Ecology

Gibson, R. N. & Yoshiyama, R. M. in Intertidal fishes; life in two worlds (eds. Horn, M. H., Martin, K. L. M. & Chotkowski, M. A.) Intertidal fish communities (Academic Press, 1999).Cox, T. E., Baumgartner, E., Philippoff, J. & Boyle, K. S. Spatial and vertical patterns in the tidepool fish assemblage on the island of O’ahu. Environ. Biol. Fishes 90, 329–342 (2011).Article 

Google Scholar 
Bezerra, L. A. V., Padial, A. A., Mariano, F. B., Garcez, D. S. & Sánchez-Botero, J. I. Fish diversity in tidepools: assembling effects of environmental heterogeneity. Environ. Biol. Fishes 100, 551–563 (2017).Article 

Google Scholar 
Metaxas, A. & Scheibling, R. E. Community structure and organization of tidepools. Mar. Ecol. Prog. Ser. 98, 187–198 (1993).Article 
ADS 

Google Scholar 
Castellanos-Galindo, G. A., Giraldo, A. & Rubio, E. A. Community structure of an assemblage of tidepool fishes on a tropical eastern Pacific rocky shore, Colombia. J. Fish. Biol. 67, 392–408 (2005).Article 

Google Scholar 
Castellanos-Galindo, G. A. & Giraldo, A. Food resource use in a tropical eastern Pacific tidepool fish assemblage. Mar. Biol. 153, 1023–1035 (2008).Article 

Google Scholar 
Moring, J. R. Seasonal absence of fishes in tidepools of a boreal environment (Maine, USA). Hydrobiologia 194, 163–168 (1990).Article 

Google Scholar 
Davis, J. L. D. Spatial and seasonal patterns of habitat partitioning in a guild of southern California tidepool fishes. Mar. Ecol. Prog. Ser. 196, 253–268 (2000).Article 
ADS 

Google Scholar 
Arakaki, S., Tsuchiya, M. & Tokeshi, M. Testing latitudinal patterns of tidepool fish assemblages: local substrate characteristics affect regional-scale trends. Hydrobiologia 733, 45–62 (2014).Article 

Google Scholar 
Arakaki, S. & Tokeshi, M. Assessment of decadal changes in the tidepool fish assemblage of Danjo Islands in the northern East China Sea. Mar. Biodivers. 52, 25 (2022).Article 

Google Scholar 
Bonaca, M. O. & Lipej, L. Factors affecting habitat occupancy of fish assemblage in the Gulf of Trieste (Northern Adriatic Sea). Mar. Ecol. 6, 42–53 (2005).Article 
ADS 

Google Scholar 
Ribeiro, J., Carvalho, G. M., Gonçalves, J. M. S. & Erzini, K. Fish assemblages of shallow intertidal habitats of the Ria Formosa lagoon (South Portugal): influence of habitat and season. Mar Ecol Prog Ser 446, 259–273 (2012).Article 
ADS 

Google Scholar 
Compaire, J. C. et al. Micro- and macroscale factors affecting fish assemblage structure in the rocky intertidal zone. Mar Ecol Prog Ser 610, 175–189 (2019).Article 
ADS 

Google Scholar 
Griffiths, S. P. Spatial and temporal dynamics of temperate Australian rockpool ichthyofaunas. Mar. Freshwater Res. 54, 163–176 (2003).Article 
ADS 

Google Scholar 
Henriques, S., Pais, M. P., Costa, M. J. & Cabral, H. N. Seasonal variability of rocky reef fish assemblages: Detecting functional and structural changes due to fishing effects. J. Sea Res. 79, 50–59 (2013).Article 
ADS 

Google Scholar 
Mendonça, V. et al. What’s in a tide pool? Just as much food web network complexity as in large open ecosystems. PLoS ONE 13, e0200066 (2018).Article 

Google Scholar 
Mahon, R. & Mahon, S. D. Structure and resilience of a tidepool fish assemblage at Barbados. Environ. Biol. Fishes 41, 171–190 (1994).Article 

Google Scholar 
Willis, T. J. & Roberts, C. D. Recolonisation and recruitment of fishes to intertidal rockpools at Wellington, New Zealand. Environ. Biol. Fish. 47, 329–343 (1996).Article 

Google Scholar 
Ho, L.-T., Wang, S.-C., Shao, K.-T., Chen, I.-S. & Chen, H. A long-term monitoring dataset of fish assemblages in rocky tidepools on the northern coast of Taiwan. Sci. Data 7, 84 (2020).Article 

Google Scholar 
Chen, H. et al. Long-term monitoring dataset of fish assemblages impinged at nuclear power plants in northern Taiwan. Sci. Data 2, 150071 (2015).Article 
CAS 

Google Scholar 
Liao, Y. C., Chen, L. S., Shao, K. T. & Tu, Y. Y. Temporal changes in fish assemblage from the impingement data at the second nuclear power plant, northern Taiwan. J. Mar. Sci. Technol. 12, 411–417 (2004).Article 

Google Scholar 
Chen, H., Chen, C.-Y. & Shao, K.-T. Recovery and variation of the coastal fish community following a cold intrusion event in the Penghu Islands, Taiwan. Plos One 15, e0238550 (2020).Article 
CAS 

Google Scholar 
Heard, J. et al. Coastal development threatens Datan area supporting greatest fish diversity at Taoyuan Algal Reef, northwestern Taiwan. Aquat. Conserv.: Mar. Freshw. Ecosyst. 31, 590–604 (2021).Article 

Google Scholar 
Chen, H., Shao, K. T. & Kishino, H. Phylogenetic skew: an index of community diversity. Mol. Ecol. 24, 759–770 (2015).Article 

Google Scholar 
Chen, H., Shao, K.-T. & Kishino, H. Bayesian hierarchical ANOVA model of stochastic seasonality for Diodon holocanthus in northern Taiwan. J. Mar. Sci. Technol. 24, 303–310 (2016).
Google Scholar 
Chen, H., Chen, C.-Y. & Shao, K.-T. Time series dataset of fish assemblages near thermal discharges at nuclear power plants in northern Taiwan. Sci. Data 5, 180055 (2018).Article 

Google Scholar 
Chen, H., Chen, C.-Y., Shao, K.-T. & Gong, G.-C. Spatial and temporal variations in species diversity of fish assemblages near a sewage treatment plant in northern Taiwan. Fish. Sci. 85, 581–590 (2019).Article 
CAS 

Google Scholar 
Shao, K.-T., Chen, J.-P. & Wang, S.-C. in Proceedings of the 5th Indo-Pacific Fish Conference (eds. Seret, B. & Sire, J.-Y.) Biogeography and database of marine fishes in Taiwan waters (French Society of Ichthyology, 1999).Shao, K.-T. et al. A checklist of the fishes of southern Taiwan, northern South China Sea. Raffles Bull. Zool. Suppl. 19, 233–271 (2008).
Google Scholar 
Shen, S.-C. Chief Editor. Fishes of Taiwan. (Department of Zoology, National Taiwan University, 1993).Ho, L-T., Wang, S-C., Shao, K-T., Chen, I-S. & Chen, H. Long-term monitoring dataset of fish assemblages in rocky tidepools on the southern coast of Taiwan, figshare, https://doi.org/10.6084/m9.figshare.20747467.v1 (2022). More

We applied a modified version of a planning framework for CWR conservation25,26 which has been used by numerous countries of Europee.g.29,63,64, Americae.g.65, Africa30 and Asia66,67. We addressed the following main steps of the toolkit (see Spanish version49): (i) CWR checklist, i.e., creating a list of CWR taxa distributed in an area (Supplementary Data 1), (ii) CWR inventory, i.e., taxa selection and collation of ancillary data, including taxonomic data (Supplementary Data 2), (iii) taxa extinction risk assessment (Table 1, Supplementary Data 3), and (iv) a systematic conservation planning assessment, i.e., spatial analyses to assess conservation areas (Fig. 1). We only provide a brief description of steps i-iii, as these are thoroughly described in Goettsch et al.2. Here, we focus on the systematic conservation planning assessment, introducing an approach in order to identify conservation areas for CWR that account for genetic differentiation in a spatially explicit way, through the use of proxies of genetic differentiation (Fig. 1).During the process -framed under the project “Safeguarding Mesoamerican crop wild relatives” (https://www.darwininitiative.org.uk/project/23007/)- more than 100 experts from academic, governmental, and non-governmental organizations from El Salvador, Guatemala, Honduras, Mexico, the UK, and IUCN participated in six workshops, shared data, and provided fundamental knowledge and feedback at each project stage to ensure accurate, reliable and robust information for next steps. The checklist, inventory and risk assessment were collaboratively developed between partners of El Salvador, Guatemala, and Mexico (hereafter, Mesoamerica; Goettsch et al.2). The spatial analysis to identify areas for in situ and ex situ conservation of CWR was done independently by each country.To assess conservation areas of CWR in Mexico, we developed proxies of genetic differentiation that account for evolutionary processes by including historical and environmental drivers of genetic diversity (see the Methods section ‘Proxies of genetic differentiation’). In addition, we used criteria such as information on taxon-specific tolerance to human-modified habitats and IUCN extinction risk category. We applied a systematic conservation planning approach and performed spatial analysis using the software Zonation50. We compared different scenarios to represent genetic diversity of CWR based on potential species distribution models (SDM) and proxies of genetic differentiation.Study areaMesoamerica is a cultural region encompassing the territories of Belize, Guatemala, El Salvador, the southern part of Mexico and parts of Honduras, Nicaragua and Costa Ricasee 2. In this study, we also included the dry areas of northern Mexico that are part of Aridamerica68 and the Nearctic biogeographic realm69 to account for the full extent of the geographic range of many taxa included in the extinction risk assessment2.For the assessment of conservation areas, we focused on Mexico, which is one of the most biodiverse countries in the world70. The Mexican territory covers 80% of the landscapes of the region called Mesoamerica. Its high biological diversity is attributed to its geographic, topographic, climatic, geological and cultural characteristics, which, among other factors, shaped the distribution of an extraordinary variety of ecosystems and species with high levels of endemism and species turnover among different regions32,71,72,73. In particular, the high genetic variation within populations of landraces and CWR is the result of past and ongoing sociocultural processes occurring in a wide range of distinct environmental conditions74,75.(i) CWR checklist and (ii) CWR inventoryThe compiled CWR checklist included ~3000 species and subspecies of 92 genera and 45 families of plants that belong to the same genus of a crop cultivated in Mesoamerica, or wild plant collected for food or other uses in the region (Supplementary Data 1).The first set of criteria were established in preparation for the first stakeholder workshop. The following criteria were applied at the genus level to compile the CWR inventory: (1) occurrence of wild relatives of cultivated plants or crops that were domesticated in Mesoamerica; (2) existence of research groups working on taxa that could support the extinction risk assessment; and (3) relation to a crop of economic and nutritional importance at local, national and regional levels, or cultivars known to require genetic improvement.To narrow the list for the inventory and extinction risk assessment, similar criteria were agreed upon in the same workshop and applied at the species level: (1) native distribution in Mesoamerica, incl. Aridamerica; (2) related to a crop of economic or social importance based on production and nutritional value; (3) related to a taxon for which Mesoamerica is the center of origin or domestication; (4) constitutes part of the primary or secondary gene pool, and in some cases the tertiary gene pool76. The primary gene pool consists of wild plants of the same species as the crop and thus their mating produces strong fertile progeny. The secondary gene pool is composed of wild relatives distinct from cultivated species but closely related as to produce some fertile offspring (same taxonomic series or section in the absence of crossing and genetic diversity information, see the ‘taxon group’ concept proposed by Maxted and collaborators77, Supplementary Note 5). The tertiary gene pool (same subgenus in the taxon group concept) corresponds to CWR that are more distant relatives to the taxa of the primary gene pool, but can have important adaptive traits which can be used with specific breeding techniques. This provided a preliminary list of 514 CWR taxa related to avocado, cotton, amaranth, cocoa, squash, sweet potato, chayote, chili pepper, cempasuchil, bean, sunflower, maize, papaya, potato, vanilla, and yuca (Supplementary Data 2).The list had to be further reduced due to time and funding restrictions to include those genera which when added together would include no more than 250 taxa, and that the taxonomic groups could be comprehensively assessed and their taxa evaluated throughout their entire range. Thus, not all species in the group necessarily met the criteria previously mentioned. See the final Mesoamerican CWR inventory in Supplementary Data 3; see summary in Table 1.(iii) Taxa extinction risk assessmentFull methodological details and results of this section are described in Goettsch et al.2. Summarizing, during the process 224 taxa were evaluated according to the International Union for Conservation of Nature, IUCN, Red List Categories and Criteria78. The IUCN Red List is a critical indicator to identify species most vulnerable to extinction considering a set of criteria, i.e., species’ population trends, size, structure, and geographic ranges. A Red List workshop with the participation of 25 experts from different project partner institutions and IUCN specialists was organized to assess the extinction risk of taxa. The threat analysis included not only species, but subspecies and subpopulations (i.e. races) for some groups (Supplementary Data 3, see summary in Table 1).(iv) Systematic conservation planning assessmentTo undertake the following spatial analyses we focused on the dataset of 224 CWR described above, which is representative of the CWR of the main crops of Mesoamerica (10 genera, Table 1).Species distribution modelingTo compile occurrence records, hundreds of data sources were consulted, including published and personal databases of the project participantse.g.79,80,81,82, the Agrobiodiversity Atlas of Guatemala (https://www.ars.usda.gov/northeast-area/beltsville-md-barc/beltsville-agricultural-research-center/national-germplasm-resources-laboratory/docs/atlas-of-guatemalan-crop-wild-relatives), the Global Biodiversity Information Facility (GBIF, https://www.gbif.org/), and Mexico’s Biodiversity Information System (SNIB, http://snib.mx/).To generate potential species distribution models (SDM), we used more than 13,000 occurrence records (Supplementary Data 4), that were standardized and curated by experts to generate the range maps of taxa as part of the extinction risk assessment, which were published in IUCN Red List (https://www.iucn.org/news/species/202109/threats-crop-wild-relatives-compromising-food-security-and-livelihoods). Spatial resolution of the SDM was 1 km2. SDM were obtained for taxa with more than 20 unique occurrence data in a 1 km2 grid covering the study extent to reduce uncertainty when using smaller sample sizes83. We used 19 bioclimatic variables and other climatic variables, such as annual potential evapotranspiration, aridity index, annual radiation, slope, and altitude84,85,86. Climate data represents annual and seasonal patterns of climate between 1950 and 2000. Also, we used a variable that described the percentage of bare soil and cultivated areas87. Collinearity between variables was assessed with the ‘corselect’ function of the package fuzzySim version 1.088, using a value of 0.8 and the variance inflation factors as criteria to exclude highly correlated variables.We used MaxEnt version 3.3.1, a machine-learning algorithm that uses the maximum entropy principle to identify a target probability distribution, subject to a set of constraints related to the occurrence records and environmental data89,90. Model calibration area for each taxon included those ecoregions where the taxon has been recorded; we used the terrestrial ecoregions dataset69. We did this based on the calibration area or ‘M element’ of the BAM diagram that refers to areas that have been accessible to the taxon via dispersal over relevant periods of time91,92. We randomly sampled 10,000 background localities from the selected areas.To reduce model complexity without compromising model performance, we built several models by varying the feature classes (FC) and regularization multipliers (RM) (see refs. 93,94,95) using R 3.6.096 and ‘ENMeval’ version 0.3.0 package97. FC determines the flexibility of the modeled response to the predictor variables, while the RM penalizes model complexity93. Occurrence records were randomly divided into 70% for model selection, and 30% of data was withheld for model validation. ENMeval carries out an internal partition of localities to test each combination of settings. Therefore, we selected the random k-fold method to divide localities into four bins. We build models with six FC combinations and varied RM values ranging from 0.5 to 4.0 in 0.5 increments. Optimal models were selected using Akaike’s Information Criterion corrected for small sample sizes (⍙AICc = 0). This method penalizes overly complex models and helps to choose those with an optimal number of parameters. However, it has been shown that the number of model parameters may not correctly estimate degrees of freedom98, and that model selection should not be selected solely with one measure99. Thus, we used 30% of the withheld data to test the area under the curve (AUC) of the receiver operating characteristic, and the omission error under a 10 percentile training threshold.We used the ten percentile or minimum training presence threshold to obtain binary maps of the presence and absence of suitable areas for species distribution. We asked experts of each taxonomic group who were also involved in the extinction risk assessment to select one of these two options and to indicate possible overestimated areas, which were then eliminated case by case using the information of Mexican ecoregions100 and watersheds101. Eight models were binarized with the minimum training presence threshold; for the other models we used the 10 percentile threshold. See MaxEnt performance and significance of SDM at Supplementary Data 5. AUC values ranged from 0 to 1; 0.5 indicated a model performance not better than random, while values closer to 1 indicated a better model performance; here we used SDM showing AUC values higher than 0.7. For Phaseolus and Zea, we used SDM that were previously generated by Delgado-Salinas et al.102, and Sánchez González et al.103, respectively. SDM for 116 taxa were validated by experts of each taxonomic group. See references and download links at Supplementary Data 6.For the conservation planning analysis of Mexico, we clipped the models to the Mexican territory, and trimmed the continuous SDM using the binary SDM to keep pixel values of areas with elevated probability of taxa presence. For taxa without SDM, we included the occurrence records of these taxa in the spatial analysis by using the information on observation location, i.e., coordinates (see Supplementary Data 3). This is done by enabling the function ‘species of special interest’ (SSI). See further details in the method section ‘Final conservation analysis’.Proxies of genetic differentiationTo identify proxies of genetic differentiation in an explicit, efficient, and repeatable way, we included environmental and historical drivers of genetic diversity. For this, we first divided Mexico into 27 Holdridge life zones (Supplementary Fig. 2, Supplementary Data 8), which we then subdivided according to phylogeographic studies that have found genetic differentiation among populations of several taxa (see division of each life zone into proxies in Supplementary Fig. 4; Supplementary Fig. 3 provides a general geographical overview of Mexico and main geographic references mentioned in Supplementary Fig. 4). The literature review was done searching for the words “phylogeography” and one of the following: (i) name of the Mexican biogeographic zones, (ii) “Mexico” + an ecosystem name (e.g. “Mexico” “rainforest”) or (iii) “Mexico” + lowlands/highlands. See list of references used in this study in Supplementary Data 9.In addition, we manually reviewed the citations to the most cited papers of the previous search. Reviews and meta-analyses were also included, although we excluded studies performed in CWR to show that our approach can be used without prior information on this group. As more studies on such taxa become available, they can be used to fine-tune the proxies of genetic differentiation. We focused on terrestrial species including plants, animals, and fungi (Supplementary Data 10) except to subdivide a life zone covering the coasts of the California Peninsula, where we could not find studies on terrestrial taxa so we included studies on fish species (see Supplementary Fig. 4).Since most of the life zones cover large territories, and complete phylogeographic congruence among different taxa is uncommon, we targeted to represent general trends that would likely occur across diverse species, instead of trying to represent fine idiosyncratic patterns of genetic differentiation. For instance, although distribution ranges of highland taxa shifted during the Pleistocene climate fluctuations, in general populations persisted (glacial-interglacial periods) within the main mountain ranges, while lowland populations were ephemeral (only glacial periods). So, gene flow among mountain ranges was more limited than within them. As a result, genetic differentiation among mountain ranges of different biogeographic provinces has been widely documented32, so we used this general pattern to subdivide the life zones that occur in highlands. These types of patterns are particularly relevant for a country like Mexico, due to its complex topography, tropical latitude, and geographic features of different ages, which promote population differentiation among the Mexican main geographic features. To translate the phylogeographic information into a spatial context, we used biogeographic regions, basins, topographic or edaphic data to split the life zones into different subzones using the best fitting cartography to represent the phylogeographic patterns (Supplementary Fig. 4).We obtained 102 proxies of genetic differentiation for Mexico (Supplementary Fig. 5). We validated our findings by using available genomic data of an empirical study of a wild relative of maize, the teosinte Zea mays subsp. parviglumis, which was not included in the literature review in order to test the usefulness of our approach regarding the lack of genetic data. The dataset includes ca. 1800 occurrence records and ca. 30,000 SNPs48. Sampling localities were not used for distribution modeling. Admixture groups per population were estimated for K1 to 60. According to the population analysis, Z. mays subsp. parviglumis is structured in 13 genetic clusters along a longitudinal gradient (Fig. 3a–c). We used the K = 13 for plotting based on the Cross-Validation error. The proportion of each genetic cluster was estimated by sampling locality and plotted using pie charts over the map (Supplementary Fig. 6). Then, using the data layer of the SDM subdivided by proxies of genetic differentiation, we extracted which was the proxy most frequent in a 5 km buffer for each sampling locality. The Admixture plot was ordered by all genetic clusters and subdivided by the proxy of genetic differentiation most frequent for each locality. In addition, we calculated a principal component analysis (PCA) and projected into a score plot the first three components. Individual samples were colored by the proxies where they fell in the 5 km buffer (Fig. 3c). To compare how genetic variation was represented by the different scenarios we plotted the proportion of the area of each proxy as given by the potential SDM according to two different scenarios (only considering SDM; combining SDM*PGD) considering 20% of Mexico’s terrestrial area (Fig. 3d). Analyses were run in R version 3.5.196 using the R packages pcadapt version 4.3.3104, ggplot2 version 2_3.3.3105, readr version 1.4.0106, gridExtra version 2.3107, ggnewscale version 0.4.5108, scatterpie version 0.1.5109, pophelper version 2.3.1110, raster version 3.4-5111, rgdal version 1.4-8112, rgl version 0.107.10113, and sp version 1.4-4114,115.Habitat preferenceWe considered habitat preference to refine the presence of CWR in the planning process; thus minimizing commission errors and highlighting areas that more probably contain taxa116. For each taxon, experts assessed its habitat preference (1: high preference; 0.5: low preference; 0.1: no preference) according to the following categories: (i) well-conserved vegetation (i.e. primary vegetation), (ii) human-impacted vegetation (i.e. secondary vegetation), (iii) less intensive rainfed and moisture agriculture, (iv) intensive rainfed and moisture agriculture, (v) irrigated agriculture, (vi) induced and cultivated grasslands and forests, and vii) urban areas (Supplementary Data 11). To spatially delimit these classes, we used the land use cover and vegetation map for Mexico117, and assessed seven main categories of land cover by grouping the map legend (Supplementary Fig. 9). To differentiate between less intensive and intensive cultivated areas, we followed Bellon et al.56, who associated the presence of native maize varieties of Mexico to occur in municipalities with average yields of less than or equal to 3 t ha-1 using agricultural production data from 2010 from the Information System of Agrifood and Fisheries (SIAP), and selected the municipalities with the established average maize yield. We combined the municipality layer with the land cover map to differentiate areas of high and low agricultural intensity. To generate taxon-specific habitat layers, we associated the habitat preference classes established by experts to the land cover map aggregated into seven major land cover categories, using R 3.6.096 and the following packages: raster version 3.4-5111 and rgdal version 1.4-8112. We obtained habitat maps for 116 taxa with SDM.Preliminary analysisWe generated five preliminary scenarios to explore different approaches to include conservation features for maximizing the representation of intraspecific diversity as given by taxa and proxies of genetic differentiation, i.e., representation of proxies within a taxa range (Supplementary Fig. 7): (i) “SDM” scenario, included 116 SDM, which we used as base scenario to examine the representation of taxa and proxies of genetic variability (n = 116); (ii) “SDM + LZ” scenario, included 116 SDM and 27 layers representing Holdridge life zones to consider environmental variation (n = 143); (iii) “SDM + PGD” scenario, included 116 SDM and 102 layers representing each proxy of genetic differentiation individually (n = 218); (iv) “SDM*PGD” scenario, included 5004 input layers representing the intersection of SDM and PGD (n = 5004; combining 116 SDM with 102 proxies resulted in 11,832 layers, but as some of the intersections produced empty outputs given the extension of SDM that do not cover all Mexico, for further analysis we used 5004 input layers with value data. To subdivide the layers, we used ArcGIS version 10.2.2118; to filter the layers, we used R 3.5.196.); (v) “SDM and PGD as ADMU” scenario, included 116 SDM as the main conservation features, while integrating one single layer of proxies of genetic differentiation to consider each of them as planning units by using the ‘Administrative units’ function. Analysis was done in Zonation50,119.We compared the results by assessing 20% of Mexico’s terrestrial area (Fig. 5b) to perform statistical analysis in R 3.5.196 using the following packages: purrr version 0.3.4120, ‘dplyr’ version 1.0.2121, ‘ggplot2’ version 2_3.3.3105, ‘raster’ version 3.4-5111, ‘scales’ version 1.2.0122, ‘sp’ version 1.4-4114,115, ‘tidyr’ version 1.0.2123, and ‘vegan’ version 2.6-2124. The area threshold was established based on Aichi target 11 and on comparisons of performance curves to efficiently represent taxa ranges delimited by SDM and proxies of genetic differentiation (Fig. 6). As using SDM combined with proxies of genetic differentiation showed the highest representation of genetic diversity (“SDM*PGD” scenario), we used this approach for the final analyses.Final conservation analysisWe identified areas of high conservation value for CWR in Mexico by using the software Zonation version 4.050,119, a systematic conservation planning tool that allows optimizing representation of species, taxa, or other conservation features, e.g., proxies of genetic differentiation, in a given study area. The program hierarchically ranks areas by removing cells of low conservation value, as given, for example, by a reduced number of taxa or occurrence of low weighted features, while considering multiple criteria such as the weighting of taxa and habitat preference of taxa. We applied the core-area zonation removal rule (CAZ) to maximize the representation of all conservation features in a minimal possible area51. Zonation generates two main outputs: (a) a hierarchical landscape priority rank map, that allows decision makers establishing different area thresholds to highlight areas of conservation interest; and (b) a representation curve showing species or conservation features range distribution in a given area. The curve also allows identifying how much area is needed to cover a certain taxon range or the distribution of a feature of conservation interest.For the conservation scenarios, we integrated the following inputs in the Zonation software: (1) 5,004 layers, i.e., SDM intersected with proxies of genetic differentiation (as described by “SDM*PGD” scenario, Fig. 4), (2) occurrence records of 98 taxa; only for those taxa without SDM, see Supplementary Data 3), (3) taxa specific habitat layers (according to Supplementary Data 11 and Supplementary Fig. 9), and (4) IUCN threat category (Supplementary Data 3) as an additional parameter to weight taxa differently to consider their vulnerability to extinction, see details below. See Zonation configuration at Supplementary Note 6.Data from different sources can be mixed in the same analysis, which is useful to not lose or omit information of any taxa of interest in the assessment. Here, we included information of a total of 214 taxa (see Supplementary Data 3). Distribution data of 116 taxa were represented by 5004 layers that resulted from combining 116 SDM and 102 PGD. This approach showed the highest proportion of area of taxa ranges (on average 41%) and highest representation of PGD within the area of each taxon (on average 76%; Fig. 4; see description in the main text). For some taxa, e.g. Cucurbita pepo, Physalis cinerascens, and Zea mays information on its distribution was assessed at subspecies level rather than at species level, explaining the difference in numbers of CWR taxa.In addition, we included occurrence data of 98 taxa without SDM to prevent missing important areas of taxa known distribution that are important to conserve (see Supplementary Data 3). We enabled the function ‘species of special interest’ (SSI) of Zonation, and included a SSI feature list file, listing the taxon names, as well as taxon-specific coordinate file for each of the 98 taxa that have been reviewed by the experts of each group. The spatial reference system was World Mercator projection. Occurrence data and SDM are treated similarly in the Zonation analysis, i.e., cells where taxa occur will be retained in the solution as long as possible to maximize its representation in the solution.We assigned weights to the 116 taxa with SDM by using IUCN threat categories (according to Supplementary Data 3), giving highest values to taxa with highest risk of extinction that urgently need management actions to further avoid genetic erosion. By including conservation feature weights, Zonation estimates the conservation value of a cell not only based on the presences of a taxa and their distribution range, but also on the weight. A high weight indicates a high conservation value of cells where these taxa are distributed. As there is no rule for weight setting, we assigned values between 1 and 0 regardless of taxa distribution ranges, which is automatically considered in the Zonation algorithm to guarantee the representation of locations where limited-range distributed taxa occur within the most valuable conservation area. Thus, weights were assigned as follows: Critically endangered, CR: 1; Endangered, EN: 1; Vulnerable, VU: 0.8; Near threatened, NT: 0.5; Data deficient, DD: 0.3; Least concern, LC: 0.2 Not evaluated, NE: 0.1. SSI taxa were all weighted similarly with 1 in order to represent the 98 SSI taxa and their occurrences in the top fraction of the most valuable conservation area, as these areas could be considered as ‘irreplaceable’ in terms of conservation. The conservation of these taxa that are only known in a few locations is crucial to maintain their populations. Information on weights for taxa with and without SDM is included in the file that lists the 5004 conservation features and the SSI file, respectively.To include the information on habitat, we included 116 habitat maps which guide the selection of cells to areas where its presence is more probable (see the Methods section: “Habitat preference”). This option can only be used for taxa represented by a raster layer, and is not available for SSI taxa included via occurrence records. By enabling the “landscape condition” option of Zonation, each habitat map is linked to a specific conservation feature layer. Areas with unfavorable habitats will quickly be masked out during the selection of cells in order to obtain a solution that favors conservation areas within areas of preferred habitat.We generated three final scenarios to identify conservation areas for (a) all taxa, (b) taxa exclusively distributing in natural vegetation, and (c) taxa associated with a wider range of habitats such as natural vegetation, agricultural and urban areas. The Zonation configuration remained similar among the three scenarios. When taxa were not included in a given scenario, we assigned a value weight of 0. This excluded the feature to be considered for the hierarchical prioritization of the landscape, but still allowed to evaluate the taxa during post-processing.To evaluate the spatial results (Supplementary Fig. 11), we analyzed performance curves to represent proxies of genetic differentiation within each taxon range (Supplementary Fig. 12). Also, we considered the most valuable 20% area of Mexico to calculate the coincidence of the three scenarios (Supplementary Fig. 13), and the overlap with federal protected areas125 and indigenous regions126,127 (Supplementary Fig. 14), and land cover data used in the analyses (Supplementary Figs. 9, 15).We discussed the proposed methodological framework, input layer and criteria during a fourth workshop in Mexico. It is worth mentioning that we ran several analyses including additional layers, such as areas where indigenous communities live that promote the presence of CWR in the landscape6. However, as the output indicated no evident difference by including this information, final analyses did not consider these data. We neither included protected areas nor tried to expand on the current 12% protected area system, because most management plans do not specifically address CWR management (but see the management program of the Protected Area of ‘Sierra de Manantlán’128), and thus generally do not adequately plan for wild and native genetic resources129. We also discussed different approaches to consider connectivity for taxa, habitats and proxies of genetic differentiation in the Zonation processing. Still, we finally decided to run the analysis without particularly accounting for connectivity as we had no taxa-specific information on dispersal abilities or possible effects of fragmentation, and we did not want to lose efficiency of the solution to represent taxa by or include lower-quality habitats by forcing the solution to an aggregation of pixels.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

Blatchford, M. L., Mannaerts, C. M., Zeng, Y., Nouri, H. & Karimi, P. Status of accuracy in remotely sensed and in-situ agricultural water productivity estimates: A review. Remote Sensing of Environment 234, 111413, https://doi.org/10.1016/j.rse.2019.111413 (2019).Article 
ADS 

Google Scholar 
Geerts, S. & Raes, D. Deficit irrigation as an on-farm strategy to maximize crop water productivity in dry areas. Agricultural Water Management 96, 1275–1284, https://doi.org/10.1016/j.agwat.2009.04.009 (2009).Article 

Google Scholar 
Hellegers, P., Soppe, R., Perry, C. & Bastiaanssen, W. Combining remote sensing and economic analysis to support decisions that affect water productivity. Irrigation Science 27, 243–251, https://doi.org/10.1007/s00271-008-0139-7 (2009).Article 

Google Scholar 
Bastiaanssen, W. G. M. & Steduto, P. The water productivity score (WPS) at global and regional level: Methodology and first results from remote sensing measurements of wheat, rice and maize. The Science of the total environment 575, https://doi.org/10.1016/j.scitotenv.2016.09.032 (2017).Seneviratne, S. I. et al. Investigating soil moisture–climate interactions in a changing climate: A review. Earth Science Reviews 99, https://doi.org/10.1016/j.earscirev.2010.02.004 (2010).Hu, X., Shi, L., Lin, L. & Zha, Y. Nonlinear boundaries of land surface temperature–vegetation index space to estimate water deficit index and evaporation fraction. Agricultural and Forest Meteorology 279, https://doi.org/10.1016/j.agrformet.2019.107736 (2019).Bowen, I. S. The Ratio of Heat Losses by Conduction and by Evaporation from any Water Surface. Physical Review 27, 779–787, https://doi.org/10.1103/PhysRev.27.779 (1926).Article 
ADS 
CAS 
MATH 

Google Scholar 
Penman, H. L. Natural evaporation from open water, hare soil and grass. Proceedings of the Royal Society of London. Series A, Mathematical and physical sciences 193, https://doi.org/10.1098/rspa.1948.0037 (1948).Monteith, J. L. Evaporation and environment. The stage and movement of water in living organisms. Symp.soc.exp.biol.the Company of Biologists (1965).Wang, K. & Dickinson, R. E. A review of global terrestrial evapotranspiration: Observation, modeling, climatology, and climatic variability. Reviews of Geophysics 50, https://doi.org/10.1029/2011RG000373 (2012).Bastiaanssen, W. G. et al. A remote sensing surface energy balance algorithm for land (SEBAL) Part 1: Fomulation. Journal of hydrology 212, 213–229, https://doi.org/10.1016/S0022-1694(98)00253-4 (1998).Article 
ADS 

Google Scholar 
Bastiaanssen, W. G. M. et al. A remote sensing surface energy balance algorithm for land (SEBAL) Part 2. Validation. Journal of Hydrology 212, https://doi.org/10.1016/S0022-1694(98)00254-6 (1998).Su, Z. The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes. Hydrology and Earth System Science 6, 85–99, https://doi.org/10.5194/hess-6-85-2002 (2002).Article 
ADS 

Google Scholar 
Norman, J. M., Kustas, W. P. & Humes, K. S. Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface temperature. Agricultural and Forest Meteorology 77, https://doi.org/10.1016/0168-1923(95)02265-y (1995).Mu, Q., Heinsch, F. A., Zhao, M. & Running, S. W. Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sensing of Environment 111, https://doi.org/10.1016/j.rse.2007.04.015 (2007).Mu, Q., Zhao, M. & Running, S. W. Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sensing of Environment 115, 1781–1800, https://doi.org/10.1016/j.rse.2011.02.019 (2011).Article 
ADS 

Google Scholar 
Fisher, J. B., Tu, K. P. & Baldocchi, D. D. Global estimates of the land–atmosphere water flux based on monthly AVHRR and ISLSCP-II data, validated at 16 FLUXNET sites. Remote Sensing of Environment 112, 901–919, https://doi.org/10.1016/j.rse.2007.06.025 (2008).Article 
ADS 

Google Scholar 
Kim, H. W., Hwang, K., Mu, Q., Lee, S. O. & Choi, M. Validation of MODIS 16 global terrestrial evapotranspiration products in various climates and land cover types in Asia. KSCE Journal of Civil Engineering 16, https://doi.org/10.1007/s12205-012-0006-1 (2012).Velpuri, N. M., Senay, G. B., Singh, R. K., Bohms, S. & Verdin, J. P. A comprehensive evaluation of two MODIS evapotranspiration products over the conterminous United States: Using point and gridded FLUXNET and water balance ET. Remote Sensing of Environment 139, https://doi.org/10.1016/j.rse.2013.07.013 (2013).Jin, X. et al. Estimation of water productivity in winter wheat using the AquaCrop model with field hyperspectral data. Precision Agriculture 19, 1–17, https://doi.org/10.1007/s11119-016-9469-2 (2016).Article 

Google Scholar 
Felix, R., Clement, A., Igor, S. & Oscar, R. Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection. Remote Sensing 5, 1704–1733, https://doi.org/10.3390/rs5041704 (2013).Article 

Google Scholar 
Lu, Y. et al. Assimilation of soil moisture and canopy cover data improves maize simulation using an under-calibrated crop model. Agricultural Water Management 252, https://doi.org/10.1016/j.agwat.2021.106884 (2021).Jin, X., Kumar, L., Li, Z., Feng, H. & Wang, J. A review of data assimilation of remote sensing and crop models. European Journal of Agronomy 92, https://doi.org/10.1016/j.eja.2017.11.002 (2018).Weiss, M., Jacob, F. & Duveiller, G. Remote sensing for agricultural applications: A meta-review. Remote Sensing of Environment 236, https://doi.org/10.1016/j.rse.2019.111402 (2019).Jin, X. et al. Winter wheat yield estimation based on multi-source medium resolution optical and radar imaging data and the AquaCrop model using the particle swarm optimization algorithm. ISPRS Journal of Photogrammetry and Remote Sensing 126, 24–37 (2017).Article 
ADS 

Google Scholar 
Tao, F., Rötter, R. P., Palosuo, T., Díaz-Ambrona, C. G. H. & Schulman, A. H. Contribution of crop model structure, parameters and climate projections to uncertainty in climate change impact assessments. Global Change Biology 24, https://doi.org/10.1111/gcb.14019 (2017).Jin, X. et al. A review of data assimilation of remote sensing and crop models. European Journal of Agronomy 92, 141–152, https://doi.org/10.1016/j.eja.2017.11.002 (2018).Article 

Google Scholar 
Anikó, K. et al. Statistical modelling of crop yield in Central Europe using climate data and remote sensing vegetation indices. Agricultural and Forest Meteorology 260-261, 300–320, https://doi.org/10.1016/j.agrformet.2018.06.009 (2018).Article 

Google Scholar 
Wang, Y., Zhang, Z., Feng, L., Du, Q. & Runge, T. Combining Multi-Source Data and Machine Learning Approaches to Predict Winter Wheat Yield in the Conterminous United States. Remote Sensing 12, 1232, https://doi.org/10.3390/rs12081232 (2020).Article 
ADS 

Google Scholar 
Franz, T. E. et al. The role of topography, soil, and remotely sensed vegetation condition towards predicting crop yield. Field Crops Research 252, https://doi.org/10.1016/j.fcr.2020.107788 (2020).Noland, R. L. et al. Estimating alfalfa yield and nutritive value using remote sensing and air temperature. Field Crops Research 222, 189–196, https://doi.org/10.1016/j.fcr.2018.01.017 (2018).Article 

Google Scholar 
Cao, J., Zhang, Z., Luo, Y., Zhang, L. & Tao, F. Wheat yield predictions at a county and field scale with deep learning, machine learning, and google earth engine. European Journal of Agronomy, 126204, https://doi.org/10.1016/j.eja.2020.126204 (2021).Jacinta, H. & Kerrie, M. Statistical Machine Learning Methods and Remote Sensing for Sustainable Development Goals: A Review. Remote Sensing 10, 1365, https://doi.org/10.3390/rs10091365 (2018).Article 

Google Scholar 
Jin, X., Liu, S., Baret, F., Hemerlé, M. & Comar, A. Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery. Remote Sensing of Environment 198, 105–114, https://doi.org/10.1016/j.rse.2017.06.007 (2017).Article 
ADS 

Google Scholar 
Maimaitijiang, M. et al. Soybean yield prediction from UAV using multimodal data fusion and deep learning. Remote Sensing of Environment 237, 111599, https://doi.org/10.1016/j.rse.2019.111599 (2020).Article 
ADS 

Google Scholar 
Hossein, A., Mohsen, A., Davoud, A., Salehi, S. H. & Soheil, R. Machine Learning Regression Techniques for the Silage Maize Yield Prediction Using Time-Series Images of Landsat 8 OLI. IEEE Journal of Selected Topics in Applied Earth Observations Remote Sensing PP, 1–15, https://doi.org/10.1109/JSTARS.2018.2823361 (2018).Johansen, K. et al. Predicting Biomass and Yield in a Tomato Phenotyping Experiment Using UAV Imagery and Random Forest. Frontiers in Artificial Intelligence 3, 28, https://doi.org/10.3389/frai.2020.00028 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Zhang, L., Ding, X., Shen, Y., Wang, Z. & Wang, X. Spatial Heterogeneity and Influencing Factors of Agricultural Water Use Efficiency in China. Resources and Environment in the Yangtze Basin 28, https://doi.org/10.11870/cjlyzyyhj201904008 (2019).Cheng, M. et al. Satellite time series data reveal interannual and seasonal spatiotemporal evapotranspiration patterns in China in response to effect factors. Agric. Water Manage. 255, https://doi.org/10.1016/j.agwat.2021.107046 (2021).Zhou, L. Comprehensive agricultural regionalization in China. (Agricultural Press of China, 1985).Luo, Y., Zhang, Z., Chen, Y., Li, Z. & Tao, F. ChinaCropPhen1km: A high-resolution crop phenological dataset for three staple crops in China during 2000-2015 based on LAI products. Figshare https://doi.org/10.6084/m9.figshare.8313530.v6 (2019).Luo, Y., Zhang, Z., Chen, Y., Li, Z. & Tao, F. ChinaCropPhen1km: a high-resolution crop phenological dataset for three staple crops in China during 2000–2015 based on leaf area index (LAI) products. Earth System Science Data 12, 197–214, https://doi.org/10.5194/essd-12-197-2020 (2020).Article 
ADS 

Google Scholar 
Song, D. Second China Soil Survey. (Chinese Science Press, 1979).Zhang, T., Yang, X., Wang, H., Li, Y. & Ye, Q. Climatic and technological ceilings for Chinese rice stagnation based on yield gaps and yield trend pattern analysis. Global Change Biology 20, 1289–1298, https://doi.org/10.1111/gcb.12428 (2014).Article 
ADS 
PubMed 

Google Scholar 
Chen, Y., Zhang, Z. & Tao, F. Improving regional winter wheat yield estimation through assimilation of phenology and leaf area index from remote sensing data. European Journal of Agronomy 101, 163–173, https://doi.org/10.1016/j.eja.2018.09.006 (2018).Article 

Google Scholar 
Cheng, M. et al. Combining multi-indicators with machine-learning algorithms for maize yield early prediction at the county-level in China. Agricultural and Forest Meteorology 323, https://doi.org/10.1016/j.agrformet.2022.109057 (2022).Amir, J. & Sinclair, T. A model of the temperature and solar-radiation effects on spring wheat growth and yield. Field Crops Research 28, 47–58, https://doi.org/10.1016/0378-4290(91)90073-5 (1991).Article 

Google Scholar 
Prince, S. D., Haskett, J., Steininger, M. & Wright, S. R. Net Primary Production of U.S. Midwest Croplands from Agricultural Harvest Yield Data. Ecological Applications 11, 1194–1205, https://doi.org/10.1890/1051-0761(2001)011[1194:NPPOUS]2.0.CO;2 (2001).Article 

Google Scholar 
Gilardelli, C. et al. Downscaling rice yield simulation at sub-field scale using remotely sensed LAI data. European journal of agronomy 103, 108–116, https://doi.org/10.1016/j.eja.2018.12.003 (2019).Article 

Google Scholar 
Shakoor, R., Hassan, M. Y., Raheem, A. & Wu, Y.-K. Wake effect modeling: A review of wind farm layout optimization using Jensen׳ s model. Renewable and Sustainable Energy Reviews 58, 1048–1059, https://doi.org/10.1016/j.rser.2015.12.229 (2016).Article 

Google Scholar 
Breiman, L. Random Forests. Machine Learning https://doi.org/10.1023/A:1010933404324 (2001).Article 
MATH 

Google Scholar 
Li, L. et al. Crop yield forecasting and associated optimum lead time analysis based on multi-source environmental data across China. Agricultural and Forest Meteorology 308–309, https://doi.org/10.1016/j.agrformet.2021.108558 (2021).Wang, L. A., Zhou, X., Zhu, X., Dong, Z. & Guo, W. Estimation of biomass in wheat using random forest regression algorithm and remote sensing data. The Crop Journal 4, 212–219, https://doi.org/10.1016/j.cj.2016.01.008 (2016).Article 

Google Scholar 
Feng, P. et al. Dynamic wheat yield forecasts are improved by a hybrid approach using a biophysical model and machine learning technique. Agricultural and Forest Meteorology 285-286, 107922, https://doi.org/10.1016/j.agrformet.2020.107922 (2020).Article 
ADS 

Google Scholar 
Lu, F., Sun, Y. & Hou, F. Using UAV Visible Images to Estimate the Soil Moisture of Steppe. Water 12, 2334, https://doi.org/10.3390/w12092334 (2020).Article 
CAS 

Google Scholar 
Wang, S. et al. High spatial resolution monitoring land surface energy, water and CO2 fluxes from an Unmanned Aerial System. Remote Sensing of Environment 229, 14–31, https://doi.org/10.1016/j.rse.2019.03.040 (2019).Article 
ADS 

Google Scholar 
Chen, Y. et al. Comparison of satellite-based evapotranspiration models over terrestrial ecosystems in China. Remote Sensing of Environment 140, 279–293, https://doi.org/10.1016/j.rse.2013.08.045 (2014).Article 
ADS 

Google Scholar 
Peralta, N., Assefa, Y., Du, J., Barden, C. & Ciampitti, I. Mid-Season High-Resolution Satellite Imagery for Forecasting Site-Specific Corn Yield. Remote Sensing 8, 848, https://doi.org/10.3390/rs8100848 (2016).Article 
ADS 

Google Scholar 
Russello, H. Convolutional neural networks for crop yield prediction using satellite images. IBM Center for Advanced Studies (2018).You, J., Li, X., Low, M., Lobell, D. & Ermon, S. in Proceedings of the AAAI Conference on Artificial Intelligence.Moran, P. A. Notes on continuous stochastic phenomena. Biometrika 37, 17–23 (1950).Article 
MathSciNet 
CAS 

Google Scholar 
Imran, M., Stein, A. & Zurita-Milla, R. Using geographically weighted regression kriging for crop yield mapping in West Africa. International Journal of Geographical Information Systems 29, 234–257, https://doi.org/10.1080/13658816.2014.959522 (2015).Article 

Google Scholar 
Harries, K. Extreme spatial variations in crime density in Baltimore County, MD. Geoforum 37, 404–416, https://doi.org/10.1016/j.geoforum.2005.09.004 (2006).Article 

Google Scholar 
Ghulam, A. et al. Remote Sensing Based Spatial Statistics to Document Tropical Rainforest Transition Pathways. Remote Sensing 7, 6257–6279, https://doi.org/10.3390/rs70506257 (2015).Article 
ADS 

Google Scholar 
Maimaitijiang, M., Ghulam, A., Sandoval, J. S. O. & Maimaitiyiming, M. Drivers of land cover and land use changes in St. Louis metropolitan area over the past 40 years characterized by remote sensing and census population data. International Journal of Applied Earth Observation Geoinformation 35, 161–174, https://doi.org/10.1016/j.jag.2014.08.020 (2015).Article 
ADS 

Google Scholar 
Cheng, M. Long time series (2001-2015) high-resolution crop yield and water productivity dataset of China, Zenodo, https://doi.org/10.5281/zenodo.5121842 (2021).Martens, B., Miralles, D. G., Lievens, H., Schalie, R. D. & Verhoest, N. GLEAM v3: Satellite-based land evaporation and root-zone soil moisture. Geoscientific Model Development 10, https://doi.org/10.5194/gmd-10-1903-2017 (2017).Wang, W., Cui, W., Wang, X. & Chen, X. Evaluation of GLDAS-1 and GLDAS-2 forcing data and Noah model simulations over China at the monthly scale. Journal of Hydrometeorology 17, 2815–2833, https://doi.org/10.1175/JHM-D-15-0191.1 (2016).Article 
ADS 

Google Scholar 
Chen, X. et al. Development of a 10-year (2001–2010) 0.1° data set of land-surface energy balance for mainland China. Atmospheric Chemistry and Physics 14, 14471–14518, https://doi.org/10.5194/acp-14-13097-2014 (2014).Article 
ADS 
CAS 

Google Scholar 
Ramoelo, A. et al. Validation of Global Evapotranspiration Product (MOD16) using Flux Tower Data in the African Savanna, South Africa. Remote Sensing 6, https://doi.org/10.3390/rs6087406 (2014).Yang, X., Yong, B., Ren, L., Zhang, Y. & Long, D. Multi-scale validation of GLEAM evapotranspiration products over China via ChinaFLUX ET measurements. International Journal of Remote Sensing https://doi.org/10.1080/01431161.2017.1346400 (2017).Article 

Google Scholar 
Hu, G., Jia, L. & Menenti, M. Comparison of MOD16 and LSA-SAF MSG evapotranspiration products over Europe for 2011. Remote Sensing of Environment 156, 510–526, https://doi.org/10.1016/j.rse.2014.10.017 (2015).Article 
ADS 

Google Scholar 
Khan, M. S., Liaqat, U. W., Baik, J. & Choi, M. Stand-alone uncertainty characterization of GLEAM, GLDAS and MOD16 evapotranspiration products using an extended triple collocation approach. Agricultural and Forest Meteorology 252, 256–268, https://doi.org/10.1016/j.agrformet.2018.01.022 (2018).Article 
ADS 

Google Scholar 
Glenn, E. P. et al. Scaling sap flux measurements of grazed and ungrazed shrub communities with fine and coarse-resolution remote sensing. Ecohydrology 1, 316–329, https://doi.org/10.1002/eco.19 (2008).Article 

Google Scholar 
Gamon, J. A. Reviews and Syntheses: optical sampling of the flux tower footprint. Biogeosciences 12, 4509–4523, https://doi.org/10.5194/bg-12-4509-2015 (2015).Article 
ADS 

Google Scholar 
Cai, Y. et al. Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches. Agricultural and Forest Meteorology 274, 144–159, https://doi.org/10.1016/j.agrformet.2019.03.010 (2019).Article 
ADS 

Google Scholar 
Chen, X. et al. Prediction of Maize Yield at the City Level in China Using Multi-Source Data. Remote Sensing 13, https://doi.org/10.3390/rs13010146 (2021).Guo, Y. et al. Integrated phenology and climate in rice yields prediction using machine learning methods. Ecological Indicators 120, 106935, https://doi.org/10.1016/j.ecolind.2020.106935 (2021).Article 

Google Scholar 
Yuan, W. et al. Estimating crop yield using a satellite-based light use efficiency model. Ecological Indicators 60, 702–709, https://doi.org/10.1016/j.ecolind.2015.08.013 (2016).Article 

Google Scholar 
Anandhi, A. Growing degree days – Ecosystem indicator for changing diurnal temperatures and their impact on corn growth stages in Kansas. Ecological Indicators 61, 149–158, https://doi.org/10.1016/j.ecolind.2015.08.023 (2016).Article 

Google Scholar 
Wart, J. V. Estimating Crop Yield Potential At National Scales. Field Crops Research 143, 34–43, https://doi.org/10.1016/j.fcr.2012.11.018 (2013).Article 

Google Scholar 
Kang, Y. S. et al. Yield prediction and validation of onion (Allium cepa L.) using key variables in narrowband hyperspectral imagery and effective accumulated temperature. Computers and Electronics in Agriculture 178, https://doi.org/10.1016/j.compag.2020.105667 (2020).Long, D., Singh, V. P. & Li, Z.-L. How sensitive is SEBAL to changes in input variables, domain size and satellite sensor? Journal of Geophysical Research: Atmospheres 116, https://doi.org/10.1029/2011jd016542 (2011).Liu, Z., Wang, L. & Wang, S. Comparison of Different GPP Models in China Using MODIS Image and ChinaFLUX Data. Remote Sensing 6, 10215–10231, https://doi.org/10.3390/rs61010215 (2014).Article 
ADS 

Google Scholar 
Edreira, J., Guilpart, N., Sadras, V., Cassman, K. G. & Grassini, P. Water productivity of rainfed maize and wheat: A local to global perspective. Agricultural and Forest Meteorology 259, 364–373, https://doi.org/10.1016/j.agrformet.2018.05.019 (2018).Article 
ADS 

Google Scholar 
Li, H. et al. Water Use Characteristics of Maize-Green Manure Intercropping Under Different Nitrogen Application Levels in the Oasis Irrigation Area Scientia Agricultura Sinica 54, 2608–2618 (2021).
Google Scholar 
Wang, S., Ibrom, A., Bauer-Gottwein, P. & Garcia, M. Incorporating diffuse radiation into a light use efficiency and evapotranspiration model: An 11-year study in a high latitude deciduous forest. Agricultural and Forest Meteorology https://doi.org/10.1016/j.agrformet.2017.10.023 (2018).Article 

Google Scholar 
Cheng, M. High-resolution crop yield and water productivity dataset generated using random forest and remote sensing. Zenodo https://doi.org/10.5281/zenodo.6444614 (2022). More

De Meyer, M., Robertson, M., Peterson, A. & Mansell, M. Ecological niches and potential geographical distributions of Mediterranean fruit fly (Ceratitis capitata) and Natal fruit fly (Ceratitis rosa). J. Biogeogr. 35, 270–281 (2008).
Google Scholar 
Nguyen, A. D. et al. Trade-offs in cold resistance at the northern range edge of the common woodland ant Aphaenogaster picea (Formicidae). Am. Nat. 194, E151–E163 (2019).Article 
PubMed 

Google Scholar 
Gilioli, G. et al. Non-linear physiological responses to climate change: the case of Ceratitis capitata distribution and abundance in Europe. Biol. Invasions 24, 261–279 (2022).Article 

Google Scholar 
Lancaster, L. T., Dudaniec, R. Y., Hansson, B. & Svensson, E. I. Latitudinal shift in thermal niche breadth results from thermal release during a climate-mediated range expansion. J. Biogeogr. 42, 1953–1963 (2015).Article 

Google Scholar 
Hallas, R., Schiffer, M. & Hoffmann, A. A. Clinal variation in Drosophila serrata for stress resistance and body size. Genet. Res. 79, 141–148 (2002).Article 
PubMed 

Google Scholar 
Hoffmann, A. A., Anderson, A. & Hallas, R. Opposing clines for high and low temperature resistance in Drosophila melanogaster. Ecol. Lett. 5, 614–618 (2002).Article 

Google Scholar 
Ragland, G. & Kingsolver, J. Influence of seasonal timing on thermal ecology and thermal reaction norm evolution in Wyeomyia smithii. J. Evol. Biol. 20, 2144–2153 (2007).Article 
CAS 
PubMed 

Google Scholar 
MacMillan, H. A. & Sinclair, B. J. Mechanisms underlying insect chill-coma. J. Insect Physiol. 57, 12–20 (2011).Article 
CAS 
PubMed 

Google Scholar 
Neilson, E. W. et al. There’sa storm a-coming: Ecological resilience and resistance to extreme weather events. Ecol. Evol. 10, 12147–12156 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Overgaard, J., Hoffmann, A. A. & Kristensen, T. N. Assessing population and environmental effects on thermal resistance in Drosophila melanogaster using ecologically relevant assays. J. Therm. Biol. 36, 409–416 (2011).Article 

Google Scholar 
Maysov, A. Chill coma temperatures appear similar along a latitudinal gradient, in contrast to divergent chill coma recovery times, in two widespread ant species. J. Exp. Biol. 217, 2650–2658 (2014).Article 
PubMed 

Google Scholar 
David, R. J. et al. Cold stress tolerance in Drosophila: analysis of chill coma recovery in D. melanogaster. J. therm. biol. 23, 291–299 (1998).Article 

Google Scholar 
Overgaard, J. & MacMillan, H. A. The integrative physiology of insect chill tolerance. Annu. Rev. Physiol. 79, 187–208 (2017).Article 
CAS 
PubMed 

Google Scholar 
Andersen, M. K. & Overgaard, J. The central nervous system and muscular system play different roles for chill coma onset and recovery in insects. Comp. Biochem. Physiol. A: Mol. Integr. Physiol. 233, 10–16 (2019).Article 
CAS 

Google Scholar 
Macdonald, S., Rako, L., Batterham, P. & Hoffmann, A. Dissecting chill coma recovery as a measure of cold resistance: evidence for a biphasic response in Drosophila melanogaster. J. Insect Physiol. 50, 695–700 (2004).Article 
CAS 
PubMed 

Google Scholar 
Gibert, P., Moreteau, B., Pétavy, G., Karan, D. & David, J. R. Chill-coma tolerance, a major climatic adaptation among Drosophila species. Evolution 55, 1063–1068 (2001).Article 
CAS 
PubMed 

Google Scholar 
Ayrinhac, A. et al. Cold adaptation in geographical populations of Drosophila melanogaster: phenotypic plasticity is more important than genetic variability. Funct. Ecol. 18, 700–706 (2004).Article 

Google Scholar 
Castañeda, L. E., Lardies, M. A. & Bozinovic, F. Interpopulational variation in recovery time from chill coma along a geographic gradient: a study in the common woodlouse, Porcellio laevis. J. Insect Physiol. 51, 1346–1351 (2005).Article 
PubMed 

Google Scholar 
Tonione, M. A., Cho, S. M., Richmond, G., Irian, C. & Tsutsui, N. D. Intraspecific variation in thermal acclimation and tolerance between populations of the winter ant Prenolepis imparis. Ecol. Evol. 10, 4749–4761 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Karl, I., Janowitz, S. A. & Fischer, K. Altitudinal life-history variation and thermal adaptation in the copper butterfly Lycaena tityrus. Oikos 117, 778–788 (2008).Article 

Google Scholar 
Ghalambor, C. K., Huey, R. B., Martin, P. R., Tewksbury, J. J. & Wang, G. Are mountain passes higher in the tropics? Janzen’s hypothesis revisited. Integr. Comp. Biol. 46, 5–17 (2006).Article 
PubMed 

Google Scholar 
Addo-Bediako, A., Chown, S. L. & Gaston, K. J. Thermal tolerance, climatic variability and latitude. In Proceedings of the Royal Society of London. Series B: Biological Sciences 267, 739–745 (2000).Poikela, N., Tyukmaeva, V., Hoikkala, A. & Kankare, M. Multiple paths to cold tolerance: the role of environmental cues, morphological traits and the circadian clock gene vrille. BMC ecol. Evol. 21, 1–20 (2021).
Google Scholar 
Andersen, J. L. et al. How to assess Drosophila cold tolerance: chill coma temperature and lower lethal temperature are the best predictors of cold distribution limits. Funct. Ecol. 29, 55–65 (2015).Article 

Google Scholar 
Papadopoulos, N., Katsoyannos, B., Carey, J. & Kouloussis, N. Seasonal and annual occurrence of the Mediterranean fruit fly (Diptera: Tephritidae) in northern Greece. Ann. Entomol. Soc. Am. 94, 41–50 (2001).Article 

Google Scholar 
Malacrida, A. et al. Globalization and fruitfly invasion and expansion: the medfly paradigm. Genetica 131, 1–9 (2007).Article 
CAS 
PubMed 

Google Scholar 
Egartner, A., Lethmayer, C., Gottsberger, R. A. & Blümel, S. In Joint Meeting of the IOBC-WPRS Working Groups “Pheromones and other semiochemicals in integrated production” & “Integrated Protection of Fruit Crops” at. 143–152.Nyamukondiwa, C., Kleynhans, E. & Terblanche, J. S. Phenotypic plasticity of thermal tolerance contributes to the invasion potential of mediterranean fruit flies (Ceratitis capitata). Ecol. Entomol. 35, 565–575 (2010).Article 

Google Scholar 
Weldon, C. W., Terblanche, J. S. & Chown, S. L. Time-course for attainment and reversal of acclimation to constant temperature in two Ceratitis species. J. Therm. Biol. 36, 479–485 (2011).Article 

Google Scholar 
Pujol-Lereis, L. M., Rabossi, A. & Quesada-Allué, L. A. Analysis of survival, gene expression and behavior following chill-coma in the medfly Ceratitis capitata: effects of population heterogeneity and age. J. Insect Physiol. 71, 156–163 (2014).Article 
CAS 
PubMed 

Google Scholar 
Pujol-Lereis, L. M., Fagali, N. S., Rabossi, A., Catalá, Á. & Quesada-Allué, L. A. Chill-coma recovery time, age and sex determine lipid profiles in Ceratitis capitata tissues. J. Insect Physiol. 87, 53–62 (2016).Article 
CAS 
PubMed 

Google Scholar 
Weldon, C. W., Nyamukondiwa, C., Karsten, M., Chown, S. L. & Terblanche, J. S. Geographic variation and plasticity in climate stress resistance among southern African populations of Ceratitis capitata (Wiedemann)(Diptera: Tephritidae). Sci. Rep. 8, 1–13 (2018).Article 
CAS 

Google Scholar 
Nyamukondiwa, C., Weldon, C. W., Chown, S. L., le Roux, P. C. & Terblanche, J. S. Thermal biology, population fluctuations and implications of temperature extremes for the management of two globally significant insect pests. J. Insect Physiol. 59, 1199–1211 (2013).Article 
CAS 
PubMed 

Google Scholar 
Mitchell, K. A., Boardman, L., Clusella-Trullas, S. & Terblanche, J. S. Effects of nutrient and water restriction on thermal tolerance: A test of mechanisms and hypotheses. Comp. Biochem. Physiol. A: Mol. Integr. Physiol. 212, 15–23 (2017).Article 
CAS 

Google Scholar 
Hoffmann, A. A. & Ross, P. A. Rates and patterns of laboratory adaptation in (mostly) insects. J. Econ. Entomol. 111, 501–509 (2018).Article 
PubMed 

Google Scholar 
Popa-Báez, Á. -D. et al. Climate stress resistance in male Queensland fruit fly varies among populations of diverse geographic origins and changes during domestication. BMC Genet. 21, 1–19 (2020).Article 

Google Scholar 
Beck, H. E. et al. Present and future Köppen-Geiger climate classification maps at 1-km resolution. Sci. Data 5, 1–12 (2018).Article 

Google Scholar 
Kozak, K. H., Graham, C. H. & Wiens, J. J. Integrating GIS-based environmental data into evolutionary biology. Trends Ecol. Evol. 23, 141–148 (2008).Article 
PubMed 

Google Scholar 
Oyen, K. J. et al. Body mass and sex, not local climate, drive differences in chill coma recovery times in common garden reared bumble bees. J. Comp. Physiol. B. 191, 843–854 (2021).Article 
PubMed 

Google Scholar 
Angert, A. L., Bontrager, M. G. & Ågren, J. What do we really know about adaptation at range edges?. Annu. Rev. Ecol. Evol. Syst. 51, 341–361 (2020).Article 

Google Scholar 
Terblanche, J. S. & Hoffmann, A. A. Validating measurements of acclimation for climate change adaptation. Curr. Opin. insect sci. 41, 7–16 (2020).Article 
PubMed 

Google Scholar 
Kourti, A. Patterns of variation within and between Greek populations of Ceratitis capitata suggest extensive gene flow and latitudinal clines. J. Econ. Entomol. 97, 1186–1190 (2004).Article 
CAS 
PubMed 

Google Scholar 
Hangartner, S., Lasne, C., Sgrò, C. M., Connallon, T. & Monro, K. Genetic covariances promote climatic adaptation in Australian Drosophila. Evolution 74, 326–337 (2020).Article 
PubMed 

Google Scholar 
Bontrager, M. & Angert, A. L. Gene flow improves fitness at a range edge under climate change. Evol. Let. 3, 55–68 (2019).Article 

Google Scholar 
Liu, Q. et al. Extension of the growing season increases vegetation exposure to frost. Nat. Commun. 9, 1–8 (2018).
Google Scholar 
Schwartz, M. D., Ahas, R. & Aasa, A. Onset of spring starting earlier across the Northern Hemisphere. Glob. Change Biol. 12, 343–351 (2006).Article 

Google Scholar 
Ma, Q., Huang, J. G., Hänninen, H. & Berninger, F. Divergent trends in the risk of spring frost damage to trees in Europe with recent warming. Glob. Change Biol. 25, 351–360 (2019).Article 

Google Scholar 
Unterberger, C. et al. Spring frost risk for regional apple production under a warmer climate. PLoS ONE 13, e0200201 (2018).Article 
MathSciNet 
PubMed 
PubMed Central 

Google Scholar 
Manrakhan, A., Daneel, J.-H., Stephen, P. R. & Hattingh, V. Cold Tolerance of Immature Stages of Ceratitis capitata and Bactrocera dorsalis (Diptera: Tephritidae). J. Econ. Entomol. 115(2), 482–492 (2022).Article 
PubMed 

Google Scholar 
Papadopoulos, N. T., Carey, J. R., Katsoyannos, B. I. & Kouloussis, N. A. Overwintering of the mediterranean fruit fly (Diptera: Tephritidae) in Northern Greece. Ann. Entomol. Soc. Am. 89, 526–534 (1996).Article 

Google Scholar 
Papadopoulos, N. T., Katsoyannos, B. I. & Carey, J. R. Temporal changes in the composition of the overwintering larval population of the Mediterranean fruit fly (Diptera: Tephritidae) in Northern Greece. Ann. Entomol. Soc. Am. 91, 430–434 (1998).Article 

Google Scholar 
Katsoyannos, B. I., Kouloussis, N. A. & Carey, J. R. Seasonal and annual occurrence of Mediterranean fruit flies (Diptera: Tephritidae) on Chios Island, Greece: Differences between two neighboring citrus orchards. Ann. Entomol. Soc. Am. 91, 43–51 (1998).Article 

Google Scholar 
Mavrikakis, P. G., Economopoulos, A. P. & Carey, J. R. Continuous winter reproduction and growth of the mediterranean fruit fly (Diptera: Tephritidae) in Heraklion, crete Southern Greece. Environ. Entomol. 29, 1180–1187 (2000).Article 

Google Scholar 
Israely, N., Ziv, Y. & Oman, S. D. Spatiotemporal distribution patterns of Mediterranean fruit fly (Diptera: Tephritidae) in the central region of Israel. Ann. Entomol. Soc. Am. 98, 77–84 (2005).Article 

Google Scholar 
Bahrndorff, S., Lauritzen, J. M., Sørensen, M. H., Noer, N. K. & Kristensen, T. N. Responses of terrestrial polar arthropods to high and increasing temperatures. J. Exp. Biol. 224, jeb230797 (2021).Article 
PubMed 

Google Scholar 
Sinclair, B. J. & Roberts, S. P. Acclimation, shock and hardening in the cold. J. Therm. Biol. 30, 557–562 (2005).Article 

Google Scholar 
Bahrndorff, S., Gertsen, S., Pertoldi, C. & Kristensen, T. N. Investigating thermal acclimation effects before and after a cold shock in Drosophila melanogaster using behavioural assays. Biol. J. Lin. Soc. 117, 241–251 (2016).Article 

Google Scholar 
Sarmad, M., Ishfaq, A., Arif, H. & Zaka, S. M. Effect of short-term cold temperature stress on development, survival and reproduction of Dysdercus koenigii (Hemiptera: Pyrrhocoridae). Cryobiology 92, 47–52 (2020).Article 
PubMed 

Google Scholar 
Steyn, V. M., Mitchell, K. A., Nyamukondiwa, C. & Terblanche, J. S. Understanding costs and benefits of thermal plasticity for pest management: Insights from the integration of laboratory, semi-field and field assessments of Ceratitis capitata (Diptera: Tephritidae). Bull. Entomol. Res., 1–11 (2022).Davis, H. E., Cheslock, A. & MacMillan, H. A. Chill coma onset and recovery fail to reveal true variation in thermal performance among populations of Drosophila melanogaster. Sci. Rep. 11, 1–10 (2021).Article 

Google Scholar 
Noh, S., Everman, E. R., Berger, C. M. & Morgan, T. J. Seasonal variation in basal and plastic cold tolerance: Adaptation is influenced by both long-and short-term phenotypic plasticity. Ecol. Evol. 7, 5248–5257 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Bruins, H. J. Ancient desert agriculture in the Negev and climate-zone boundary changes during average, wet and drought years. J. Arid Environ. 86, 28–42 (2012).Article 

Google Scholar 
Hoffmann, A. A., Sørensen, J. G. & Loeschcke, V. Adaptation of Drosophila to temperature extremes: Bringing together quantitative and molecular approaches. J. Therm. Biol. 28, 175–216 (2003).Article 

Google Scholar 
Kawecki, T. J. & Ebert, D. Conceptual issues in local adaptation. Ecol. Lett. 7, 1225–1241 (2004).Article 

Google Scholar 
Nyamukondiwa, C. & Terblanche, J. S. Thermal tolerance in adult mediterranean and Natal fruit flies (Ceratitis capitata and Ceratitis rosa): Effects of age, gender and feeding status. J. Therm. Biol. 34, 406–414 (2009).Article 

Google Scholar 
Team, R. C. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (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 
Mazerolle, M. J. Model selection and multimodel inference using the AICcmodavg package (2020).Therneau, T. A Package for Survival Analysis in R. R Package Version 3.2-13.(2021. (2021).Kassambara, A., Kosinski, M., Biecek, P. & Fabian, S. Survminer: Drawing Survival Curves using’ggplot2′. R package version 0.4. 9. 2021. (2021).Lenth, R. V. Emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 1.7.2. (2022). More

Sarwar, N. et al. Phytoremediation strategies for soils contaminated with heavy metals: Modifications and future perspectives. Chemosphere 171, 710–721 (2017).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Lin, H. M. et al. Cadmium-stress mitigation through gene expression of rice and silicon addition. Plant Growth Regul.: Int. J. Nat. Synthetic Regul. 81(1), 91–101 (2017).Article 
CAS 

Google Scholar 
Pan, F. S. et al. Enhanced Cd extraction of oilseed rape (Brassica napus) by plant growth-promoting bacteria isolated from Cd hyperaccumulator Sedum alfredii Hance. Int. J. Phytorem. 19(1/6), 281–289 (2017).Article 
CAS 

Google Scholar 
Puangprasert, S. & Prueksasit, T. Health risk assessment of airborne Cd, Cu, Ni and Pb for electronic waste dismantling workers in Buriram Province, Thailand. J. Environ. Manag. 252, 109601 (2019).Article 
CAS 

Google Scholar 
Tipu, M. I. et al. Growth and physiology of maize (Zea mays L.) in a nickel-contaminated soil and phytoremediation efficiency using EDTA. J. Plant Growth Regul. 40(2), 774–786 (2021).Article 
CAS 

Google Scholar 
Chaturvedi, N., Dhal, N. K. & Patra, H. K. EDTA and citric acid-mediated phytoextraction of heavy metals from iron ore tailings using Andrographis paniculata: A comparative study. Int. J. Min. Reclam. Environ. 29(1), 33–46 (2015).Article 
CAS 

Google Scholar 
Wang, G. Y. et al. Heavy metal removal by GLDA washing: Optimization, redistribution, recycling, and changes in soil fertility. Sci. Total Environ. 569–570, 557–568 (2016).Article 
ADS 
PubMed 

Google Scholar 
Kołodyńska, D. Cu(II), Zn(II), Co(II) and Pb(II) removal in the presence of the complexing agent of a new generation. Desalination 267(2–3), 175–183 (2011).Article 

Google Scholar 
Guo, X. F. et al. Mixed chelators of EDTA, GLDA, and citric acid as washing agent effectively remove Cd, Zn, Pb, and Cu from soils. J. Soils Sediments 18(2), 835–844 (2017).
Google Scholar 
Wang, X. et al. Subcellular distribution and chemical forms of cadmiun in Bechmeria nivea L. Gaud. Environ. Exp. Bot. 62(3), 389–395 (2008).Article 
CAS 

Google Scholar 
Gallego, S. M. et al. Unravelling cadmium toxicity and tolerance in plants: Insight into regulatory mechanisms. Environ. Exp. Bot. 83, 33–46 (2012).Article 
CAS 

Google Scholar 
Clemens, S., Aarts, M. G. M., Thomine, S. & Verbruggen, N. Plant science: The key to preventing slow cadmium poisoning. Trends Plant Sci. 18(2), 92–99 (2013).Article 
CAS 
PubMed 

Google Scholar 
Zhou, J. T. et al. Integration of cadmium accumulation, subcellular distribution, and physiological responses to understand cadmium tolerance in apple rootstocks. Front. Plant Sci. 8, 966 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Yang, L. P., Zhu, J., Wang, P., Lyu, D. G. & Li, H. F. Effect of Cd on growth, physiological response, Cd subcellular distribution and chemical forms of Koelreuteria paniculata. Ecotoxicol. Environ. Saf. 160, 10–18 (2018).Article 
CAS 
PubMed 

Google Scholar 
Wang, W. J., Zhang, M. Z. & Liu, J. N. Subcellular distribution and chemical forms of Cd in Bougainvillea spectabilis Willd. as an ornamental phytostabilizer: An integrated consideration. Int. J. Phytorem. 20(11), 1087–1095 (2017).Article 

Google Scholar 
Weigel, H. J. & Jäger, H. J. Subcellular distribution and chemical form of cadmium in bean plants. Plant Physiol. 65(3), 480–482 (1980).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Khanna, K., Kohli, S. K., Ohri, P., Bhardwaj, R. & Ahmad, P. Agroecotoxicological aspect of Cd in soil–plant system: Uptake, translocation and amelioration strategies. Environ. Sci. Pollut. Res. 29, 30908–30934 (2022).Article 
CAS 

Google Scholar 
Wei, Z. B., Chen, X. H., Wu, Q. T. & Tan, M. Biodegradable chelator GLDA induced remediation of heavy metal contaminated soil in Southeast Jingtian. Environ. Sci. 36(5), 1864–1869 (2015).CAS 

Google Scholar 
Wang, K., Liu, Y. H., Song, Z. G., Wang, D. & Qiu, W. W. Chelator complexes enhanced Amaranthus hypochondriacus L. phytoremediation efficiency in Cd-contaminated soils. Chemosphere 237, 124480 (2019).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Meng, N., Wang, M., Chen, L., Zheng, H. & Chen, S. B. Remediation effects of different herbaceous plants intercropping on Cd-contaminated soil. China Environ. Sci. 38(7), 2618–2624 (2018).CAS 

Google Scholar 
Jones, D. & Willett, V. Experimental evaluation of methods to quantify dissolved organic nitrogen (don) and dissolved organic carbon (doc) in soil. Soil Biol. Biochem. 38(5), 991–999 (2006).Article 
CAS 

Google Scholar 
Su, F. L. et al. The distribution and enrichment characteristics of copper in soil and Phragmites australis of Liao River estuary wetland. Environ. Monit. Assess.: Int. J. 190(6), 1–9 (2018).Article 
CAS 

Google Scholar 
Shahid, M., Dumat, C. & Khalid, S. Reviews of Environmental Contamination and Toxicology Vol. 241, 3–137 (Springer, 2016).
Google Scholar 
Yuliya, V. et al. Comparison of soil-to-root transfer and translocation coefficients of trace elements in vines of Chardonnay and Muscat white grown in the same vineyard. Sci. Hortic. 192, 89–96 (2015).Article 

Google Scholar 
Liu, Q. Q., Chen, Y. H., Shen, Z. G. & Zheng, L. Q. Roles of cell wall in plant heavy metal tolerance. Plant Physiol. J. 50(5), 605–611 (2014).
Google Scholar 
Zhen, S. et al. Foliar application of Zn reduces Cd accumulation in grains of late rice by regulating the antioxidant system, enhancing Cd chelation onto cell wall of leaves, and inhibiting Cd translocation in rice. Sci. Total Environ. 770, 145302 (2021).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Shi, Y. X. et al. Simulation of the absorption, migration and accumulation process of heavy metal elements in soil-crop system. Environ. Sci. 37(10), 3996–4003 (2016).
Google Scholar 
Yan, X. X. et al. Effect of foliar application of different manganese fertilizers on cadmium accumulation and subcellular distribution in pak choi. J. Agro Environ. Sci. 38(8), 1872–1881 (2019).
Google Scholar 
He, S., Wu, Q. & He, Z. Effect of DA-6 and EDTA alone or in combination on uptake, subcellular distribution and chemical form of Pb in Lolium perenne. Chemosphere 93(11), 2782–2788 (2013).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Li, C. C. et al. Integration of metal chemical forms and subcellular partitioning to understand metal toxicity in two lettuce (Lactuca sativa L.) cultivars. Plant Soil 384(1/2), 201–212 (2014).Article 
CAS 

Google Scholar 
Li, D., He, T., Saleem, M. & He, G. Metalloprotein-specific or critical amino acid residues: Perspectives on plant-precise detoxification and recognition mechanisms under cadmium stress. Int. J. Mol. Sci. 23(3), 1734 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Perriguey, J., Sterckeman, T. & Morel, J. L. Effect of rhizosphere and plantrelated factors on the cadmium uptake by maize(Zea mays L.). Environ. Exp. Bot. 63(1/3), 333–341 (2008).Article 
CAS 

Google Scholar 
Dai, S. et al. Effects of biochar amendments on speciation and bioavailability of heavy metals in coal-mine-contaminated soil. Hum. Ecol. Risk Assess. Int. J. 24(7), 1887–1900 (2018).Article 
CAS 

Google Scholar 
Hou, S., Zheng, N., Tang, L., Ji, X. F. & Li, Y. Y. Effect of soil pH and organic matter content on heavy metals availability in maize (Zea mays L.) rhizospheric soil of non-ferrous metals smelting area. Environ. Monit. Assess. 191(10), 634 (2019).Article 
PubMed 

Google Scholar 
Wu, H. J. et al. Effects of Astragalus smicuson cadmium effectiveness in paddy soil and cadmium accumulation in rice plant. Chin. Agric. Sci. Bull. 33(16), 105–111 (2017).ADS 

Google Scholar 
Jin, P. K., Liu, K. J. & Wang, X. B. Conversion and utilization of slowly biodegradable organic matter. Chin. J. Environ. Eng. 10(5), 2168–2174 (2016).CAS 

Google Scholar 
Kopáček, J. et al. Factors affecting the leaching of dissolved organic carbon after tree dieback in an unmanaged European mountain forest. Environ. Sci. Technol. 52(11), 6291–6299 (2018).Article 
ADS 
PubMed 

Google Scholar 
Anwar, S. et al. Impact of chelator-induced phytoextraction of cadmium on yield and ionic uptake of maize. Int. J. Phytorem. 19(6), 505–513 (2017).Article 
CAS 

Google Scholar 
Wu, J. M., Xi, M. & Kong, F. L. Review of researches on the factors influencing the dynamics of dissolved organic carbon in soils. Geol. Rev. 59(5), 953–961 (2013).CAS 

Google Scholar 
AkzoNobel. Dissolvine GL® Technichal Brochure 1–5 (AkzoNobel Amsterdam, 2010).
Google Scholar 
Beygi, M. & Jalali, M. Assessment of trace elements (Cd, Cu, Ni, Zn) fractionation and bioavailability in vineyard soils from the Hamedan, Iran. Geoderma 337, 1009–1020 (2019).Article 
ADS 
CAS 

Google Scholar 
Gul, I. et al. Comparative effectiveness of organic and inorganic amendments on cadmium bioavailability and uptake by Pelargonium hortorum. J. Soils Sediments 19(5), 2346–2356 (2019).Article 
CAS 

Google Scholar 
Wang, H., Sun, L. N., Li, H. B. & Sun, T. Y. Effect of different chelators application on Cd accumulation in metal polluted soils by Beta vulgaris var. cicla L. Ecol. Environ. 17(6), 2249–2252 (2008).
Google Scholar 
Zhang, G. X. et al. Effects of biochars on the availability of heavy metals to ryegrass in an alkaline contaminated soil. Environ. Pollut. 218, 513–522 (2016).Article 
CAS 
PubMed 

Google Scholar 
Gu, M. H. et al. Effects of manganese application on the formation of manganese oxides and cadmium fixation in soil. Ecol. Environ. Sci. 229(2), 360–368 (2020).
Google Scholar 
Bradl, H. B. Adsorption of heavy metal ions on soils and soils constituents. J. Colloid Interface Sci. 277(1), 1–18 (2004).Article 
ADS 
CAS 
PubMed 

Google Scholar  More

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.This is a summary of: Marin, F. R. et al. Protecting the Amazon forest and reducing global warming via agricultural intensification. Nat. Sustain. https://doi.org/10.1038/s41893-022-00968-8 (2022). More

Longline fishing boats such as these at Honolulu’s harbour in Hawaii must respect a large no-fishing zone off the western side of the archipelago.Credit: Sarah Medoff

Large no-fishing areas can drive the recovery of commercially valuable fish species, a study suggests. Ten years’ worth of fisheries data have shown that catch rates of two important types of tuna increased drastically in the vicinity of a marine protected area surrounding the northwestern Hawaiian islands.“It’s a win–win for fish and fishermen,” says Jennifer Raynor, an economist at the University of Wisconsin–Madison and a co-author of the study, which was published on 20 October in Science1.The results highlight the value of large-scale marine protected areas — a type of environmental management that has emerged in the past two decades, mostly in the Pacific Ocean, says Kekuewa Kikiloi, who studies Hawaiian culture at the University of Hawaii at Mānoa. Countries around the world have committed to protecting 30% of their land and oceans by 2030.Previous research showed that marine protected areas can help to restore populations of creatures that don’t move around much or at all, such as corals2 and lobsters3. Raynor and her colleagues wanted to test whether the areas could also drive the recovery of migratory species and provide spillover benefits for fisheries. The researchers looked at one of the largest such areas in the world, the 1.5-million-square-kilometre Papahānaumokuākea Marine National Monument, which was created in 2006 and expanded in 2016 to protect biological and cultural resources.The team focused on the Hawaiian ‘deep-set’ longline fishery, which mainly targets yellowfin tuna (Thunnus albacares) and bigeye tuna (Thunnus obesus).The researchers analysed catch data collected on fishing vessels between 2010 and late 2019. Then, they compared catch rates at various distances up to 600 nautical miles (1,111 kilometres) from the protected area, before and after its expansion in 2016. (The protected area itself currently extends for 200 nautical miles from the northwestern part of the Hawaiian archipelago.) They found that after the expansion, catch rates — defined as the number of fish caught for every 1,000 hooks deployed — went up, and that the increases were greater the closer the boats were to the no-fishing zone. At distances of up to 100 nautical miles, the catch rate for yellowfin tuna increased by 54%, and that for bigeye tuna by 12%. Some other types of catch rate also increased, but not by equally significant margins.The size of the Papahānaumokuākea Marine National Monument — more than three times the surface area of California — probably played a part in the positive effects, as did its shape. It spans about 2,000 kilometres from west to east, protecting large swathes of ocean waters at tropical latitudes. This means that tropical fish such as yellowfin and bigeye tuna — which tend to move along an east–west axis to stay in their preferred temperature range — can travel a long way and still stay in the no-fishing zone.What’s more, says Raynor, Papahānaumokuākea is a spawning ground for yellowfin tuna. Because the animals don’t travel far from their birthplace, the no-take zone provides refuge from fishing, helping tuna to aggregate and reproduce.“It is exciting to see that there are benefits to the fishing industry from this marine protected area,” says David Kroodsma, director of research and innovation at Global Fishing Watch in Oakland, California, a US non-governmental organization that monitors fishing activity worldwide. However, he adds, it’s unclear whether the results can be generalized to other areas of the world.Regardless, the findings could help others to design marine protected areas so that benefits trickle down to fisheries, says Steve Gaines, a marine ecologist at the University of California, Santa Barbara. The study, he says, “provides a platform to definitively evaluate what is working and what isn’t”.Co-managed by Indigenous populations, the state of Hawaii and the US government, Papahānaumokuākea is an example of a collaborative management strategy that bridges Indigenous knowledge and modern science, Kikiloi says. The approach, he adds, “can work successfully in other places too, if given a chance”. More

Field samplingLake Masoko fish were chased into fixed gill nets and SCUBA by a team of professional divers at different target depths determined by diver depth gauge (12× male benthic, 12× male littoral). Riverine fish (11× Mbaka River and 1× Itupi river) were collected by local fishermen. On collection, all fish were euthanized using clove oil. Collection of wild fish was done in accordance with local regulations and permits in 2015, 2016, 2018 and 2019. On collection, fish were immediately photographed with color and metric scales, and tissues were dissected and stored in RNAlater (Sigma-Aldrich); some samples were first stored in ethanol. Only male specimens (showing bright nuptial coloration) were used in this study for the practical reason of avoiding any misassignment of individuals to the wrong population (only male individuals show clear differences in phenotypes and could therefore be reliably assigned to a population). Furthermore, we assumed that any epigenetic divergence relevant to speciation should be contributing to between-population differences in traits possessed by both sexes (habitat occupancy, diet). To investigate the role of epigenetics in phenotypic diversification and adaptation to different diets, homogenized liver tissue – a largely homogenous and key organ involved in dietary metabolism, hormone production and hematopoiesis – was used for all RNA-seq and WGBS experiments.Common-garden experimentCommon-garden fish were bred from wild-caught fish specimens, collected and imported at the same time by a team of professional aquarium fish collectors according to approved veterinary regulations of the University of Bangor, UK. Wild-caught fish were acclimatized to laboratory tanks and reared to produce first-generation (G1) common-garden fish, which were reared under the same controlled laboratory conditions in separate tanks (light–dark cycles, diet: algae flakes daily, 2–3 times weekly frozen diet) for approximately 6 months (post hatching). G1 adult males showing bright nuptial colors were culled at the same biological stages (6 months post hatching) using MS222 in accordance with the veterinary regulations of the University of Bangor, UK. Immediately on culling, fish were photographed and tissues collected and snap-frozen in tubes.Stable isotopesTo assess dietary/nutritional profiles in the three ecomorph populations, carbon (δ13C) and nitrogen (δ15N) isotope analysis of muscle samples (for the same individuals as RRBS; 12, 12 and 9 samples for benthic, littoral and riverine populations, respectively) was undertaken by elemental analyzer isotope ratio mass spectrometry by Iso-Analytical Limited. It is important to note that stable isotope analysis does not depend on the use of the same tissue as the ones used for the RRBS/WGBS samples45. Normality tests (Shapiro–Wilk, using the R package rstatix v.0.7.0), robust for small sample sizes, were performed to assess sample deviation from a Gaussian distribution. Levene’s test for homogeneity of variance was then performed (R package carData v.3.0-5) to test for homogeneity of variance across groups. Finally, Welch’s ANOVA was performed followed by Games–Howell all-pairs comparison tests with adjusted P value using Tukey’s method (rstatix v.0.7.0). Mean differences in isotope measurements and 95% CI mean differences were calculated using Dabestr v.0.3.0 with 5,000 bootstrapped resampling.Throughout this manuscript, all box plots are defined as follows: centre line, median; box limits, upper and lower quartiles; whiskers, 1.5× interquartile range; points, outliers.RNA-seqNext-generation sequencing library preparationTotal RNA from liver tissues stored in RNAlater was extracted using a phenol/chloroform approach (TRIzol reagent; Sigma-Aldrich). Of note, when tissues for bisulphite sequencing samples were not available, additional wild-caught samples were used (Supplementary Table 3). The quality and quantity of RNA extraction were assessed using TapeStation (Agilent Technologies), Qubit and NanoDrop (Thermo Fisher Scientific). Next-generation sequencing (NGS) libraries were prepared using poly(A) tail-isolated RNA fraction and sequenced on a NovaSeq system (S4; paired-end 100/150 bp; Supplementary Table 3), yielding on average 32.9 ± 3.9 Mio reads.Read alignment and differential gene expression analysisAdaptor sequence in reads, low-quality bases (Phred score  More