detailed in the following sections. Further detail on the participatory mapping process is provided in the Supplementary Methods (Additional File 1).Figure 2Participatory mapping of anthrax risk areas in the Ngorongoro Conservation Area. Images show (A) the set-up of a mapping session, (B) participants engaged during a session and (C) an example of a 1:50,000 scale map annotated by participants. The map was created with QGIS opensource mapping software. The basemap used was a scanned and geo-referenced full colour 1:50,000 scale topographic map produced by the Surveys & Mapping Division, Ministry of Lands, Housing & Human Settlements, Dar es Salaam, Tanzania. The grid is based on the Arc1960 UTM 36S projection and datum. The map was exported from QGIS in Acrobat Pdf format to enable it to be printed at suitable sizes for using in the fieldwork and to be manually annotated during the participatory mapping.Full size imageDigitisation of maps and generation of random pointsScanned maps were saved as PDF files and converted to high resolution TIFF files for digitisation in QGIS 2.18.2-Las Palmas free OpenSource software26. All maps were georeferenced with geographical coordinates during production and reference points were available to enable the precise mapping of all locations. The digitization was carried out using the QGIS digitizing tools and by creating polygon layers of the defined risk areas.Sourcing data on the environmental predictors of anthraxAvailable soil and environmental data (250 m grid) for Tanzania were obtained from various sources (Table 1). From the available data, we selected the following seven variables which have previously been shown to contribute to or explain the risk of anthrax based on the biology of B. anthracis (Table 1).Table 1 Environmental factors with potential to influence anthrax occurrence.Full size tableCation exchange capacity (CEC)Measured in cmol/kg, CEC is the total capacity of the soil to retain exchangeable cations such as Ca2+, Mg2+ etc. It is an inherent soil characteristic and is difficult to alter significantly. It influences the soil’s ability to hold on to essential nutrients and provides a buffer against soil acidification27. CEC has been reported to be positively correlated with anthrax risk. In addition, CEC is a proxy for calcium content, which may contribute to anthrax risk in a pH-dependent manner as explained below19,22.Predicted topsoil pH (pH)Soil pH below 6.0 (acidic soil) is thought to inhibit the viability of spores19 thus a positive effect of higher pH on the risk of anthrax is expected. It has been suggested that the exosporium of B. anthracis is negatively charged in soils with neutral to slightly alkaline pH. This negative charge attracts positively charged cations in soil, mainly calcium, enabling the spores to be firmly attached to soil particles and calcium to be maintained within the spore core, thereby promoting the viability of B. anthracis19,28.Distance to inland water bodies (DOWS)Both the distance from water and proximity to water may increase anthrax risk. Distance to inland water may indicate the degree to which an area is dry/arid. Anthrax outbreaks have been shown to occur in areas with very dry conditions19. Although anthrax occurrence has also been associated with high soil moisture, this relates more to the spore germination in the environment (a mechanism that is disputed) and the concentration of spores in moist humus that amount to an infectious dose18,29. Spores will survive much longer in soils with low moisture content19. Low moisture may also be associated with low vegetation which results in animals grazing close to the soil, increasing the risk of ingesting soil with spores. Hampson et al. reported that anthrax outbreaks occurred close to water sources in the Serengeti ecosystem of Tanzania in periods of heavy rainfall20, and Steenkamp et al. found that close proximity to water bodies was key to the transmission of B. anthracis spores in Kruger National Park, South Africa22. Water is an important resource for livestock and a large number of animals may congregate at water sources during dry seasons. The close proximity of a water source to a risk area may increase the chance of infection, particularly during periods of high precipitation which might unearth buried spores.Average enhanced vegetation index (EVI)Vegetation density may influence the likelihood of an animal ingesting soil or inhaling dust that may be contaminated with spores. Grazing animals are more likely to encounter bacteria in soil with low vegetation density20, although there is a possibility that spores can be washed onto higher vegetation by the action of water19. Vegetation index may also reflect the moisture content of soil. Arid/dry conditions favour the formation and resistance of spores in the environment, thus lower vegetation may be associated with the occurrence of anthrax.Average daytime land surface temperature (LSTD)Anthrax has been more commonly reported to occur in regions with warmer climates worldwide. Minett observed that under generally favourable conditions and at 32 °C to 37 °C, sporulation of B. anthracis occurs readily but vegetative cells are more likely to disintegrate at temperatures below 21 °C30. Another hypothesis for the association of high temperature with anthrax occurrence is altered host immune response to disease due to stress caused by elevated temperatures19. In addition, elevated temperatures are usually associated with arid areas where vegetation is low, limiting access to adequate nutrition, which in turn affects immunity. Similarly, in hotter climates where infectious diseases occur more often, host interactions with other pathogens may modulate immune response to anthrax31. In this case, a lower infectious and lethal dose of spores would be sufficient to cause infection and death, respectively19. Contact with and ingestion of soil, spores and abrasive pasture is also higher with low vegetation in hot and arid areas19,32. In boreal regions such as in northern Canada, where anthrax occurs in wood bison, and Siberia, the disease is more commonly reported in the summer19. We therefore hypothesised a positive effect of LSTD on the risk of anthrax.SlopeSpores of B. anthracis are hypothesized to persist more easily in flat landscapes that are characterised by shallow slopes19, as it is thought that wind and water may disperse spores more easily along areas with a higher slope gradient, thereby decreasing the density of spores to levels that may be insufficient to cause infection in a susceptible host. Therefore, we expected a negative relationship between slope and the risk of anthrax.Predicted topsoil organic carbon content (SOC)Organic matter (g/kg) may aid spore persistence by providing mechanical support. The negatively charged exosporium of spores is attracted to the positive charges on hummus-rich soil, thus anthrax is thought to persist in soil rich in organic matter18. Based on available evidence, we expected a positive effect of SOC on the risk of anthrax.Creating the datasetThe annotated and digitised maps yielded polygons of high-risk areas within the NCA (Fig. 3). After digitization, 5000 random points were generated33 to cover the 8292 km2 area of the NCA. This enabled us to obtain distinct points allowed by the 250 m grid resolution of the environmental variables. Points falling within the defined risk areas were selected to represent risk areas while those falling outside represented low-risk areas. Measures of the environmental characteristics associated with individual points were obtained with the ‘add Raster data to points’ feature in QGIS.Figure 3Ngorongoro Conservation Area map showing (A) defined risk areas (in red) and (B) distance to settlements. For analysis, 5000 random points were generated throughout the area; points falling within 4.26 km of human settlements (the average distance herds are moved from settlements in a day as determined through interviews of resident livestock owners) were retained for analysis (n = 2173, shown in blue in 3a). The maps were created in QGIS 2.18.2 using data from the National Bureau of Statistics, Tanzania (http://www.nbs.go.tz/).Full size imageIn order to focus on areas of greatest risk to humans and livestock and to exclude locations that are not accessible, only points within a certain range of distance from settlements were included (Fig. 3). On average, herders in the NCA move their livestock 4.26 km away from settlements for grazing and watering during the day (unpublished data obtained through a cross-sectional survey of 209 households). Thus, only points falling within this distance from settlements were selected, providing us with data on areas where infection is most likely to occur. Data on locations of settlements were obtained from satellite imagery and included permanent residences as well as temporary settlements (e.g. seasonal camps set up after long distance movement away from permanent settlements, typically in the dry season, in search of pasture and water). These data were collated from the Center for International Earth Science Information Network (CIESIN).After adjusting for accessibility of resource locations using the average distance moved by livestock, 2173 points were retained for analysis, of which 239 (11%) fell within high-risk areas.Data analysisAll statistical analyses were carried out in R (v 4.1.0) within the RStudio environment34. The aims of the statistical analysis were to infer the relationship between anthrax risk areas as determined through participatory mapping and the environmental factors identified in Table 1, and to use this relationship to make spatial predictions of anthrax risk across the study area. We achieved both aims by modelling the binary risk status (high or low) of the randomly generated points as a function of their environmental characteristics in a Bayesian spatial logit-binomial generalised linear mixed-effects model (GLMM), implemented in the package glmmfields35. Spatial autocorrelation (residual non-independence between nearby points) was accounted for by including spatial random effects in the GLMM. We chose relatively non-informative priors for the intercept and the covariates, using Student’s t-distributions centred at 0 and wide variances (intercept: df = 3, location = 0, scale = 10; betas: df = 3, location = 0, scale = 3). For the spatial Gaussian Process and the observation process scale parameters, we adopted the default glmmfields settings and used half-t priors (both gp_theta and gp_sigma: df = 3, location = 0, scale = 5), and 12 knots. To achieve convergence, the models were run for 5000 iterations35.First, univariable models were fitted to estimate unadjusted associations between each environmental factor (CEC, pH, DOWS, EVI, LSTD, slope, and SOC; Table 1; Supplementary Table S1) and high- and low-risk areas. Second, we constructed multivariable models by fitting multiple environmental variables (Supplementary Table S2). Three variables, SOC, slope and EVI showed a strongly right-skewed distribution and were therefore log-transformed prior to GLMM analysis to prevent excessive influence of outliers. All predictor variables were centred to zero mean and scaled to unit standard deviation for analysis, and odds ratios were rescaled back to the original units for ease of interpretation. Prior to fitting the multivariable GLMM, the presence of collinearity among the predictor variables—which were all continuous—was assessed using variance inflation factors (VIFs)36, calculated with the car package and illustrated using scatter plots (Supplementary Fig. S1)36. Three predictor variables showed a VIF greater than 3 (LSTD, ln EVI and pH with VIFs of 6.8, 4.2 and 3.5, respectively). Removal of LSTD and ln EVI reduced all VIFs to below 3, therefore these two variables were excluded from the multivariable regression analysis37.The model performance was assessed by calculating the area under the receiver operating characteristic curve. The predicted probability of being an anthrax high-risk area was determined and depicted on a map of the NCA using a regular grid of points generated throughout the NCA with one point sampled every 500 m.Consent for publicationPermission to publish was granted by the National Institute for Medical Research, Tanzania. More