Ecology

Trenberth, K. E. & Jones, P. D. in Climate Change 2007: The Physical Science Basis (eds Solomon, S. et al.) 235–335 (Cambridge Univ. Press, 2007).Linderholm, H. W. Growing season changes in the last century. Agr. For. Meteorol. 137, 1–14 (2006).
Google Scholar 
Yang, B. et al. New perspective on spring vegetation phenology and global climate change based on Tibetan Plateau tree-ring data. Proc. Natl Acad. Sci. USA 114, 6966–6971 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Shen, M., Tang, Y., Chen, J. & Yang, W. Specification of thermal growing season in temperate China from 1960 to 2009. Clim. Change 114, 783–798 (2012).
Google Scholar 
Zhou, B., Zhai, P., Chen, Y. & Yu, R. Projected changes of thermal growing season over Northern Eurasia in a 1.5 °C and 2 °C warming world. Environ. Res. Lett. 13, 35004 (2018).
Google Scholar 
Barichivich, J., Briffa, K. R., Osborn, T. J., Melvin, T. M. & Caesar, J. Thermal growing season and timing of biospheric carbon uptake across the Northern Hemisphere. Glob. Biogeochem. Cycles 26, B4015 (2012).
Google Scholar 
Buitenwerf, R., Rose, L. & Higgins, S. I. Three decades of multi-dimensional change in global leaf phenology. Nat. Clim. Change 5, 364–368 (2015).
Google Scholar 
Gonsamo, A., Chen, J. M. & Ooi, Y. W. Peak season plant activity shift towards spring is reflected by increasing carbon uptake by extratropical ecosystems. Glob. Change Biol. 24, 2117–2128 (2018).
Google Scholar 
Menzel, A. et al. European phenological response to climate change matches the warming pattern. Glob. Change Biol. 12, 1969–1976 (2006).
Google Scholar 
Montgomery, R. A., Rice, K. E., Stefanski, A., Rich, R. L. & Reich, P. B. Phenological responses of temperate and boreal trees to warming depend on ambient spring temperatures, leaf habit, and geographic range. Proc. Natl Acad. Sci. USA 117, 10397–10405 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Piao, S. et al. Leaf onset in the northern hemisphere triggered by daytime temperature. Nat. Commun. 6, 6911 (2015).CAS 
PubMed 

Google Scholar 
Barichivich, J. et al. Large-scale variations in the vegetation growing season and annual cycle of atmospheric CO2 at high northern latitudes from 1950 to 2011. Glob. Change Biol. 19, 3167–3183 (2013).
Google Scholar 
Peñuelas, J., Rutishauser, T. & Filella, I. Phenology feedbacks on climate change. Science 324, 887–888 (2009).PubMed 

Google Scholar 
Piao, S. et al. Weakening temperature control on the interannual variations of spring carbon uptake across northern lands. Nat. Clim. Change 7, 359–363 (2017).CAS 

Google Scholar 
Richardson, A. D. et al. Influence of spring and autumn phenological transitions on forest ecosystem productivity. Philos. Trans. R. Soc. B 365, 3227–3246 (2010).
Google Scholar 
Bonan, G. B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449 (2008).CAS 
PubMed 

Google Scholar 
Piao, S. et al. Plant phenology and global climate change: current progresses and challenges. Glob. Change Biol. 25, 1922–1940 (2019).
Google Scholar 
Fu, Y. H. et al. Declining global warming effects on the phenology of spring leaf unfolding. Nature 526, 104–107 (2015).CAS 
PubMed 

Google Scholar 
Park, T. et al. Changes in timing of seasonal peak photosynthetic activity in northern ecosystems. Glob. Change Biol. 25, 2382–2395 (2019).
Google Scholar 
Xu, C., Liu, H., Williams, A. P., Yin, Y. & Wu, X. Trends toward an earlier peak of the growing season in Northern Hemisphere mid-latitudes. Glob. Change Biol. 22, 2852–2860 (2016).
Google Scholar 
Wang, X. et al. No trends in spring and autumn phenology during the global warming hiatus. Nat. Commun. 10, 2389 (2019).PubMed 
PubMed Central 

Google Scholar 
Piao, S., Friedlingstein, P., Ciais, P., Viovy, N. & Demarty, J. Growing season extension and its impact on terrestrial carbon cycle in the Northern Hemisphere over the past 2 decades. Glob. Biogeochem. Cycles 21, B3018 (2007).
Google Scholar 
Buermann, W., Bikash, P. R., Jung, M., Burn, D. H. & Reichstein, M. Earlier springs decrease peak summer productivity in North American boreal forests. Environ. Res. Lett. 8, 24027 (2013).
Google Scholar 
Buermann, W. et al. Widespread seasonal compensation effects of spring warming on northern plant productivity. Nature 562, 110–114 (2018).CAS 
PubMed 

Google Scholar 
Lian, X. et al. Summer soil drying exacerbated by earlier spring greening of northern vegetation. Sci. Adv. 6, eaax0255 (2020).PubMed 
PubMed Central 

Google Scholar 
Piao, S. et al. Net carbon dioxide losses of northern ecosystems in response to autumn warming. Nature 451, 49–52 (2008).CAS 
PubMed 

Google Scholar 
Wang, H. et al. Alpine grassland plants grow earlier and faster but biomass remains unchanged over 35 years of climate change. Ecol. Lett. 23, 701–710 (2020).PubMed 
PubMed Central 

Google Scholar 
Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).CAS 
PubMed 

Google Scholar 
Delpierre, N. et al. Temperate and boreal forest tree phenology: from organ-scale processes to terrestrial ecosystem models. Ann. For. Sci. 73, 5–25 (2016).
Google Scholar 
Huang, J. et al. Photoperiod and temperature as dominant environmental drivers triggering secondary growth resumption in Northern Hemisphere conifers. Proc. Natl Acad. Sci. USA 117, 20645–20652 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Li, X. et al. Critical minimum temperature limits xylogenesis and maintains treelines on the southeastern Tibetan Plateau. Sci. Bull. 62, 804–812 (2017).
Google Scholar 
Rossi, S. et al. Critical temperatures for xylogenesis in conifers of cold climates. Glob. Ecol. Biogeogr. 17, 696–707 (2008).
Google Scholar 
Lenz, A., Vitasse, Y., Hoch, G. & Körner, C. Growth and carbon relations of temperate deciduous tree species at their upper elevation range limit. J. Ecol. 102, 1537–1548 (2014).
Google Scholar 
Zeng, Q., Rossi, S., Yang, B., Qin, C. & Li, G. Environmental drivers for cambial reactivation of Qilian junipers (Juniperus przewalskii) in a semi-arid region of northwestern China. Atmosphere 11, 232 (2020).
Google Scholar 
Ren, P. et al. Growth rate rather than growing season length determines wood biomass in dry environments. Agr. For. Meteorol. 271, 46–53 (2019).
Google Scholar 
Sanginés De Cárcer, P. et al. Vapor-pressure deficit and extreme climatic variables limit tree growth. Glob. Change Biol. 24, 1108–1122 (2017).
Google Scholar 
Zhang, J. et al. Drought limits wood production of Juniperus przewalskii even as growing seasons lengthens in a cold and arid environment. Catena 196, 104936 (2021).
Google Scholar 
Huang, J., Deslauriers, A. & Rossi, S. Xylem formation can be modeled statistically as a function of primary growth and cambium activity. New Phytol. 203, 831–841 (2014).CAS 
PubMed 

Google Scholar 
Rossi, S., Morin, H. & Deslauriers, A. Causes and correlations in cambium phenology: towards an integrated framework of xylogenesis. J. Exp. Bot. 63, 2117–2126 (2012).CAS 
PubMed 

Google Scholar 
Rossi, S., Girard, M. J. & Morin, H. Lengthening of the duration of xylogenesis engenders disproportionate increases in xylem production. Glob. Change Biol. 20, 2261–2271 (2014).
Google Scholar 
Cuny, H. E. et al. Woody biomass production lags stem-girth increase by over one month in coniferous forests. Nat. Plants 1, 15160 (2015).CAS 
PubMed 

Google Scholar 
Pasho, E., Camarero, J. J. & Vicente-Serrano, S. M. Climatic impacts and drought control of radial growth and seasonal wood formation in Pinus halepensis. Trees 26, 1875–1886 (2012).
Google Scholar 
Keenan, T. F. et al. Net carbon uptake has increased through warming-induced changes in temperate forest phenology. Nat. Clim. Change 4, 598–604 (2014).CAS 

Google Scholar 
Chen, L. et al. Leaf senescence exhibits stronger climatic responses during warm than during cold autumns. Nat. Clim. Change 10, 777–780 (2020).CAS 

Google Scholar 
Körner, C. Paradigm shift in plant growth control. Curr. Opin. Plant Biol. 25, 107–114 (2015).PubMed 

Google Scholar 
Muller, B. et al. Water deficits uncouple growth from photosynthesis, increase C content, and modify the relationships between C and growth in sink organs. J. Exp. Bot. 62, 1715–1729 (2011).CAS 
PubMed 

Google Scholar 
Charney, N. D. et al. Observed forest sensitivity to climate implies large changes in 21st century North American forest growth. Ecol. Lett. 19, 1119–1128 (2016).PubMed 

Google Scholar 
Liu, Q. et al. Extension of the growing season increases vegetation exposure to frost. Nat. Commun. 9, 426 (2018).PubMed 
PubMed Central 

Google Scholar 
Deslauriers, A. & Morin, H. Intra-annual tracheid production in balsam fir stems and the effect of meteorological variables. Trees 19, 402–408 (2005).
Google Scholar 
Piao, S. et al. Characteristics, drivers and feedbacks of global greening. Nat. Rev. Earth Environ. 1, 14–27 (2020).
Google Scholar 
Huang, M. et al. Air temperature optima of vegetation productivity across global biomes. Nat. Ecol. Evol. 3, 772–779 (2019).PubMed 
PubMed Central 

Google Scholar 
Keenan, T. F. & Riley, W. J. Greening of the land surface in the world’s cold regions consistent with recent warming. Nat. Clim. Change 8, 825–828 (2018).CAS 

Google Scholar 
Camarero, J. J., Olano, J. M. & Parras, A. Plastic bimodal xylogenesis in conifers from continental Mediterranean climates. New Phytol. 185, 471–480 (2010).PubMed 

Google Scholar 
Fu, Y. H. et al. Unexpected role of winter precipitation in determining heat requirement for spring vegetation green-up at northern middle and high latitudes. Glob. Change Biol. 20, 3743–3755 (2014).
Google Scholar 
Wu, X. et al. Uneven winter snow influence on tree growth across temperate China. Glob. Change Biol. 25, 144–154 (2018).
Google Scholar 
Wang, X. et al. Disentangling the mechanisms behind winter snow impact on vegetation activity in northern ecosystems. Glob. Change Biol. 24, 1651–1662 (2018).
Google Scholar 
Adams, H. D. et al. Experimental drought and heat can delay phenological development and reduce foliar and shoot growth in semiarid trees. Glob. Change Biol. 21, 4210–4220 (2015).
Google Scholar 
He, W., Liu, H., Qi, Y., Liu, F. & Zhu, X. Patterns in nonstructural carbohydrate contents at the tree organ level in response to drought duration. Glob. Change Biol. 26, 3627–3638 (2020).
Google Scholar 
Williams, A. P. et al. Temperature as a potent driver of regional forest drought stress and tree mortality. Nat. Clim. Change 3, 292–297 (2012).
Google Scholar 
Vitasse, Y. et al. Contrasting resistance and resilience to extreme drought and late spring frost in five major European tree species. Glob. Change Biol. 25, 3781–3792 (2019).
Google Scholar 
Zhao, S. et al. The International Tree-Ring Data Bank (ITRDB) revisited: data availability and global ecological representativity. J. Biogeogr. 46, 355–368 (2019).
Google Scholar 
Babst, F., Poulter, B., Bodesheim, P., Mahecha, M. D. & Frank, D. C. Improved tree-ring archives will support earth-system science. Nat. Ecol. Evol. 1, 8 (2017).PubMed 

Google Scholar 
Elmore, A. J., Guinn, S. M., Minsley, B. J. & Richardson, A. D. Landscape controls on the timing of spring, autumn, and growing season length in mid-Atlantic forests. Glob. Change Biol. 18, 656–674 (2012).
Google Scholar 
Kannenberg, S. A. et al. Drought legacies are dependent on water table depth, wood anatomy and drought timing across the eastern US. Ecol. Lett. 22, 119–127 (2018).PubMed 

Google Scholar 
Rossi, S., Deslauriers, A., Anfodillo, T. & Carraro, V. Evidence of threshold temperatures for xylogenesis in conifers at high altitudes. Oecologia 152, 1–12 (2007).PubMed 

Google Scholar 
Gao, S. et al. Dynamic responses of tree-ring growth to multiple dimensions of drought. Glob. Change Biol. 24, 5380–5390 (2018).
Google Scholar 
Peltier, D. M. P. & Ogle, K. Tree growth sensitivity to climate is temporally variable. Ecol. Lett. 23, 1561–1572 (2020).PubMed 

Google Scholar 
Wilmking, M. et al. Global assessment of relationships between climate and tree growth. Glob. Change Biol. 26, 3212–3220 (2020).
Google Scholar 
Seftigen, K., Frank, D. C., Björklund, J., Babst, F. & Poulter, B. The climatic drivers of normalized difference vegetation index and tree-ring-based estimates of forest productivity are spatially coherent but temporally decoupled in Northern Hemispheric forests. Glob. Ecol. Biogeogr. 27, 1352–1365 (2018).
Google Scholar 
Bunn, A. G. A dendrochronology program library in R (dplR). Dendrochronologia 26, 115–124 (2008).
Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019); https://www.R-project.org/Sheffield, J., Goteti, G. & Wood, E. F. Development of a 50-year high-resolution global dataset of meteorological forcings for land surface modeling. J. Clim. 19, 3088–3111 (2006).
Google Scholar 
Frich, P. L. et al. Observed coherent changes in climatic extremes during the second half of the twentieth century. Clim. Res. 19, 193–212 (2002).
Google Scholar 
Selyaninov, G. T. About climate agricultural estimation (in Russian). Proc. Agric. Meteorol. 20, 165–177 (1928).
Google Scholar 
Streiner, D. L. Finding our way: an introduction to path analysis. Can. J. Psychiatry 50, 115–122 (2005).PubMed 

Google Scholar 
Fox, J., Nie, Z. & Byrnes, J. sem: Structural equation models. R package version 3.1-9 https://CRAN.R-project.org/package=sem (2017).Iturbide, M. et al. An update of IPCC climate reference regions for subcontinental analysis of climate model data: definition and aggregated datasets. Earth Syst. Sci. Data 12, 2959–2970 (2020).
Google Scholar 
Bagozzi, R. P. & Yi, Y. Specification, evaluation, and interpretation of structural equation models. J. Acad. Mark. Sci. 40, 8–34 (2012).
Google Scholar  More

Global map of forest cover change and its validationWe use a high-resolution map of global forest cover change15 (annual intervals at 30 m spatial resolution; version 1.7) to quantify forest loss across the tropics. The GFC dataset maps where and when forests were converted (naturally and anthropogenically) from 2001 to 2019. Trees are defined as all vegetation taller than 5 m in height, and forests are defined with a tree canopy threshold of at least 30%. The definition of forests includes plantations and tree crops such as oil palm. Forest loss is the mortality or removal of all tree cover within a pixel.Our study uses the v.1.7 product that spans the period 2001–2019 for the analysis. Different methods are used for detecting forest cover loss in two periods (2001–2010 and 2011–2019). This change in detection method as well as in satellite data (Landsat 7 and Landsat 8) might result in inconsistencies of data during the two periods. Therefore, we perform an independent assessment of the v.1.7 product throughout the study period (2001–2019) using stratified random-sample reference data. We randomly sample 18,000 pixels, a much larger sample population than the assessment of the original product (v.1.0, 628 pixels across the tropics; ref. 15), and visually interpret forest loss using Landsat imagery. Specifically, we randomly select 50 path/row locations (World Reference System II) of Landsat imagery in each tropical continent (150 path/row locations in total in the three tropical continents; Supplementary Fig. 6). For each path/row location, we randomly select 20 loss pixels and 10 non-loss pixels in each period of 2001–2005, 2006–2010, 2011–2014 and 2015–2019, with total sampling pixels of ~18,000 ((20 loss + 10 non-loss) × 50 path/row locations × 4 periods × 3 continents). Our study compares the increase in forest carbon loss from the start (~2001) to the end (~2019) of the study period. Thus, we divided the whole 19 yr study period into four subperiods (2001–2005, 2006–2010, 2011–2014 and 2015–2019), with the first five years considered as the start period and the last five years considered as the end period for the comparison. Some path/row locations do not have 20 loss pixels in a specific period; for example, there is no loss detected in some locations of the Sahara. Therefore, we sample 11,198 loss pixels and 6,000 non-loss pixels (Supplementary Data). Finally, we download time-series Landsat imagery covering 1999–2020 to visually interpret these pixels as reference data.Following the suggestion of Global Forest Watch19 and per best practice guidance of ref. 18, we use a stratified random-sample approach for area estimation, which is independent of the method and satellite changes in the GFC data. The sampling reference data (Supplementary Data) are used to estimate loss area:$${it{p}}_{hi} = w_hfrac{{mathop {sum}limits_{j = 1}^{150} {n_{hij} times A_{hi}} }}{{mathop {sum}limits_{j = 1}^{150} {n_{hj} times A_i} }}$$
(1)
where phi is the stratified random-sample estimated area for GFC map class h that is classified as reference class i; wh is the proportion of the total area GFC map class h; nhij is the number of pixels in GFC map class h that is classified as reference class i in jth Landsat path/row location; nhj is the total number of pixels in GFC map class h in jth Landsat path/row location; Ahj is the total area in GFC map class h in jth Landsat path/row location; and Aj is the total land area of jth Landsat path/row location.We then calculate the error matrix, which includes overall accuracy (OA), user’s accuracy (UA) and producer’s accuracy (PA), as follows:$${mathrm{OA}} = mathop {sum}limits_{h = 1}^H {p_{hh}}$$
(2)
$${mathrm{UA}}_h = frac{{p_{hh}}}{{p_h}}$$
(3)
$${mathrm{PA}}_j = frac{{p_{jj}}}{{p_j}}$$
(4)
where UAh is the UA for stratum h; ph and pj are the total area in stratum h and j, respectively; and PAj is the PA in stratum j.Using the preceding equations, we estimate OA, UA and PA in the four periods (Supplementary Table 2). In general, OAs are >99%, UAs are >88% and PAs are >72% in each period. The stratified random-sample approach for area estimation is considered the most robust method to investigate loss trends in GFC product and can avoid inconsistencies of the dataset due to changes in detection model and satellite sensors19. Our results show that the forest loss from the stratified random-sample approach is similar to GFC mapped loss, both of which show a consistent increase during the four periods of 2001–2019 (Fig. 1a), confirming the increasing forest carbon loss across the tropics during the early twenty-first century.Forest carbon stocksWe estimate forest (aboveground and belowground) biomass carbon losses by co-locating GFC loss data with corresponding biomass data. Forest biomass maps are not universally reliable, owing to uncertainties and some degree of bias. We use four biomass maps to quantify forest carbon stocks, which helps reduce the uncertainties and bias44. The four maps were developed by refs. 9,48,49,20 and are hereafter referred to as ‘Baccini’, ‘Saatchi’, ‘Avitabile’ and ‘Zarin’ maps, respectively. The Baccini map, derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data, presents aboveground live woody biomass (AGB) across the tropics at 500 m spatial resolution. The Saatchi map, also derived from MODIS data, presents total forest carbon stocks at 1 km spatial resolution across the tropics. The Avitabile map, integrated from the Baccini and Saatchi maps, shows AGB at 1 km resolution across the tropics. The Zarin map, derived from Landsat data, presents AGB across the globe at 30 m resolution.Belowground root biomass (BGB) data are sparse because measurements of BGB are time consuming, laborious and technically challenging50. Thus, we calculate BGB (in Mg ha−1 biomass) from AGB maps (Baccini, Avitabile and Zarin) using an empirical model at the pixel level51:$${mathrm{BGB}} = 0.489 times {mathrm{AGB}}^{0.89}$$
(5)
Total forest biomass is calculated as the sum of AGB and BGB. Finally, total forest carbon stocks (MgC ha−1) in live woody forest are estimated as 50% of total biomass20,50. The Saatchi map provides total forest carbon stocks rather than AGB. The total forest carbon stocks are calculated from AGB using the same method mentioned in the preceding50. Thus, we estimate AGB and BGB from total forest carbon stocks in the Saatchi map using the preceding method to separate aboveground and belowground parts of forest carbon stocks.There are inconsistencies in the MODIS-derived biomass maps (Baccini, Saatchi and Avitabile) and Landsat-derived GFC data41, which may underestimate forest carbon loss by the three coarse forest biomass maps (Supplementary Fig. 7). The Zarin map is derived from Landsat data and considers tree cover using GFC data, and tree loss can be co-located with the corresponding biomass20, indicating the consistencies of the Zarin map and GFC. Therefore, to correct the three biomass maps with coarse resolution and reduce the inconsistencies, we resample the Zarin map from 30 m to 500 m (the resolution of the Baccini map) and 1 km (the resolutions of the Avitabile and Baccini maps) and calculate forest carbon loss using the two resampled biomass maps. We then estimate the ratios of forest carbon loss derived from the 30 m biomass map to the forest carbon loss derived from resampled biomass maps in each GFC tile (10° × 10°). The ratios are then used as a scale factor to correct the three biomass maps (Baccini, Saatchi and Avitabile). For the resampling, forest carbon density at 500 m or 1 km is averaged from all 30 m pixels in the corresponding locations.Soil organic carbon stocksDeforestation not only causes forest biomass carbon loss, but also results in loss of SOC10. We calculate SOC loss at 0–30 cm soil depth as:$${mathrm{SOC}}_{{{{mathrm{loss}}}}} = {mathrm{OCS}} times theta$$
(6)
where SOCloss is SOC loss resulting from forest loss measured in MgC ha−1, OCS is SOC stocks at 0–30 cm depth measured in MgC ha−1 and θ is the SOC loss rate.We obtain SOC stocks at 0–30 cm depth from SoilGrids (version 2.0), created by the International Soil Reference and Information Centre52. SOC stocks are calculated using a calibrated quantile random forest model at a spatial resolution of 250 m. We further resample the data from 250 m to 30 m using the nearest-neighbour method to match the scale of forest cover loss data.SOC loss rate is affected predominantly by land-use types following forest loss and tree species53. The loss rate data are compiled from a previous meta-analysis, which summarizes the rate of SOC loss resulting from forest loss over the tropics50. SOC losses resulting from primary and secondary forest loss differ (Supplementary Table 3). We use a map of primary humid tropical forests in 2001 developed by ref. 54 to classify primary and secondary forests. The land covers following forest loss are determined according to the driver of forest loss (see Drivers of forest carbon loss). The spatial resolution of the SOC data is coarse compared with GFC data, which may result in inconsistencies in the calculation. However, as SOC loss resulting from forest loss accounts for a small proportion of total forest carbon loss (8%), we ignore the potential inconsistencies.Drivers of forest carbon lossWe determine drivers of tree-cover loss using the dataset generated by ref. 27. This dataset shows the dominant driver of tree-cover loss at each 10 km grid cell for 2001–2019. There are five categories of drivers of tree-cover loss: commodity-driven deforestation, defined as permanent and/or long-term clearing of trees to other land uses (for example, commodity croplands), shifting agriculture, forestry, wildfire and urbanization. Commodity-driven deforestation, shifting agriculture and forestry dominate tropical forest loss27. Thus, wildfire and urbanization are combined and categorized as ‘others’. In tropical Africa, spatial patterns of commodity-driven deforestation are almost similar to that of shifting agriculture, as pointed out by ref. 27, resulting in large uncertainties in separating commodity agriculture from shifting agriculture. Since commodity-driven deforestation is usually for large-scale agricultural plantations, we treat commodity-driven deforestation as loss for large-scale agriculture. Because shifting agriculture is usually smallholder and/or patchy farming systems, we term shifting agriculture as small-scale agriculture, which may include commodity agriculture with similar spatial patterns to shifting agriculture in some regions such as tropical Africa. The driver data are created using decision-tree models trained by ~5,000 high-resolution Google Earth imagery cells, showing overall accuracy of 89 ± 3% from a separate validation of more than 1,500 randomly selected cells. We resample the data from 10 km resolution to 30 m using the nearest-neighbour method to match the scale of forest cover loss data.Interpretation of post-forest-loss land covers in 2020We collect cloudless and very-high-resolution satellite imagery in 2020 from Planet to determine the fate of the agriculture-driven forest loss during 2001–2019. Planet provides two products, RapidEye (at a spatial resolution of 5 m) and Doves (at a spatial resolution of 3 m; 4-band PlanetScope Scene). We randomly sample 500 pixels that show forest loss owing to agriculture expansion during 2001–2005 and 500 pixels that show forest loss owing to agriculture expansion during 2015–2019, then check cloudless satellite imagery in 2020 to visually interpret the land cover of each sampled pixel in 2020. We classify three types of land cover: agricultural land, forest/shrubland and others (Supplementary Fig. 4). Rubber and oil-palm plantations are classified as agricultural lands.Uncertainty and methods for analysisWe use committed emissions of forest carbon, even though some of this carbon will be lost only in later years or transited to other carbon pools or stored as wood products12. Forest carbon loss is defined as gross carbon loss due to forest removal (as indicated by GFC product), including (aboveground and belowground) forest biomass carbon and SOC losses. We calculate only the gross loss of forest carbon stocks while gain of carbon via reforestation and afforestation is not considered.We first estimate reference sample-based forest loss area using sample data:$${mathrm{AS}}_j = {mathrm{AM}}_jmathop {sum}limits_{h = 1}^H {p_{hj}}$$
(7)
where ASj and AMj are reference sample-based forest loss areas and mapped forest loss areas in stratum j, respectively.To estimate forest carbon loss (aboveground, belowground and soil carbon) using sample-based forest loss area, we apply a ‘stratify and multiply’ approach21,22 by assigning mean forest carbon density for each stratum. Aboveground and belowground forest carbon losses are estimated using four biomass maps (Baccini, Saatchi, Avitabile and Zarin), and we report ensemble mean ± s.d. from the four maps as our best estimate.Mountain forest carbon loss at different elevations is calculated by overlaying mountain polygon from the Global Mountain Biodiversity Assessment inventory55 (version 1.2) and the 30 m ASTER Global Digital Elevation Model56 (version 3).Although our validation shows that the GFC (v.1.7) product could accurately map forest loss, we cannot reduce the omission and commission errors. In addition, the accuracy of the disturbance year is 75.2%, with 96.7% of the disturbance occurring within one year before or after the estimated disturbance year in GFC product15. Therefore, we calculate 3 yr moving averages of annual forest and related carbon losses for time-series analysis, following the suggestion of the Global Forest Watch19 and refs. 28,38. We use a non-parametric Theil–Sen estimator regression method53 to detect trends in time-series results and test the significance of the trend by Mann–Kendall test57.Reporting SummaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

Kim, I. R. et al. Genetic diversity and population structure of nutria (Myocastor coypus) in South Korea. Animals 9, 1164. https://doi.org/10.3390/ani9121164 (2019).Article 
PubMed Central 

Google Scholar 
GISD. Of the World’s Worst Invasive Alien Species. Global Invasive Species Database. http://www.iucngisd.org/gisd/100_worst.php. 100, (2021).Hong, S., Do, Y., Kim, J. Y., Kim, D. & Joo, G. Distribution, spread and habitat preferences of nutria (Myocastor coypus) invading the lower Nakdong River, South Korea. Biol. Invas. 17, 1485–1496. https://doi.org/10.1007/s10530-014-0809-8 (2015).Article 

Google Scholar 
Ojeda, R., Bidau, C. & Emmons, L. Myocastor coypus (errata version published in 2017). The IUCN Red List Threat. Species (2016): e.T14085A121734257.Tsiamis, K. et al. Baseline Distribution of Invasive Alien Species of Union Concern (Publications Office of the European Union, 2017).
Google Scholar 
Carter, J. & Leonard, B. P. A review of the literature on the worldwide distribution, spread of, and efforts to eradicate the coypu (Myocastor coypus). Wildl. Soc. Bull. 30, 162–175 (2002).
Google Scholar 
Kim, Y. C. et al. Distribution and management of nutria (Myocastor coypus) populations in South Korea. Sustainability 11, 4169. https://doi.org/10.3390/su11154169 (2019).Article 

Google Scholar 
Park, J. H. et al. The first case of Capillaria hepatica infection in a nutria (Myocastor coypus) in Korea. Korean J. Parasitol. 52, 527–529. https://doi.org/10.3347/kjp.2014.52.5.527 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Fratini, F., Turchi, B. E., Ebani, V. V. & Bertelloni, F. The presence of Leptospira in coypus (Myocastor coypus) and rats (Rattus norvegicus) living in a protected wetland in Tuscany (Italy). Vet. Arh. 85, 407–414 (2015).
Google Scholar 
Lee, D. H., Kil, J. H. & Kim, D. E. The study on the distribution and inhabiting status of nutria (Myocastor coypus) in Korea. Korean J. Environ. Ecol. 27, 316–326 (2013).CAS 

Google Scholar 
Guichón, M. L., Doncaster, C. P. & Cassini, M. H. Population structure of coypus (Myocastor coypus) in their region of origin and comparison with introduced populations. J. Zool. 261, 265–272. https://doi.org/10.1017/S0952836903004187 (2003).Article 

Google Scholar 
Bertolino, S., Perrone, A. & Gola, L. Effectiveness of coypu control in small Italian Wetland areas. Wildl. Soc. Bull. 33, 714–720. https://doi.org/10.2193/0091-7648(2005)33[714:EOCCIS]2.0.CO;2 (2005).Article 

Google Scholar 
Schertler, A. et al. The potential current distribution of the coypu (Myocastor coypus) in Europe and climate change induced shifts in the near future. NeoBiota 58, 129–160. https://doi.org/10.3897/neobiota.58.33118 (2020).Article 

Google Scholar 
Hilts, D. J., Belitz, M. W., Gehring, T. M., Pangle, K. L. & Uzarski, D. G. Climate change and nutria range expansion in the Eastern United States. J. Wild. Manaag. 83, 591–598. https://doi.org/10.1002/jwmg.21629’ (2019).Article 

Google Scholar 
Jarnevich, C. et al. Evaluating simplistic methods to understand current distributions and forecast distribution changes under climate change scenarios: An example with coypu (Myocastor coypus). NeoBiota 32, 107–125. https://doi.org/10.3897/neobiota.32.8884 (2017).Article 

Google Scholar 
Korean Metrological Administration, (2020). Korean Climate Change Assessment Report 2020.Guillera-Arroita, G. et al. Is my species distribution model fit for purpose? Matching data and models to applications. Glob. Ecol. Biogeogr. 24, 276–292. https://doi.org/10.1111/geb.12268 (2015).Article 

Google Scholar 
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Modell. 190, 231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026 (2006).Article 

Google Scholar 
Hong, S., Cowan, P., Do, Y. & Gim, J. S. Seasonal feeding habits of coypu (Myocastor coypus) in South Korea. Hystrix 27, 123–128 (2016).
Google Scholar 
Kim, H. S., Kong, J. Y., Kim, J. H., Yeon, S. C. & Hong, I. H. A Case of Fascioliasis in A Wild Nutria, Myocastor coypus Republic of Korea. Korean J. Parasitol. 56, 375–378. https://doi.org/10.3347/kjp.2018.56.4.375 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Do, Y., Kim, J. Y., Im, R. Y. & Kim, S. B. Spatial distribution and social characteristics for wetlands in Gyeongsangnam-do Province. Korean J. Limnol. 45, 252–260 (2012).
Google Scholar 
IPCC. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. (2013).Sheffels, T. R. Status of Nutria (Myocastor coypus) Populations in the Pacific Northwest and Development of Associated Control and Management Strategies, with an Emphasis on Metropolitan Habitats, PhD Thesis (Portland State Univ., 2013).Doncaster, C. P. & MlCOL, T. Annual cycle of a coypu (Myocastor coypus) population: Male and female strategies. J. Zool. 217, 227–240. https://doi.org/10.1111/j.1469-7998.1989.tb02484.x (1989).Article 

Google Scholar 
Reggiani, G., Boitani, L. & Stefano, R. Population dynamics and regulation in the coypu Myocastor coypus in Central Italy. Ecography 18, 138–146. https://doi.org/10.1111/j.1600-0587.1995.tb00334.x (1995).Article 

Google Scholar 
Cha, Y., Cho, K. H., Lee, H., Kang, T. & Kim, J. H. The relative importance of water temperature and residence time in predicting cyanobacteria abundance in regulated rivers. Water Res. 124, 11–19. https://doi.org/10.1016/j.watres.2017.07.040 (2017).CAS 
Article 
PubMed 

Google Scholar 
Hellmann, J. J., Byers, J. E., Bierwagen, B. G. & Dukes, J. S. Five potential consequences of climate change for invasive species. Conserv. Biol. 22, 534–543. https://doi.org/10.1111/j.1523-1739.2008.00951.x (2008).Article 
PubMed 

Google Scholar 
Pereira, A. D. et al. Modeling the geographic distribution of Myocastor coypus (Mammalia, Rodentia) in Brazil: Establishing priority areas for monitoring and an alert about the risk of invasion. Stud. Neotrop. Fauna Environ. 55, 139–148. https://doi.org/10.1080/01650521.2019.1707419 (2020).Article 

Google Scholar 
Pearson, R. G. & Dawson, T. P. Predicting the impacts of climate change on the distribution of species: Are bioclimate envelope models useful?. Glob. Ecol. Biogeogr. 12, 361–371. https://doi.org/10.1046/j.1466-822X.2003.00042.x (2003).Article 

Google Scholar 
Rogers, C. E. & McCarty, J. P. Climate change and ecosystems of the mid-atlantic region. Clim. Res. 14, 235–244. https://doi.org/10.3354/cr014235 (2000).Article 

Google Scholar 
Adhikari, P. et al. Potential impact of climate change on plant invasion in the Republic of Korea. J. Ecol. Environ. 43, 36. https://doi.org/10.1186/s41610-019-0134-3 (2019).Article 

Google Scholar 
Welsch, D. J., Smart, D. L., Boyer, J. N. & Minkin, P. Forested Wetlands: Functions, Benefits and the Use of Best Management Practices (US Dept of the Interior Fish and Wildlife Service, 2021).
Google Scholar 
Borgnia, M., Galante, M. L. & Cassini, M. H. Diet of the coypu (nutria, Myocastor coypus) in agro-systems of Argentinean pampas. J. Wildl. Manag. 64, 354–361. https://doi.org/10.2307/3803233 (2000).Article 

Google Scholar 
Colares, I. G., Oliveira, R. N. V., Liveira, R. M. & Colares, E. P. Feeding habits of coypu (Myocastor coypus Molina 1978) in the wetlands of the Southern region of Brazil. An. Acad. Bras. Cienc. 82, 671–678. https://doi.org/10.1590/s0001-37652010000300015 (2010).Article 
PubMed 

Google Scholar 
Corriale, M. J., Arias, S. M., Bó, R. F. & Porini, G. Habitat-use patterns of the coypu (Myocastor coypus) in an urban wetland of its original distribution. Acta Theriol. 51, 295–302. https://doi.org/10.1007/BF03192681 (2006).Article 

Google Scholar 
Linscombe, G., Kinler, N. & Wright, V. Nutria population density and vegetative changes in brackish marsh in coastal Louisiana. In Worldwide Furbearer Conference Proceedings (eds Chapman, J. A. & Pursley, D.) 129–141 (Worlwide Furbearer Conference Inc, 1981).
Google Scholar 
Aliev, F. Contribution to the study of nutria migrations (Myocastor coypus). Saugetierkd. Mitt. 16, 301–303 (1968).
Google Scholar 
Farashi, A. & Najafabadi, M. S. A model to predict dispersion of the alien nutria, Myocastor coypus Molina, 1782 (Rodentia) Northern Iran. Acta Zool. Bulg. 69, 65–70 (2017).
Google Scholar 
Vilà, M. et al. How well do we understand the impacts of alien species on ecosystem services? A pan-European, cross-taxa assessment. Front. Ecol. Environ. 8, 135–144. https://doi.org/10.1890/080083 (2010).Article 

Google Scholar 
Adhikari, P. et al. Seasonal and altitudinal variation in roe deer (Capreolus pygargus tianschanicus) diet on Jeju Island, South Korea. J. Asia Pac. Biodivers. 9, 422–428. https://doi.org/10.1016/j.japb.2016.09.001 (2016).Article 

Google Scholar 
Koo, K. A., Kong, W. S., Nibbelink, N. P., Hopkinson, C. S. & Lee, J. H. Potential effects of climate change on the distribution of cold-tolerant evergreen broadleaved woody plants in the Korean Peninsula. PLoS ONE 10, e0134043. https://doi.org/10.1371/journal.pone.0134043 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
National Institute of Biological Research. Korean Red List of Threatened Species 2nd edn. (Ministry of Environement of Korea, 2014).
Google Scholar 
Kil, J. et al. Monitoring of Invasive Alien Species Designated by the Wildlife Protection Act (VII) (Natl Inst. of Environmental Research, 2013).
Google Scholar 
Busby, J. R. In Bioclim, a Bioclimatic Analysis and Prediction System in Nature Conservation: Cost Effective Biological Surveys and Data Analysis (eds Margules, C. R. & Austin, M. P.) 64–68 (CSIRO, 1991).
Google Scholar 
Lee I. H., Park S. H., Kang, H. S. & Cho C. H. Regional climate projections using the HadGEM3-RA in Proceedings of the 3rd International Conference on Earth System Modelling; Hamburg, Germany. 17–21 September 2012. (2012).Robert, J. H., Phillips, S., Leathwick, J. & Elith, J. Package ‘dismo’ version 1.3. , https://cran.rproject.org/web/packages/dismo.pdf (2021).Jeon, J. Y., Adhikari, P. & Seo, C. Impact of climate change on potential dispersal of Paeonia obovata (Paeoniaceae), a critically endangered medicinal plant of South Korea. Ecol. Environ. Conserv. 26, S145–S155 (2020).
Google Scholar 
Dormann, C. F. et al. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46. https://doi.org/10.1111/j.1600-0587.2012.07348.x (2013).Article 

Google Scholar 
Shin, M. S., Seo, C., Lee, M. & Kim, J. Y. Prediction of potential species richness of plants adaptable to climate change in the Korean Peninsula. J. Environ. Impact Assess. 27, 562–581 (2018).
Google Scholar 
Adhikari, P. et al. Northward range expansion of southern butterflies according to climate change in South Korea. KSCCR 11, 643–656. https://doi.org/10.15531/KSCCR.2020.11.6.643 (2020).Article 

Google Scholar 
Song, C. et al. Estimation of future land cover considering shared socioeconomic pathways using scenario generators. KSCCR 9, 223–234. https://doi.org/10.15531/KSCCR.2018.9.3.223 (2018).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. https://doi.org/10.1002/joc.5086 (2017).Article 

Google Scholar 
Dukes, J. S. & Mooney, H. A. Does global change increase the success of biological invaders?. Trends Ecol. Evol. 14, 135–139. https://doi.org/10.1016/s0169-5347(98)01554-7 (1999).CAS 
Article 
PubMed 

Google Scholar 
Thuiller, W., Georges, D., Gueguen, M., Engler, R. & Breiner, F. Package ‘biomod2’: Ensemble Platform for Species Distribution Modeling, version 3.5.1. https://cran.r-project.org/web/packages/biomod2/biomod2.pdf (2021).Elith, J. et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129–151. https://doi.org/10.1111/j.2006.0906-7590.04596.x (2006).Article 

Google Scholar 
Barbet-Massin, M., Jiguet, F., Albert, C. H. & Thuiller, W. Selecting pseudo-absences for species distribution models: how, where and how many?. Methods Ecol. Evol. 3, 327–338. https://doi.org/10.1111/j.2041-210X.2011.00172.x (2012).Article 

Google Scholar 
Brown, J. L. SDM toolbox: A python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses. Methods Ecol. Evol. 5, 694–700. https://doi.org/10.1111/2041-210X.12200 (2014).Article 

Google Scholar 
Veloz, S. D. Spatially autocorrelated sampling falsely inflates measures of accuracy for presence–only niche models. J. Biogeogr. 36, 2290–2299. https://doi.org/10.1111/j.1365-2699.2009.02174.x (2009).Article 

Google Scholar 
Adhikari, P., Lee, Y. H., Park, Y.-S. & Hong, S. H. Assessment of the spatial invasion risk of intentionally introduced alien plant species (IIAPS) under environmental change in South Korea. Biology 10, 1169 (2021).Article 

Google Scholar 
Hong, S. H., Lee, Y. H., Lee, G., Lee, D. H. & Adhikari, P. Predicting impacts of climate change on northward range expansion of invasive weeds in South Korea. Plants 10, 1604. https://doi.org/10.3390/plants10081604 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Pearsons, R. G. Species distribution modeling for conservation educators and practitioners. Lessons Conserv. 3, 54–58 (2010).
Google Scholar 
Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43, 1223–1232. https://doi.org/10.1111/j.1365-2664.2006.01214.x (2006).Article 

Google Scholar 
Thuiller, W., Lavorel, S. & Araújo, M. B. Niche properties and geographical extent as predictors of species sensitivity to climate change. Glob. Ecol. Biogeogr. 14, 347–357. https://doi.org/10.1111/j.1466-822X.2005.00162.x (2005).Article 

Google Scholar 
Lobo, J. M., Jiménez-Valverde, A. & Real, R. AUC: A misleading measure of the performance of predictive distribution models. Global. Ecol. Biogeography. 17, 145–151. https://doi.org/10.1111/j.1466-8238.2007.00358.x (2008).Article 

Google Scholar 
Swets, J. A. Measuring the accuracy of diagnostic systems. Science 240, 1285–1293 (1988).MathSciNet 
CAS 
Article 
ADS 

Google Scholar 
Baldwin, R. Use of maximum entropy modeling in wildlife research. Entropy 11, 854–866. https://doi.org/10.3390/e11040854 (2009).Article 
ADS 

Google Scholar 
Adhikari, P. et al. Potential impact of climate change on the species richness of subalpine plant species in the mountain national parks of South Korea. J. Ecol. Environ. 42, 36. https://doi.org/10.1186/s41610-018-0095-y (2018).Article 

Google Scholar 
Hijmans, R. J. et al. Package ‘raster’ v 3.5: geographical data analysis and modeling. https://cran.r-project.org/web/packages/raster/raster.pdf, (2021). More

We used publicly available, daily, county-level COVID-19 cases and deaths from the Pennsylvania Department of Health (PA DOH) (https://www.health.pa.gov/topics/disease/coronavirus/pages/cases.aspx)13,14 for Centre County and the six neighboring counties with which it shares borders: Blair, Clearfield, Clinton, Huntingdon, Mifflin, and Union (Table 1, Fig. 1). Official COVID-19 reporting for these counties began on March 1, 2020 and is ongoing.Table 1 Summary statistics. COVID-19 reporting, census data, SafeGraph mobile-device derived data.Full size tableFigure 1(a) The cumulative COVID-19 case trajectory for Centre County minus the student cases (red line) has the same shape as the outbreak for the neighboring counties. When looking at student cases only (blue line), the curve leads other counties. Centre County cumulative cases including the university (purple line) take on the shape of an early increase because of the student cases. (b) When aggregating cases from students and non-students, Centre County (purple dot) reported about the number of cases expected for its population size, relative to the neighboring counties (black dots). When the university-reported student cases are separated from the non-student residents of the county, cases reported in Centre County non-students (red dots show possible range of total cases) fall below the number of cases we would expect for the population size. Student cases only (blue dot) are high for the student population size.Full size imageWithin Centre County, PSU provided COVID-19 testing for UP students from August 7, 2020 onward and reported anonymized weekly (2020) and daily (2021) confirmed cases, negative test results, and total tests completed for each campus in a public dashboard (Figs. 1a, S1) (https://virusinfo.psu.edu/covid-19-dashboard/)8. Two types of testing were conducted: students who were enrolled in on-campus classes were randomly selected for surveillance testing and all students could use on-demand testing. Through March 23, 2021, a total of 45,092 random tests were conducted for surveillance, of which 462, or 1.0%, were infected. Surveillance testing efforts ranged from 2440 to 4020 weekly tests through the Fall 2020 semester and were designed to consistently test approximately 1% of students throughout the school year.During the same time period, 75,436 on-demand tests were conducted, of which 6093, or 8.1%, were infected. Students living in both on-campus dorms and off-campus apartments had equal access to university-provided testing. Both on-campus and off-campus residences are within Centre County so positive and negative tests results were also included in the overall Centre County reports of COVID-19 cases.Pre-arrival testing was required for students returning to campus from transmission hotspots. Students with positive tests from pre-arrival testing were required to isolate for 10–14 days after their positive test before arriving on campus. Results from pre-arrival testing for students returning to campus in the Fall of 2020 are not included in these data.At the county level, PA DOH reports the total positive, probable, and negative tests for each county. Because PSU is within Centre County, we estimated the number of total positive and negative tests for non-student Centre County residents by subtracting the PSU estimates (from the PSU dashboard) from the Centre County estimates provided by PA DOH. However, not all student tests were reported to DOH. A portion of the on-demand tests conducted for PSU UP students were completed by a third-party vendor, which required student registration. At the time of student registration, an estimated 0–25% of students registered with an address for a family home that did not reflect their residence in Centre County. Their test results were reported to the county of their registered address. This impacts a maximum of 1,166 positive student test results and 10,760 negative student tests.We conducted a sensitivity analysis to assess the uncertainty in reporting around the negative and positive students tests that may have been misallocated due to the reported residence of student tests. We have calculated the minimum and maximum number of affected positive and negative student tests. This uncertainty from student tests impacts non-student values, which are calculated by subtracting student values from county level reports. The calculations are based on a range of a possible 0–1166 positive student tests misallocated to other counties and up to 10,760 misallocated negative student tests. We have used the ranges of misallocated student tests to calculate, for non-student Centre County residents, the full possible range of (1) total cases, (2) reported cases per capita, and (3) tests per capita (Table 1, Fig. 1b). As a result, our estimates of cases and per capita testing among non-student residents in Centre County are imprecise (Table 1).We also used publicly available data from PA DOH data and PSU to calculate COVID-19 deaths per 100,000 for Centre County, the six neighboring counties, and PSU UP.We acquired county-level data on median household income, population size, and college enrollment status from the 2019 United States Census Bureau’s American Community Survey (ACS) 5-year data (https://www.census.gov/data/developers/data-sets/acs-5year.html) for all seven previously mentioned counties in central PA15.We divide the census block groups (CBG) of Centre County into two categories. We first designated ‘student-dominated CBGs’ as CBGs where  > 50% of ACS responses report enrollment as undergraduate students. We consider data from the 19 student-dominated CBGs in Centre County to be representative of the student population in Centre County. In addition to off-campus locations, the 19 student-dominated CBGs include all on-campus dorms. These 19 CBGs are either on or adjacent to PSU’s UP campus and occupy exactly 6 census tracts. The remaining 25 county census tracts were designated as non-student dominated areas.SafeGraph16 receives geolocation data from anonymized mobile devices collected from numerous applications. We analyzed SafeGraph’s mobile device-derived daily visit counts to points of interest (POI), which are fixed locations, such as businesses or attractions. SafeGraph data provide daily counts for total numbers of visits by mobile devices while using at least one application that provides geolocation data to SafeGraph. A “visit” indicates that the device entered the building or spatial perimeter designated as a POI. We acquired daily visit counts for POIs in the seven previously mentioned counties in central PA from January 1, 2019 forward (Table 1) and within Centre County grouped counts into student-dominated CBGs and non-student dominated CBGs. From January 1, 2020 forward, we used SafeGraph data on the median daily minutes that devices spent outside of their home in each county and the student- and non-student dominated CBG divisions in Centre County. The “home location” of each device is defined by its location overnight. Finally, we used SafeGraph’s weekly calculated number of devices residing in each county and the CBGs of Centre County for 2019 to measure SafeGraph’s data representation across the seven counties and the CBGs of Centre County.No administrative permissions were required to obtain these data. Academic researchers can register to receive access to SafeGraph data at no charge for non-commercial purposes only. See Data Availability statement below for details. More

Castelle CJ, Banfield JF. Major new microbial groups expand diversity and alter our understanding of the tree of life. Cell. 2018;172:1181–97.CAS 
PubMed 

Google Scholar 
Hug LA, Baker BJ, Anantharaman K, Brown CT, Probst AJ, Castelle CJ, et al. A new view of the tree of life. Nat Microbiol. 2016;1:16048.CAS 
PubMed 
PubMed Central 

Google Scholar 
Parks DH, Rinke C, Chuvochina M, Chaumeil PA, Woodcroft BJ, Evans PN, et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat Microbiol. 2017;2:1533–42.CAS 
PubMed 

Google Scholar 
Imachi H, Nobu MK, Nakahara N, Morono Y, Ogawara M, Takaki Y. et al. Isolation of an archaeon at the prokaryote-eukaryote interface. Nature. 2020;577:519–25.CAS 
PubMed 
PubMed Central 

Google Scholar 
Spang A, Saw JH, Jorgensen SL, Zaremba-Niedzwiedzka K, Martijn J, Lind AE. et al. Complex archaea that bridge the gap between prokaryotes and eukaryotes. Nature. 2015;521:173–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Liu Y, Makarova KS, Huang W-C, Wolf YI, Nikolskaya AN, Zhang X. et al. Expanded diversity of Asgard archaea and their relationships with eukaryotes. Nature. 2021;593:553–7.CAS 
PubMed 

Google Scholar 
Huber H, Stetter KO Thermoplasmatales. In: M Dworkin, S Falkow, E Rosenberg, KH Schleifer, E Stackebrandt (eds). The Prokaryotes, 3rd edn. (Springer, New York, 2006), pp 101–12.Inagaki F, Suzuki M, Takai K, Oida H, Sakamoto T, Aoki K, et al. Microbial communities associated with geological horizons in coastal subseafloor sediments from the Sea of Okhotsk. Appl Environ Microbiol. 2003;69:7224–35.CAS 
PubMed 
PubMed Central 

Google Scholar 
Vetriani C, Jannasch HW, MacGregor AJ, Stahl DA, Reysenbach AR. Population structure and phylogenetic characterization of marine benthic archaea in deep-sea sediments. Appl Environ Microbiol. 1999;65:4375–84.CAS 
PubMed 
PubMed Central 

Google Scholar 
Orsi WD, Vuillemin A, Rodriguez P, Coskun OK, Gomez-Saez GV, Lavik G, et al. Metabolic activity analyses demonstrate that Lokiarchaeon exhibits homoacetogenesis in sulfidic marine sediments. Nat Microbiol. 2019;5:248–55.PubMed 

Google Scholar 
Yu T, Wu W, Liang W, Lever MA, Hinrichs KU, Wang F. Growth of sedimentary Bathyarchaeota on lignin as an energy source. Proc Natl Acad Sci USA. 2018;115:6022–7.CAS 
PubMed 
PubMed Central 

Google Scholar 
Yin X, Cai M, Liu Y, Zhou G, Richter-Heitmann T, Aromokeye DA, et al. Subgroup level differences of physiological activities in marine Lokiarchaeota. ISME J. 2020;15:848–61.PubMed 
PubMed Central 

Google Scholar 
Lloyd KG, Schreiber L, Petersen DG, Kjeldsen KU, Lever MA, Steen AD. et al. Predominant archaea in marine sediments degrade detrital proteins. Nature. 2013;496:215–8.CAS 
PubMed 

Google Scholar 
Lin X, Handley KM, Gilbert JA, Kostka JE. Metabolic potential of fatty acid oxidation and anaerobic respiration by abundant members of Thaumarchaeota and Thermoplasmata in deep anoxic peat. ISME J. 2015;9:2740–4.CAS 
PubMed 
PubMed Central 

Google Scholar 
He Y, Li M, Perumal V, Feng X, Fang J, Xie J, et al. Genomic and enzymatic evidence for acetogenesis among multiple lineages of the archaeal phylum Bathyarchaeota widespread in marine sediments. Nat Microbiol. 2016;1:16035.CAS 
PubMed 

Google Scholar 
Zhou Z, Liu Y, Lloyd KG, Pan J, Yang Y, Gu J-D, et al. Genomic and transcriptomic insights into the ecology and metabolism of benthic archaeal cosmopolitan, Thermoprofundales (MBG-D archaea). ISME J. 2019;13:885–901.CAS 
PubMed 

Google Scholar 
Lazar CS, Baker BJ, Seitz K, Hyde AS, Dick GJ, Hinrichs KU, et al. Genomic evidence for distinct carbon substrate preferences and ecological niches of Bathyarchaeota in estuarine sediments. Environ Microbiol. 2016;18:1200–11.CAS 
PubMed 

Google Scholar 
Cai M, Liu Y, Yin X, Zhou Z, Friedrich MW, Richter-Heitmann T, et al. Diverse Asgard archaea including the novel phylum Gerdarchaeota participate in organic matter degradation. Sci China Life Sci. 2020;63:886–97.CAS 
PubMed 

Google Scholar 
Spang A, Stairs CW, Dombrowski N, Eme L, Lombard J, Caceres EF, et al. Proposal of the reverse flow model for the origin of the eukaryotic cell based on comparative analyses of Asgard archaeal metabolism. Nat Microbiol. 2019;4:1138–48.CAS 
PubMed 

Google Scholar 
Baker BJ, Appler KE, Gong X. New microbial biodiversity in marine sediments. Ann Rev Mar Sci. 2020;13:161–75.PubMed 

Google Scholar 
Gorke B, Stulke J. Carbon catabolite repression in bacteria: many ways to make the most out of nutrients. Nat Rev Microbiol. 2008;6:613–24.PubMed 

Google Scholar 
Siliakus MF, van der Oost J, Kengen SWM. Adaptations of archaeal and bacterial membranes to variations in temperature, pH and pressure. Extremophiles. 2017;21:651–70.CAS 
PubMed 
PubMed Central 

Google Scholar 
Takano Y, Chikaraishi Y, Ogawa NO, Nomaki H, Morono Y, Inagaki F. et al. Sedimentary membrane lipids recycled by deep-sea benthic archaea. Nat Geosci. 2010;3:858–61.CAS 

Google Scholar 
Li M, Baker BJ, Anantharaman K, Jain S, Breier JA, Dick GJ. Genomic and transcriptomic evidence for scavenging of diverse organic compounds by widespread deep-sea archaea. Nat Commun. 2015;6:8933.CAS 
PubMed 

Google Scholar 
Dekas AE, Parada AE, Mayali X, Fuhrman JA, Wollard J, Weber PK, et al. Characterizing chemoautotrophy and heterotrophy in marine Archaea and Bacteria with single-cell multi-isotope NanoSIP. Front Microbiol. 2019;10:2682.PubMed 
PubMed Central 

Google Scholar 
Vuillemin A, Wankel SD, Coskun ÖK, Magritsch T, Vargas S, Estes ER. et al. Archaea dominate oxic subseafloor communities over multimillion-year time scales. Sci Adv. 2019;5:eaaw4108PubMed 
PubMed Central 

Google Scholar 
Könneke M, Bernhard AE, de la Torre JR, Walker CB, Waterbury JB, Stahl DA. Isolation of an autotrophic ammonia-oxidizing marine archaeon. Nature. 2005;437:543–6.PubMed 

Google Scholar 
Qin W, Amin SA, Martens-Habbena W, Walker CB, Urakawa H, Devol AH, et al. Marine ammonia-oxidizing archaeal isolates display obligate mixotrophy and wide ecotypic variation. Proc Natl Acad Sci USA. 2014;111:12504–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Aoyagi T, Hanada S, Itoh H, Sato Y, Ogata A, Friedrich MW, et al. Ultra-high-sensitivity stable-isotope probing of rRNA by high-throughput sequencing of isopycnic centrifugation gradients. Environ Microbiol Rep. 2015;7:282–7.CAS 
PubMed 

Google Scholar 
Yin X, Wu W, Maeke M, Richter-Heitmann T, Kulkarni AC, Oni OE, et al. CO2 conversion to methane and biomass in obligate methylotrophic methanogens in marine sediments. ISME J. 2019;13:2107–19.CAS 
PubMed 
PubMed Central 

Google Scholar 
Oni O, Miyatake T, Kasten S, Richter-Heitmann T, Fischer D, Wagenknecht L, et al. Distinct microbial populations are tightly linked to the profile of dissolved iron in the methanic sediments of the Helgoland mud area, North Sea. Front Microbiol. 2015;6:365.PubMed 
PubMed Central 

Google Scholar 
Bohrmann G, Aromokeye AD, Bihler V, Dehning K, Dohrmann I, Gentz T, et al. R/V METEOR Cruise Report M134, Emissions of free gas from cross-shelf troughs of South Georgia: distribution, quantification, and sources for methane ebullition sites in sub-Antarctic waters, Port Stanley (Falkland Islands) – Punta Arenas (Chile). Ber aus dem MARUM und dem Fachbereich Geowissenschaften der Univät Brem. 2017;317:1–220.
Google Scholar 
Yin X, Kulkarni AC, Friedrich MW DNA and RNA stable isotope probing of methylotrophic methanogenic archaea. In: Dumont M, Hernández García M (eds), Stable Isotope Probing, Methods in Molecular Biology, (Humana Press New York, 2019) pp 189–206.Danovaro R, Dell¹Anno A, Fabiano M. Bioavailability of organic matter in the sediments of the Porcupine Abyssal Plain, northeastern Atlantic. Mar Ecol Prog Ser. 2001;220:25–32.CAS 

Google Scholar 
Yang T, Jiang S-Y, Yang J-H, Lu G, Wu N-Y, Liu J, et al. Dissolved inorganic carbon (DIC) and its carbon isotopic composition in sediment pore waters from the Shenhu area, northern South China Sea. J Oceanogr. 2008;64:303–10.CAS 

Google Scholar 
Lueders T, Manefield M, Friedrich MW. Enhanced sensitivity of DNA- and rRNA-based stable isotope probing by fractionation and quantitative analysis of isopycnic centrifugation gradients. Environ Microbiol. 2003;6:73–8.
Google Scholar 
Ovreas L, Forney L, Daae FL, Torsvik V. Distribution of bacterioplankton in meromictic Lake Saelenvannet, as determined by denaturing gradient gel electrophoresis of PCR-amplified gene fragments coding for 16S rRNA. Appl Environ Microbiol. 1997;63:3367–73.CAS 
PubMed 
PubMed Central 

Google Scholar 
Takai K, Horikoshi K. Rapid detection and quantification of members of the archaeal community by quantitative PCR using fluorogenic probes. Appl Environ Microbiol. 2000;66:5066–72.CAS 
PubMed 
PubMed Central 

Google Scholar 
Aromokeye DA, Richter-Heitmann T, Oni OE, Kulkarni A, Yin X, Kasten S, et al. Temperature controls crystalline iron oxide utilization by microbial communities in methanic ferruginous marine sediment incubations. Front Microbiol. 2018;9:2574.PubMed 
PubMed Central 

Google Scholar 
Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods. 2013;10:996–8.CAS 

Google Scholar 
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6.CAS 
PubMed 

Google Scholar 
Wegener G, Kellermann MY, Elvert M. Tracking activity and function of microorganisms by stable isotope probing of membrane lipids. Curr Opin Biotechnol. 2016;41:43–52.CAS 
PubMed 

Google Scholar 
Boschker HTS, Nold SC, Wellsbury P, Bos D, de Graaf W, Pel R. et al. Direct linking of microbial populations to specific biogeochemical processes by 13C-labelling of biomarkers. Nature. 1998;392:801–5.CAS 

Google Scholar 
Sturt HF, Summons RE, Smith K, Elvert M, Hinrichs KU. Intact polar membrane lipids in prokaryotes and sediments deciphered by high-performance liquid chromatography/electrospray ionization multistage mass spectrometry-new biomarkers for biogeochemistry and microbial ecology. Rapid Commun Mass Spectrom. 2004;18:617–28.CAS 
PubMed 

Google Scholar 
Liu XL, Lipp JS, Simpson JH, Lin YS, Summons RE, Hinrichs KU. Mono- and dihydroxyl glycerol dibiphytanyl glycerol tetraethers in marine sediments: Identification of both core and intact polar lipid forms. Geochim Cosmochim Acta. 2012;89:102–15.CAS 

Google Scholar 
Ertefai TF, Heuer VB, Prieto-Mollar X, Vogt C, Sylva SP, Seewald J, et al. The biogeochemistry of sorbed methane in marine sediments. Geochim Cosmochim Acta. 2010;74:6033–48.CAS 

Google Scholar 
Baker BJ, De Anda V, Seitz KW, Dombrowski N, Santoro AE, Lloyd KG. Diversity, ecology and evolution of Archaea. Nat Microbiol. 2020;5:887–900.CAS 
PubMed 

Google Scholar 
Hu W, Pan J, Wang B, Guo J, Li M, Xu M. Metagenomic insights into the metabolism and evolution of a new Thermoplasmata order (Candidatus Gimiplasmatales). Environ Microbiol. 2020;23:3695–709.PubMed 

Google Scholar 
Spang A, Stairs CW, Dombrowski N, Eme L, Lombard J, Caceres EF, et al. Proposal of the reverse flow model for the origin of the eukaryotic cell based on comparative analyses of Asgard archaeal metabolism. Nat Microbiol. 2019;4:1138–48.CAS 
PubMed 

Google Scholar 
Almagro Armenteros JJ, Tsirigos KD, Sonderby CK, Petersen TN, Winther O, Brunak S, et al. SignalP 5.0 improves signal peptide predictions using deep neural networks. Nat Biotechnol. 2019;37:420–3.CAS 
PubMed 

Google Scholar 
Uritskiy GV, DiRuggiero J, Taylor J. MetaWRAP-a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome. 2018;6:158PubMed 
PubMed Central 

Google Scholar 
Li D, Luo R, Liu CM, Leung CM, Ting HF, Sadakane K. et al. MEGAHIT v1.0: A fast and scalable metagenome assembler driven by advanced methodologies and community practices. Methods. 2016;102:3–11.CAS 
PubMed 

Google Scholar 
Wu Y-W, Simmons BA, Singer SW. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics. 2015;32:605–7.PubMed 

Google Scholar 
Alneberg J, Bjarnason BS, de Bruijn I, Schirmer M, Quick J, Ijaz UZ, et al. Binning metagenomic contigs by coverage and composition. Nat Methods. 2014;11:1144–6.CAS 
PubMed 

Google Scholar 
Kang DD, Li F, Kirton E, Thomas A, Egan R, An H. et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ. 2019;7:e7359–e.PubMed 
PubMed Central 

Google Scholar 
Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25:1754–60.CAS 
PubMed 
PubMed Central 

Google Scholar 
Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 2017;27:824–34.CAS 
PubMed 
PubMed Central 

Google Scholar 
Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.CAS 
PubMed 
PubMed Central 

Google Scholar 
Chaumeil PA, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. J Bioinform. 2019;36:1925–7.
Google Scholar 
Hyatt D, Chen GL, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinforma. 2010;11:119.
Google Scholar 
Kanehisa M, Sato Y, Morishima K. BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. J Mol Biol. 2016;428:726–31.CAS 
PubMed 

Google Scholar 
Huerta-Cepas J, Forslund K, Coelho LP, Szklarczyk D, Jensen LJ, von Mering C, et al. Fast genome-wide functional annotation through orthology assignment by eggNOG-mapper. Mol Biol Evol. 2017;34:2115–22.CAS 
PubMed 
PubMed Central 

Google Scholar 
Jones P, Binns D, Chang H-Y, Fraser M, Li W, McAnulla C. et al. InterProScan 5: genome-scale protein function classification. Bioinformatics. 2014;30:1236–40.CAS 
PubMed 
PubMed Central 

Google Scholar 
Pruesse E, Peplies J, Glöckner FO. SINA: accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics. 2012;28:1823–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Ludwig W, Strunk O, Westram R, Richter L, Meier H, Yadhukumar, et al. ARB: a software environment for sequence data. Nucleic Acids Res. 2004;32:1363–71.CAS 
PubMed 
PubMed Central 

Google Scholar 
Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30:1312–3.CAS 
PubMed 
PubMed Central 

Google Scholar 
Letunic I, Bork P. Interactive Tree Of Life (iTOL): an online tool for phylogenetic tree display and annotation. Bioinformatics. 2006;23:127–8.PubMed 

Google Scholar 
Zhou Z, Pan J, Wang F, Gu JD, Li M. Bathyarchaeota: globally distributed metabolic generalists in anoxic environments. FEMS Microbiol Rev. 2018;42:639–55.CAS 
PubMed 

Google Scholar 
Lee MD. GToTree: a user-friendly workflow for phylogenomics. Bioinformatics. 2019;35:4162–4.CAS 
PubMed 
PubMed Central 

Google Scholar 
Eren AM, Esen OC, Quince C, Vineis JH, Morrison HG, Sogin ML. et al. Anvi’o: an advanced analysis and visualization platform for ‘omics data. PeerJ. 2015;3:e1319PubMed 
PubMed Central 

Google Scholar 
Nguyen LT, Schmidt HA, von Haeseler A, Minh BQ. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol Biol Evol. 2015;32:268–74.CAS 

Google Scholar 
Manefield M, Whiteley AS, Ostle N, Ineson P, Bailey MJ. Technical considerations for RNA- based stable isotope probing an approach to associating microbial diversity with microbial community function. Rapid Commun Mass Spectrom. 2002;16:2179–83.CAS 
PubMed 

Google Scholar 
Lazar CS, Baker BJ, Seitz KW, Teske AP. Genomic reconstruction of multiple lineages of uncultured benthic archaea suggests distinct biogeochemical roles and ecological niches. ISME J. 2017;11:1118–29.CAS 
PubMed 
PubMed Central 

Google Scholar 
Villanueva L, Damste JS, Schouten S. A re-evaluation of the archaeal membrane lipid biosynthetic pathway. Nat Rev Microbiol. 2014;12:438–48.CAS 
PubMed 

Google Scholar 
Konstantinidis KT, Rossello-Mora R, Amann R. Uncultivated microbes in need of their own taxonomy. ISME J. 2017;11:2399–406.PubMed 
PubMed Central 

Google Scholar 
Hedges JI, Keil RG. Sedimentary organic matter preservation: an assessment and speculative synthesis. Mar Chem. 1995;49:81–115.CAS 

Google Scholar 
Arndt S, Jørgensen BB, LaRowe DE, Middelburg JJ, Pancost RD, Regnier P. Quantifying the degradation of organic matter in marine sediments: a review and synthesis. Earth-Sci Rev. 2013;123:53–86.CAS 

Google Scholar 
LaRowe DE, Arndt S, Bradley JA, Estes ER, Hoarfrost A, Lang SQ, et al. The fate of organic carbon in marine sediments – New insights from recent data and analysis. Earth-Sci Rev. 2020;204:103146.CAS 

Google Scholar 
Zhu Q-Z, Elvert M, Meador TB, Becker KW, Heuer VB, Hinrichs KU. Stable carbon isotopic compositions of archaeal lipids constrain terrestrial, planktonic, and benthic sources in marine sediments. Geochim Cosmochim Acta. 2021;307:319–37.CAS 

Google Scholar 
Jain S, Caforio A, Driessen AJ. Biosynthesis of archaeal membrane ether lipids. Front Microbiol. 2014;5:641.PubMed 
PubMed Central 

Google Scholar 
Yang S, Lv Y, Liu X, Wang Y, Fan Q, Yang Z, et al. Genomic and enzymatic evidence of acetogenesis by anaerobic methanotrophic archaea. Nat Commun. 2020;11:3941.CAS 
PubMed 
PubMed Central 

Google Scholar 
Zinke LA, Evans PN, Santos-Medellín C, Schroeder AL, Parks DH, Varner RK, et al. Evidence for non-methanogenic metabolisms in globally distributed archaeal clades basal to the Methanomassiliicoccales. Environ Microbiol. 2021;23:340–57.CAS 
PubMed 

Google Scholar 
Bhatnagar L, Jain MK, Aubert JP, Zeikus JG. Comparison of assimilatory organic nitrogen, sulfur, and carbon sources for growth of methanobacterium species. Appl Environ Microbiol. 1984;48:785–90.CAS 
PubMed 
PubMed Central 

Google Scholar 
Maupin-Furlow JA. Proteolytic systems of archaea: slicing, dicing, and mincing in the extreme. Emerg Top Life Sci. 2018;2:561–80.CAS 
PubMed 
PubMed Central 

Google Scholar 
Pohlschroder M, Pfeiffer F, Schulze S, Abdul Halim MF. Archaeal cell surface biogenesis. FEMS Microbiol Rev. 2018;42:694–717.CAS 
PubMed 
PubMed Central 

Google Scholar 
Yancey P, Clark M, Hand S, Bowlus R, Somero G. Living with water stress: evolution of osmolyte systems. Science. 1982;217:1214–22.CAS 
PubMed 

Google Scholar 
Orsi WD, Smith JM, Liu S, Liu Z, Sakamoto CM, Wilken S, et al. Diverse, uncultivated bacteria and archaea underlying the cycling of dissolved protein in the ocean. ISME J. 2016;10:2158–73.CAS 
PubMed 
PubMed Central 

Google Scholar 
Oni OE, Schmidt F, Miyatake T, Kasten S, Witt M, Hinrichs KU, et al. Microbial communities and organic matter composition in surface and subsurface sediments of the Helgoland Mud Area, North Sea. Front Microbiol. 2015;6:1290.PubMed 
PubMed Central 

Google Scholar 
Pelikan C, Wasmund K, Glombitza C, Hausmann B, Herbold CW, Flieder M, et al. Anaerobic bacterial degradation of protein and lipid macromolecules in subarctic marine sediment. ISME J. 2021;15:833–47.CAS 
PubMed 

Google Scholar 
Orsi WD, Schink B, Buckel W, Martin WF. Physiological limits to life in anoxic subseafloor sediment. FEMS Microbiol Rev. 2020;44:219–31.CAS 
PubMed 
PubMed Central 

Google Scholar 
Heijnen JJ, Van, Dijken JP. In search of a thermodynamic description of biomass yields for the chemotrophic growth of microorganisms. Biotechnol Bioeng. 1992;39:833–58.CAS 
PubMed 

Google Scholar 
Braun S, Mhatre SS, Jaussi M, Røy H, Kjeldsen KU, Pearce C, et al. Microbial turnover times in the deep seabed studied by amino acid racemization modelling. Sci Rep. 2017;7:5680.PubMed 
PubMed Central 

Google Scholar  More

Konduri, V. S., Vandal, T. J., Ganguly, S. & Ganguly, A. R. Data science for weather impacts on crop yield. Front. Sustain. Food Syst. 4, 52. https://doi.org/10.3389/fsufs.2020.00052 (2020).Article 

Google Scholar 
Xu, X., Gao, P., Zhu, X., Guo, W., Ding, J., Li, C., … & Wu, X. Design of an integrated climatic assessment indicator (ICAI) for wheat production: A case study in Jiangsu Province, China. Eco. Ind., 101, 943953. https://doi.org/10.1016/J.ECOLIND.2019.01.059 (2019).Moeinizade, S., Hu, G., Wang, L. & Schnable, P. S. Optimizing selection and mating in genomic selection with a look-ahead approach: An operations research framework. G3: Genes Genomes Genet. 9, 2123–2133. https://doi.org/10.1534/g3.118.200842 (2019).Article 

Google Scholar 
Basso, B. & Liu, L. Chapter four: Seasonal crop yield forecast: Methods, applications, and accuracies. In Advances in Agronomy Vol. 154 (ed. Sparks, D. L.) 201–255. https://doi.org/10.1016/bs.agron.2018.11.002 (2019)Zarindast, A., & Wood, J. A Data-Driven Personalized Lighting Recommender System. Front. in Big Data, 4. (2021).Shahhosseini, M., Hu, G., Khaki, S., & Archontoulis, S. V. Corn yield prediction with ensemble CNN-DNN. Front. Plant Sci., 12. (2021).Haghighat, A. K., Ravichandra-Mouli, V., Chakraborty, P., Esfandiari, Y., Arabi, S., & Sharma, A. Applications of deep learning in intelligent transportation systems. J. Big Data Anal. Transp. 2, 115–145. https://doi.org/10.1007/s42421-020-00020-1 (2020).Zarindast, A., Sharma, A., & Wood, J. Application of text mining in smart lighting literature-an analysis of existing literature and a research agenda. Int. J. Info. Mgmt. Data Insights, 1(2), 100032. (2021).Chlingaryan, A., Sukkarieh, S. & Whelan, B. Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review. Comput. Electron. Agric. 151, 61–69. https://doi.org/10.1016/j.compag.2018.05.012 (2018).Article 

Google Scholar 
Zarindast, A., Poddar, S., & Sharma, A. A Data-Driven Method for Congestion Identification and Classification. J. Trans. Eng., Part A: Sys., 148(4), 04022012. (2022)Shahhosseini, M., Martinez-Feria, R. A., Hu, G. & Archontoulis, S. V. Maize yield and nitrate loss prediction with machine learning algorithms. Environ. Res. Lett. 14, 124026. https://doi.org/10.1088/1748-9326/ab5268 (2019).Article 
ADS 

Google Scholar 
Khaki, S. & Wang, L. Crop yield prediction using deep neural networks. Front. Plant Sci.https://doi.org/10.3389/fpls.2019.00621 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Feng, P., Wang, B., Li Liu, D., Waters, C., Xiao, D., Shi, L., & Yu, Q. Dynamic wheat yield forecasts are improved by a hybrid approach using a biophysical model and machine learning technique. Agric. For. Meteorol. 285–286, 107922. https://doi.org/10.1016/j.agrformet.2020.107922 (2020).Article 
ADS 

Google Scholar 
Kang, Y., Ozdogan, M., Zhu, X., Ye, Z., Hain, C., & Anderson, M. Comparative assessment of environmental variables and machine learning algorithms for maize yield prediction in the US Midwest. Environ. Res. Lett. 15, 064005. https://doi.org/10.1088/1748-9326/ab7df9 (2020).Article 
ADS 

Google Scholar 
Van Klompenburg, T., Kassahun, A. & Catal, C. Crop yield prediction using machine learning: A systematic literature review. Comput. Electron. Agric. 177, 105709. https://doi.org/10.1016/j.compag.2020.105709 (2020).Article 

Google Scholar 
Khaki, S., Safaei, N., Pham, H. & Wang, L. WheatNet: A Lightweight Convolutional Neural Network for High-throughput Image-based Wheat Head Detection and Counting. arXiv:2103.09408 (2021).Hassan, M. A., Khalil, A., Kaseb, S. & Kassem, M. A. Exploring the potential of tree-based ensemble methods in solar radiation modeling. Appl. Energy 203, 897–916. https://doi.org/10.1016/j.apenergy.2017.06.104 (2017).Article 

Google Scholar 
Mishra, S. & Santra, D. M. A. G. H. Applications of machine learning techniques in agricultural crop production: A review paper. Indian J. Sci. Technol. 9, 1–14. https://doi.org/10.17485/ijst/2016/v9i38/95032 (2016).Article 

Google Scholar 
Kamilaris, A. & Prenafeta-Boldú, F. Deep learning in agriculture: A survey. Comput. Electron. Agric.https://doi.org/10.1016/j.compag.2018.02.016 (2018).Article 

Google Scholar 
Liakos, K. G., Busato, P., Moshou, D., Pearson, S. & Bochtis, D. Machine learning in agriculture: A review. Sensors 18, 2674. https://doi.org/10.3390/s18082674 (2018).Article 
PubMed Central 
ADS 

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 Sens. 12, 1232. https://doi.org/10.3390/rs12081232 (2020).Article 
ADS 

Google Scholar 
Cao, J., Zhang, Z., Luo, Y., Zhang, L., Zhang, J., Li, Z., & Tao, F. Wheat yield predictions at a county and field scale with deep learning, machine learning, and google earth engine. Eur. J. Agron. 123, 126204. https://doi.org/10.1016/j.eja.2020.126204 (2021).Article 

Google Scholar 
FAO. FAOSTAT (2021).Zhao, G., Webber, H., Hoffmann, H., Wolf, J., Siebert, S., & Ewert, F. The implication of irrigation in climate change impact assessment: a European‐wide study. Glob. Chang. Biol. 21, 4031–4048. https://doi.org/10.1111/gcb.13008 (2015).Article 
PubMed 
ADS 

Google Scholar 
EUROSTAT. Glossary: Nomenclature of territorial units for statistics (NUTS) (2019).COPERNICUS. CORINE Land Cover (2006).Webber, H., Lischeid, G., Sommer, M., Finger, R., Nendel, C., Gaiser, T., & Ewert, F. No perfect storm for crop yield failure in Germany. Environ. Res. Lett. 15, 104012 (2020).Article 
ADS 

Google Scholar 
EUROSTAT. NUTS – Nomenclature of territorial units for statistics – Eurostat (2019).Ämter, S. Regionaldatenbank Deutschland (2020).DWD. Wetter und Klima – Deutscher Wetterdienst (2020).Shook, J., Gangopadhyay, T., Wu, L., Ganapathysubramanian, B., Sarkar, S., & Singh, A. K. Crop yield prediction integrating genotype and weather variables using deep learning. PLOS ONE 16, e0252402. https://doi.org/10.1371/journal.pone.0252402 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Nevavuori, P., Narra, N., Linna, P. & Lipping, T. Crop yield prediction using multitemporal UAV data and spatio-temporal deep learning models. Remote Sens. 12, 4000. https://doi.org/10.3390/rs12234000 (2020).Article 
ADS 

Google Scholar 
Breiman, L. Random forests. Mach. Learn. 45, 5–32. https://doi.org/10.1023/A:1010933404324 (2001).Article 
MATH 

Google Scholar 
Cover, T. & Hart, P. Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13, 21–27. https://doi.org/10.1109/TIT.1967.1053964 (1967).Article 
MATH 

Google Scholar 
Hu, L.-Y., Huang, M.-W., Ke, S.-W. & Tsai, C.-F. The distance function effect on k-nearest neighbor classification for medical datasets. SpringerPlus 5, 1304. https://doi.org/10.1186/s40064-016-2941-7 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
James, G., Witten, D., Hastie, T. & Tibshirani, R. An Introduction to Statistical Learning: With Applications in R (Springer, 2013).Book 

Google Scholar 
Hoerl, A. E. & Kennard, R. W. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics 12, 55–67. https://doi.org/10.1080/00401706.1970.10488634 (1970).Article 
MATH 

Google Scholar 
Breiman, L., Friedman, J., Stone, C. J. & Olshen, R. Classification and Regression Trees 1st edn. (Chapman and Hall/CRC Press, 1984).MATH 

Google Scholar 
Cortes, C. & Vapnik, V. Support-vector networks. Mach. Learn. 20, 273–297. https://doi.org/10.1007/BF00994018 (1995).Article 
MATH 

Google Scholar 
Chen, T., & Guestrin, C. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785–794) (2016).Zhang, L., Zhang, Z., Luo, Y., Cao, J. & Tao, F. Combining optical, fluorescence, thermal satellite, and environmental data to predict county-level maize yield in China using machine learning approaches. Remote Sens. 12, 21. https://doi.org/10.3390/rs12010021 (2020).Article 
ADS 

Google Scholar 
Nigam, A., Garg, S., Agrawal, A. & Agrawal, P. Crop Yield Prediction Using Machine Learning Algorithms. 2019 Fifth International Conference on Image Information Processing (ICIIP) https://doi.org/10.1109/ICIIP47207.2019.8985951 (2019).LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444. https://doi.org/10.1038/nature14539 (2015).CAS 
Article 
ADS 

Google Scholar 
Khaki, S., Wang, L. & Archontoulis, S. V. A CNN-RNN framework for crop yield prediction. Front. Plant Sci. https://doi.org/10.3389/fpls.2019.01750 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Khaki, S., Pham, H., & Wang, L. Simultaneous corn and soybean yield prediction from remote sensing data using deep transfer learning. Sci. Rep. 11(1), 1–14. (2021).Article 

Google Scholar 
Khaki, S., Khalilzadeh, Z. & Wang, L. Predicting yield performance of parents in plant breeding: A neural collaborative filtering approach. PLoS ONE 15, e0233382. https://doi.org/10.1371/journal.pone.0233382 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Lamorski, K., Pachepsky, Y., Sławiński, C. & Walczak, R. T. Using support vector machines to develop pedotransfer functions for water retention of soils in Poland. Soil Sci. Soc. Am. J. 72, 1243–1247. https://doi.org/10.2136/sssaj2007.0280N (2008).CAS 
Article 
ADS 

Google Scholar 
Merdun, H., Çınar, C., Meral, R. & Apan, M. Comparison of artificial neural network and regression pedotransfer functions for prediction of soil water retention and saturated hydraulic conductivity. Soil Tillage Res. 1–2, 108–116. https://doi.org/10.1016/j.still.2005.08.011 (2006).Article 

Google Scholar 
Landeras, G., Ortiz-Barredo, A. & López, J. J. Comparison of artificial neural network models and empirical and semi-empirical equations for daily reference evapotranspiration estimation in the Basque Country (Northern Spain). Agric. Water Manag. 95, 553–565. https://doi.org/10.1016/j.agwat.2007.12.011 (2008).Article 

Google Scholar 
Yamaç, S. S. & Todorovic, M. Estimation of daily potato crop evapotranspiration using three different machine learning algorithms and four scenarios of available meteorological data. Agric. Water Manag. 228, 105875. https://doi.org/10.1016/j.agwat.2019.105875 (2020).Article 

Google Scholar 
Obsie, E. Y., Qu, H. & Drummond, F. Wild blueberry yield prediction using a combination of computer simulation and machine learning algorithms. Comput. Electron. Agric. 178, 105778. https://doi.org/10.1016/j.compag.2020.105778 (2020).Article 

Google Scholar 
Shapley, L. S. A Value for n-Person Games (Princeton University Press, 1953).MATH 

Google Scholar 
Vogel, E., Donat, M. G., Alexander, L. V., Meinshausen, M., Ray, D. K., Karoly, D., … & Frieler, K. The effects of climate extremes on global agricultural yields. Environ. Res. Lett. 14, 054010. https://doi.org/10.1088/1748-9326/ab154b (2019).Article 
ADS 

Google Scholar 
Stokes, V. J., Morecroft, M. D. & Morison, J. I. L. Boundary layer conductance for contrasting leaf shapes in a deciduous broadleaved forest canopy. Agric. For. Meteorol. 139, 40–54. https://doi.org/10.1016/j.agrformet.2006.05.011 (2006).Article 
ADS 

Google Scholar 
Chen, X. & Chen, S. China feels the heat: Negative impacts of high temperatures on China’s rice sector. Aust. J. Agric. Resour. Econ. 62, 576–588. https://doi.org/10.1111/1467-8489.12267 (2018).Article 

Google Scholar 
Tao, F., Xiao, D., Zhang, S., Zhang, Z. & Rötter, R. P. Wheat yield benefited from increases in minimum temperature in the Huang–Huai–Hai Plain of China in the past three decades. Agric. For. Meteorol. 239, 1–14. https://doi.org/10.1016/j.agrformet.2017.02.033 (2017).Article 
ADS 

Google Scholar 
Zheng, C., Zhang, J., Chen, J., Chen, C., Tian, Y., Deng, A., … & Zhang, W. Nighttime warming increases winter-sown wheat yield across major Chinese cropping regions. Field Crops Res. 214, 202–210. https://doi.org/10.1016/j.fcr.2017.09.014 (2017).Article 
ADS 

Google Scholar 
Gibson, L. R. & Paulsen, G. M. Yield components of wheat grown under high temperature stress during reproductive growth. Crop Sci. 39, 1841–1846. https://doi.org/10.2135/cropsci1999.3961841x (1999).Article 

Google Scholar 
Lobell, D. B., Sibley, A. & Ivan Ortiz-Monasterio, J. Extreme heat effects on wheat senescence in India. Nat. Clim. Chang. 2, 186–189. https://doi.org/10.1038/nclimate1356 (2012).Article 
ADS 

Google Scholar 
Tashiro, T. & Wardlaw, I. A comparison of the effect of high temperature on grain development in wheat and rice. Ann. Bot. 64, 59–65. https://doi.org/10.1093/oxfordjournals.aob.a087808 (1989).Article 

Google Scholar 
Wollenweber, B., Porter, J. R. & Schellberg, J. Lack of interaction between extreme high-temperature events at vegetative and reproductive growth stages in wheat. J. Agron. Crop Sci. 189, 142–150. https://doi.org/10.1046/j.1439-037X.2003.00025.x (2003).Article 

Google Scholar 
Mäkinen, H., Kaseva, J., Trnka, M., Balek, J., Kersebaum, K. C., Nendel, C., … & Kahiluoto, H. Sensitivity of European wheat to extreme weather. Field Crops Res., 222, 209–217. https://doi.org/10.1016/j.fcr.2017.11.008 (2018).Article 

Google Scholar 
Farooq, M., Bramley, H., Palta, J. A. & Siddique, K. H. Heat stress in wheat during reproductive and grain-filling phases. Crit. Rev. Plant Sci. 30, 491–507. https://doi.org/10.1080/07352689.2011.615687 (2011).Article 

Google Scholar 
Peichl, M., Thober, S., Meyer, V. & Samaniego, L. The effect of soil moisture anomalies on maize yield in Germany. Nat. Hazards Earth Syst. Sci. 18, 889–906. https://doi.org/10.5194/nhess-18-889-2018 (2018).Article 
ADS 

Google Scholar 
Rezaei, E. E. et al. Quantifying the response of wheat yields to heat stress: The role of the experimental setup. Field Crops Res., 217, 93–103. https://doi.org/10.1016/j.fcr.2017.12.015 (2018).Article 

Google Scholar 
Cannell, R. Q., Belford, R. K., Gales, K., Dennis, C. W. & Prew, R. D. Effects of waterlogging at different stages of development on the growth and yield of winter wheat. J. Sci. Food Agric. 31, 117–132. https://doi.org/10.1002/jsfa.2740310203 (1980).Article 

Google Scholar 
Gömann, H. Wetterextreme: mögliche Folgen für die Landwirtschaft in Deutschland (2018).Kumar, I. E., Venkatasubramanian, S., Scheidegger, C., & Friedler, S. Problems with Shapley-value-based explanations as feature importance measures. In International Conference on Machine Learning (pp. 5491–5500). PMLR. (2020).Rudin, C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215. (2019). More

Tay, D. Vegetable hybrid seed production. in Seeds: Trade, Production and Technology. 18–139. (2002).Piquerez, S. J., Harvey, S. E., Beynon, J. L. & Ntoukakis, V. Improving crop disease resistance: Lessons from research on Arabidopsis and tomato. Front. Plant Sci. 5, 671. https://doi.org/10.3389/fpls.2014.00671 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Mason, A. S. & Batley, J. Creating new interspecific hybrid and polyploid crops. Trends Biotechnol. 33, 436–441. https://doi.org/10.1016/j.tibtech.2015.06.004 (2015).CAS 
Article 
PubMed 

Google Scholar 
Broussard, M. A., Mas, F., Howlett, B., Pattemore, D. & Tylianakis, J. M. Possible mechanisms of pollination failure in hybrid carrot seed and implications for industry in a changing climate. PLoS ONE 12, 180215.  https://doi.org/10.1371/journal.pone.0180215 (2017).CAS 
Article 

Google Scholar 
Wilcock, C. & Neiland, R. Pollination failure in plants: why it happens and when it matters. Trends Plant Sci. 7, 270–277. https://doi.org/10.1016/S1360-1385(02)02258-6 (2002).CAS 
Article 
PubMed 

Google Scholar 
Fijen, T. P., Scheper, J. A., Vogel, C., van Ruijven, J. & Kleijn, D. Insect pollination is the weakest link in the production of a hybrid seed crop. Agric. Ecosyst. Environ. 290, 106743. https://doi.org/10.1016/j.agee.2019.106743 (2020).CAS 
Article 

Google Scholar 
Batra, S. W. Male-fertile potato flowers are selectively buzz-pollinated only by Bombus terricola Kirby in upstate New York. J. Kans. Entomol. Soc. 1, 252–254 (1993).
Google Scholar 
Evans, L., Goodwin, R., Walker, M. & Howlett, B. Honey bee (Apis mellifera) distribution and behaviour on hybrid radish (Raphanus sativus L.) crops. N.Z. Plant Prot. 64, 32–36. https://doi.org/10.30843/nzpp.2011.64.5952 (2011).Article 

Google Scholar 
Estravis Barcala, M. C., Palottini, F. & Farina, W. M. Honey bee and native solitary bee foraging behavior in a crop with dimorphic parental lines. PLoS ONE 14, e0223865. https://doi.org/10.1371/journal.pone.0223865 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Nye, W. P., Shasha’a, N., Campbell, W. & Hamson, A. Insect pollination and seed set of onions (Allium cepa L.). Utah State Univ. Agric. Exp. Station Res. Rep. 6, 1 (1973).
Google Scholar 
Zulkarnain, Z., Eliyanti, E. & Swari, E. I. Pollen viability and stigma receptivity in Swainsona formosa (G. Don) J. Thompson (Fabaceae), an ornamental legume native to Australia. Ornam. Hortic. 25, 158–167. https://doi.org/10.14295/oh.v25i2.2011 (2019).Article 

Google Scholar 
Ne’eman, G., Jürgens, A., Newstrom-Lloyd, L., Potts, S. G. & Dafni, A. A framework for comparing pollinator performance: Effectiveness and efficiency. Biol. Rev. 85, 435–451. https://doi.org/10.1111/j.1469-185X.2009.00108.x (2010).Article 
PubMed 

Google Scholar 
Bione, N. C. P., Pagliarini, M. S. & Toledo, J. F. F. D. Meiotic behavior of several Brazilian soybean varieties. Genet. Mol. 23, 623–631. https://doi.org/10.1590/S1415-47572000000300022 (2000).Article 

Google Scholar 
Levin, D. A. The exploitation of pollinators by species and hybrids of Phlox. Evolution 1, 367–377 (1970).Article 

Google Scholar 
Smith-Huerta, N. L. & Vasek, F. C. Pollen longevity and stigma pre-emption in Clarkia. Am. J. Bot. 71, 1183–1191 (1984).Article 

Google Scholar 
Ashman, T. L. & Arceo-Gómez, G. Toward a predictive understanding of the fitness costs of heterospecific pollen receipt and its importance in co-flowering communities. Am. J. Bot. 100, 1061–1070. https://doi.org/10.3732/ajb.1200496 (2013).Article 
PubMed 

Google Scholar 
Stanghellini, M., Schultheis, J. & Ambrose, J. Pollen mobilization in selected Cucurbitaceae and the putative effects of pollinator abundance on pollen depletion rates. J. Am. Soc. Hortic. Sci. 127, 729–736. https://doi.org/10.21273/Jashs.127.5.729 (2002).Article 

Google Scholar 
Jahed, K. R. & Hirst, P. M. Pollen tube growth and fruit set in apple. HortScience 52, 1054–1059. https://doi.org/10.21273/Hortsci11511-16 (2017).Article 

Google Scholar 
Erdtman, G. Pollen Morphology and Plant Taxonomy: Angiosperms. Vol. 1. (Brill Archive, 1986).Weber, R. W. Pollen identification. Ann. Allergy Asthma Immunol. 80, 141–148. https://doi.org/10.1016/S1081-1206(10)62947-X (1998).CAS 
Article 
PubMed 

Google Scholar 
Castro López, A. J. et al. Seedless watermelons: From the microscope to the table through the greenhouse. High Sch. Students Agric. Scii. Res.. 3. 27–32 (2013).
Google Scholar 
Laws, H. M. Pollen-grain morphology of polyploid Oenotheras. J. Hered. 56, 18–21 (1965).Article 

Google Scholar 
Shoemaker, J. S. Pollen development in the apple, with special reference to chromosome behavior. Bot. Gaz. 81, 148–172 (1926).Article 

Google Scholar 
Hao, L., Ma, H., da Silva, J. A. T. & Yu, X. Pollen morphology of herbaceous peonies with different ploidy levels. J. Am. Soc. Hortic. Sci. 141, 275–284. https://doi.org/10.21273/Jashs.141.3.275 (2016).CAS 
Article 

Google Scholar 
Jacob, Y. & Pierret, V. Pollen size and ploidy level in the genus Rosa. XIX International Symposium on Improvement of Ornamental Plants, Vol. 508. 289–292. (1998).Karlsdóttir, L., Hallsdóttir, M., Thórsson, A. T. & Anamthawat-Jónsson, K. Characteristics of pollen from natural triploid Betula hybrids. Grana 47, 52–59. https://doi.org/10.1080/00173130801927498 (2008).Article 

Google Scholar 
Wrońska-Pilarek, D., Danielewicz, W., Bocianowski, J., Maliński, T. & Janyszek, M. Comparative pollen morphological analysis and its systematic implications on three European Oak (Quercus L., Fagaceae) species and their spontaneous hybrids. PLoS ONE 11, e0161762. https://doi.org/10.1371/journal.pone.0161762 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Martin, C., Viruel, M., Lora, J. & Hormaza, J. I. Polyploidy in fruit tree crops of the genus Annona (Annonaceae). Front. Plant Sci. 10, 99. https://doi.org/10.3389/fpls.2019.00099 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Sedgley, M. & Scholefield, P. B. Stigma secretion in the watermelon before and after pollination. Bot. Gaz. 141, 428–434 (1980).Article 

Google Scholar 
Sedgley, M. Anatomy of the unpollinated and pollinated watermelon stigma. J. Cell Sci. 54, 341–355.  https://doi.org/10.1242/jcs.54.1.341 (1982).Article 

Google Scholar 
Sedgley, M. & Blesing, M. A. Foreign pollination of the stigma of watermelon (Citrullus lanatus [Thunb.] Matsum and Nakai). Bot. Gaz. 143, 210–215 (1982).Article 

Google Scholar 
Hiscock, S. J. & Allen, A. M. Diverse cell signalling pathways regulate pollen-stigma interactions: The search for consensus. New Phytol. 179, 286–317. https://doi.org/10.1111/j.1469-8137.2008.02457.x (2008).CAS 
Article 
PubMed 

Google Scholar 
Swanson, R., Edlund, A. F. & Preuss, D. Species specificity in pollen-pistil interactions. Annu. Rev. Genet. 38, 793–818. https://doi.org/10.1146/annurev.genet.38.072902.092356 (2004).CAS 
Article 
PubMed 

Google Scholar 
Edlund, A. F., Swanson, R. & Preuss, D. Pollen and stigma structure and function: the role of diversity in pollination. Plant Cell 16, S84–S97. https://doi.org/10.1105/tpc.015800 (2004).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Wehner, T. Cucurbit Breeding. https://cucurbitbreeding.wordpress.ncsu.edu/watermelon-breeding/seedless-watermelon-breeding/ (2011).Maynard, D. N. & Elmstrom, G.W. Triploid watermelon production practices and varieties. Acta Hort. 318, 169–173 (1992).Article 

Google Scholar 
Tupý, J. Callose formation in pollen tubes and incompatibility. Biol. Plant. 1, 192–198. https://doi.org/10.1007/BF02928684 (1959).Article 

Google Scholar 
Distefano, G. et al. Pollen tube behavior in different mandarin hybrids. J. Am. Soc. Hortic. Sci. 134, 583–588. https://doi.org/10.21273/Jashs.134.6.583 (2009).Article 

Google Scholar 
Glišić, I. et al. Examination of self-compatibility in promising plum (Prunus domestica L.) genotypes developed at the Fruit Research Institute. Čačak. Sci. Hortic. 224, 156–162. https://doi.org/10.1016/j.scienta.2017.06.006 (2017).Article 

Google Scholar 
Arndt, G. C., Rueda, J., Kidane-Mariam, H. & Peloquin, S. Pollen fertility in relation to open pollinated true seed production in potatoes. Am. Potato. J 67, 499–505. https://doi.org/10.1007/Bf03045112 (1990).Article 

Google Scholar 
Jing, S., Kryger, P., Markussen, B. & Boelt, B. Pollination and plant reproductive success of two ploidy levels in red clover (Trifolium pratense L.). Front. Plant Sci. 1, 1580.  https://doi.org/10.3389/fpls.2021.720069 (2021).Article 

Google Scholar 
Suárez-Mariño, A., Arceo-Gómez, G., Sosenski, P. & Parra-Tabla, V. Patterns and effects of heterospecific pollen transfer between an invasive and two native plant species: The importance of pollen arrival time to the stigma. Am. J. Bot. 106, 1308–1315. https://doi.org/10.1002/ajb2.1361 (2019).Article 
PubMed 

Google Scholar 
FAO. FAOSTAT. Food and Agriculture Organization of the United Nations, Rome, Italy. http://www.fao.org/faostat/en/#data (2017).Stanghellini, M., Ambrose, J. & Schultheis, J. Seed production in watermelon: A comparison between two commercially available pollinators. HortScience 33, 28–30.  https://doi.org/10.21273/Hortsci.33.1.28 (1998).Article 

Google Scholar 
Delaplane, K. S. A. M. D.F. Crop Pollination by Bees. (CABI Publishing, 2005).Wijesinghe, S., Evans, L., Kirkland, L. & Rader, R. A global review of watermelon pollination biology and ecology: The increasing importance of seedless cultivars. Sci. Hortic. 271, 109493. https://doi.org/10.1016/j.scienta.2020.109493 (2020).CAS 
Article 

Google Scholar 
AgMRC. Watermelon. https://www.agmrc.org/commodities-products/vegetables/watermelon (2018).Bomfim, I. G. A., Bezerra, A. D. D. M., Nunes, A. C., Freitas, B. M. & Aragão, F. A. S. D. Pollination requirements of seeded and seedless mini watermelon varieties cultivated under protected environment. Pesqui. Agropecu. Bras. 50, 44–53. https://doi.org/10.1590/s0100-204×2015000100005 (2015).Article 

Google Scholar 
Maynard, D. N. & Elmstrom, G. W. Triploid watermelon production practices and varieties. II International Symposium on Specialty and Exotic Vegetable Crops, Vol. 318. 169–178.Jones, G. D. Pollen analyses for pollination research, acetolysis. J. Pollinat. Ecol. 13, 203–217. https://doi.org/10.26786/1920-7603(2014)19 (2014).Article 

Google Scholar 
Kurtz, E. B. Jr. Pollen morphology of the Cactaceae. Grana 4, 367–372.  https://doi.org/10.1080/00173136309429110 (1963).Article 

Google Scholar 
Halbritter, H. et al. Illustrated Pollen Terminology. 97–127. (Springer, 2018).Punt, W., Hoen, P., Blackmore, S., Nilsson, S. & Le Thomas, A. Glossary of pollen and spore terminology. Rev. Palaeobot. Palynol. 143, 1–81.  https://doi.org/10.1016/j.revpalbo.2006.06.008 (2007).Article 

Google Scholar 
Kaya, Y., Mesut Pınar, S., Emre Erez, M., Fidan, M. & Riding, J. B. Identification of Onopordum pollen using the extreme learning machine, a type of artificial neural network. Palynology 38, 129–137. https://doi.org/10.1080/09500340.2013.868173 (2014).Article 

Google Scholar 
Pruesapan, K. & Van Der Ham, R. Pollen morphology of Trichosanthes (Cucurbitaceae). Grana 44, 75–90. https://doi.org/10.1080/00173130510010512 (2005).Article 

Google Scholar 
Sedgley, M. & Buttrose, M. Some effects of light intensity, daylength and temperature on flowering and pollen tube growth in the watermelon (Citrullus lanatus). Ann. Bot. 42, 609–616. https://doi.org/10.1093/oxfordjournals.aob.a085495 (1978).Article 

Google Scholar 
Martin, F. W. Staining and observing pollen tubes in the style by means of fluorescence. Stain Technol. 34, 125–128. https://doi.org/10.3109/10520295909114663 (1959).CAS 
Article 
PubMed 

Google Scholar 
Godini, A. Counting pollen grains of some almond cultivars by means of an haemocytometer. Riv. Studi Ital. 1, 173–178 (1981).
Google Scholar 
Howlett, B., Evans, L., Pattemore, D. & Nelson, W. Stigmatic pollen delivery by flies and bees: Methods comparing multiple species within a pollinator community. Basic Appl. Ecol. 19, 19–25. https://doi.org/10.1016/j.baae.2016.12.002 (2017).Article 

Google Scholar 
Winfree, R., Williams, N. M., Dushoff, J. & Kremen, C. Native bees provide insurance against ongoing honey bee losses. Ecol. Lett. 10, 1105–1113. https://doi.org/10.1111/j.1461-0248.2007.01110.x (2007).Article 
PubMed 

Google Scholar 
Abdelgadir, H., Johnson, S. & Van Staden, J. Pollen viability, pollen germination and pollen tube growth in the biofuel seed crop Jatropha curcas (Euphorbiaceae). S. Afr. J. Bot. 79, 132–139. https://doi.org/10.1016/j.sajb.2011.10.005 (2012).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. https://www.R-project.org/ (R Foundation for Statistical Computing, 2020).Pinheiro, J., Bates, D., Deb Roy, S. & Sarkar; D. R Core Team. nlme: Linear and nonlinear mixed effects models. R package version 3.1-117. http://CRAN.R-project.org/package=nlme (2014).Lenth, R., Singmann, H., Love, J., Buerkner, P. & Herve, M. emmeans: Estimated marginal means, aka least-squares means. R package. https://CRAN.R-project.org/package=emmeans (2018).Bolker, B. M. et al. Generalized linear mixed models: a practical guide for ecology and evolution. Trends Ecol. Evol 24, 127–135. https://doi.org/10.1016/j.tree.2008.10.008 (2009).Article 
PubMed 

Google Scholar 
Hartig, F. DHARMa: Residual diagnostics for hierarchical (multi-level/mixed) regression models. R package v. 0.2. 0. (Regensburg: University of Regensburg, 2018). More

Marqués, M. J., Martínez-Conde, E., Rovira, J. V. & Ordóñez, S. Heavy metals pollution of aquatic ecosystems in the vicinity of a recently closed underground lead-zinc mine (Basque Country, Spain). Environ. Geol. 40, 1125–1137 (2001).
Google Scholar 
Bud, I., Duma, S., Denuţ, I. & Taşcu, I. Water pollution due to mining activity. Causes and consequences Wasserverunreinigung aufgrund von Bergbauaktivitäten. Ursachen und Konsequenzen. BHM Berg- Hüttenmännische Monatsh. 152, 326–328 (2007).CAS 

Google Scholar 
Ugya, Y. Assessment of ambient air quality resulting from anthropogenic emissions. Am. J. Prev. Med. Public Health https://doi.org/10.5455/ajpmph.20171030080402 (2017).Article 

Google Scholar 
Dore, E. Environment and society: Long-term trends in Latin American mining. Environ. Hist. Camb. 6, 1–29 (2000).
Google Scholar 
Zhou, Q. et al. Total concentrations and sources of heavy metal pollution in global river and lake water bodies from 1972 to 2017. Glob. Ecol. Conserv. 22, 925 (2020).
Google Scholar 
Graesser, J., Aide, T. M., Grau, H. R. & Ramankutty, N. Cropland/pastureland dynamics and the slowdown of deforestation in Latin America. Environ. Res. Lett. 10, 034017 (2015).ADS 

Google Scholar 
Ramírez, A., Pringle, C. M. & Wantzen, K. M. Tropical stream conservation. Trop. Stream Ecol. https://doi.org/10.1016/B978-012088449-0.50012-1 (2008).Article 

Google Scholar 
Uriarte, M., Yackulic, C. B., Lim, Y. & Arce-Nazario, J. A. Influence of land use on water quality in a tropical landscape: A multi-scale analysis. Landsc. Ecol. 26, 1151–1164 (2011).PubMed 
PubMed Central 

Google Scholar 
White, M. & Barquera, S. Mexico adopts food warning labels, why now?. Health Syst. Reform 6, e1752063 (2020).PubMed 

Google Scholar 
Koleff, P. et al. Biodiversity in Mexico: State of knowledge. in Global Biodiversity. 285–337. https://doi.org/10.1201/9780429433634-8. (Apple Academic Press, 2018).Armendáriz-Villegas, E. J. et al. Metal mining and natural protected areas in Mexico: Geographic overlaps and environmental implications. Environ. Sci. Policy 48, 9–19 (2015).
Google Scholar 
Montoya-Lopera, P. et al. New geological, geochronological and geochemical characterization of the San Dimas mineral system: Evidence for a telescoped Eocene-Oligocene Ag/Au deposit in the Sierra Madre Occidental, Mexico. Ore Geol. Rev. 118, 103195 (2020).
Google Scholar 
LeuraVicencio, A. K., CarrizalesYañez, L. & RazoSoto, I. Mercury pollution assessment of mining wastes and soils from former silver amalgamation area in North-Central Mexico. Rev. Int. Contam. Ambient. 33, 655–669 (2017).
Google Scholar 
Veiga, M. M. Introducing New Technologies for Abatement of Global Mercury Pollution in Latin America. United Nations Industrial Development Organization (UNIDO), University of British Columbia (UBC), Center of Mineral Technology (CETEM) (UNIDO, UBC, CETEM, 1997).Camacho, A. et al. Mercury mining in Mexico: I. Community engagement to improve health outcomes from artisanal mining. Ann. Glob. Health 82, 149 (2016).PubMed 

Google Scholar 
IUCN. Benefits Beyond Boundaries: Proceedings of the Vth IUCN World Parks Congress : Durban, South Africa. 8–17 September 2003. (Iucn, 2005).González, S. O., Almeida, C. A., Calderón, M., Mallea, M. A. & González, P. Assessment of the water self-purification capacity on a river affected by organic pollution: Application of chemometrics in spatial and temporal variations. Environ. Sci. Pollut. Res. 21, 10583–10593 (2014).
Google Scholar 
Rico-Sánchez, A. E. et al. Biological diversity in protected areas: Not yet known but already threatened. Glob. Ecol. Conserv. 22, e01006 (2020).
Google Scholar 
Harvey, C. A. et al. Integrating agricultural landscapes with biodiversity conservation in the Mesoamerican hotspot. Conserv. Biol. 22, 8–15 (2008).PubMed 

Google Scholar 
Messerli, B., Grosjean, M. & Vuille, M. Water availability, protected areas, and natural resources in the Andean desert altiplano. Mt. Res. Dev. 17, 229–238 (1997).
Google Scholar 
Servicio Geológico Mexicano. Conoce GeoInfoMex en 3D. https://www.gob.mx/sgm/articulos/conoce-el-sistema-de-consulta-de-informacion-geocientifica-geoinfomex?idiom=es. Accessed 18 Feb 2021. (2019).Resh, V. H. Which group is best? Attributes of different biological assemblages used in freshwater biomonitoring programs. Environ. Monit. Assess. 138, 131–138 (2008).PubMed 

Google Scholar 
Ruiz-Picos, R. A., Sedeño-Díaz, J. E. & López-López, E. Calibrating and validating the biomonitoring working party (BMWP) index for the bioassessment of water quality in neotropical streams. in Water Quality (InTech, 2017).Oertel, N. & Salánki, J. Biomonitoring and bioindicators in aquatic ecosystems. in Modern Trends in Applied Aquatic Ecology. 219–246. https://doi.org/10.1007/978-1-4615-0221-0_10. (Springer, 2011).Goodyear, K. L. & McNeill, S. Bioaccumulation of heavy metals by aquatic macro-invertebrates of different feeding guilds: A review. Sci. Total Environ. 229, 1–19 (1999).ADS 
CAS 

Google Scholar 
Clements, W. H. Small-scale experiments support causal relationships between metal contamination and macroinvertebrate community responses. Ecol. Appl. 14, 954–967 (2004).
Google Scholar 
Michailova, P., Warchałowska-Śliwa, E., Szarek-Gwiazda, E. & Kownacki, A. Does biodiversity of macroinvertebrates and genome response of Chironomidae larvae (Diptera) reflect heavy metal pollution in a small pond?. Environ. Monit. Assess. 184, 1–14 (2012).CAS 
PubMed 

Google Scholar 
Wright, I. A. & Ryan, M. M. Impact of mining and industrial pollution on stream macroinvertebrates: Importance of taxonomic resolution, water geochemistry and EPT indices for impact detection. Hydrobiologia 772, 103–115 (2016).CAS 

Google Scholar 
Wright, I. A. & Burgin, S. Comparison of sewage and coal-mine wastes on stream macroinvertebrates within an otherwise clean upland catchment, Southeastern Australia. Water Air Soil Pollut. 204, 227–241 (2009).ADS 
CAS 

Google Scholar 
Batty, L. C. The potential importance of mine sites for biodiversity. Mine Water Environ. 24, 101–103 (2005).
Google Scholar 
Dolédec, S. & Chessel, D. Co-inertia analysis: An alternative method for studying species–environment relationships. Freshw. Biol. 31, 277–294 (1994).
Google Scholar 
Thioulouse, J. et al. Multivariate Analysis of Ecological Data with ade4. Multivariate Analysis of Ecological Data with ade4. https://doi.org/10.1007/978-1-4939-8850-1. (Springer, 2018).Dodds, W. K., Clements, W. H., Gido, K., Hilderbrand, R. H. & King, R. S. Thresholds, breakpoints, and nonlinearity in freshwaters as related to management. J. N. Am. Benthol. Soc. 29, 988–997 (2010).
Google Scholar 
Sundermann, A., Gerhardt, M., Kappes, H. & Haase, P. Stressor prioritisation in riverine ecosystems: Which environmental factors shape benthic invertebrate assemblage metrics?. Ecol. Indic. 27, 83–96 (2013).
Google Scholar 
Gutiérrez-Yurrita, P. J., García-Serrano, L. A. & Plata, M. R. Is ecotourism a viable option to generate wealth in brittle environments? A reflection on the case of the Sierra Gorda Biosphere Reserve, México. WIT Trans. Ecol. Environ. 161, 141–151 (2012).
Google Scholar 
Vinson, M. R. Long-term dynamics of an invertebrate assemblage downstream from a large dam. Ecol. Appl. 11, 711–730 (2001).
Google Scholar 
Torres-Olvera, M. J., Durán-Rodríguez, O. Y., Torres-García, U., Pineda-López, R. & Ramírez-Herrejón, J. P. Validation of an index of biological integrity based on aquatic macroinvertebrates assemblages in two subtropical basins of central Mexico. Lat. Am. J. Aquat. Res. 46, 945–960 (2018).
Google Scholar 
Carabias Lillo, J., Provencio, E., de la Maza Elvira, J. & Ruiz Corzo, M. Programa de Manejo Reserva de la Biosfera Sierra Gorda. (México, Instituto Nacional de Ecologıa, SEMARNAT, 1999).Macedo, D. R. et al. The relative influence of catchment and site variables on fish and macroinvertebrate richness in cerrado biome streams. Landsc. Ecol. 29, 1001–1016 (2014).
Google Scholar 
Dutra, S. L. & Callisto, M. Macroinvertebrates as tadpole food: Importance and body size relationships. Rev. Bras. Zool. 22, 923–927 (2005).
Google Scholar 
Wang, Z. et al. River-groundwater interaction affected species composition and diversity perpendicular to a regulated river in an arid riparian zone. Glob. Ecol. Conserv. 27, e01595 (2021).
Google Scholar 
López-López, E., Sedeño-Díaz, J. E., Mendoza-Martínez, E., Gómez-Ruiz, A. & Ramírez, E. M. Water quality and macroinvertebrate community in dryland streams: The case of the Tehuacán-Cuicatlán Biosphere Reserve (México) facing climate change. Water (Switzerland) 11, 1376 (2019).
Google Scholar 
O’Connor, N. A. The effects of habitat complexity on the macroinvertebrates colonising wood substrates in a lowland stream. Oecologia 85, 504–512 (1991).ADS 
PubMed 

Google Scholar 
Milner, A. M. & Gloyne-Phillips, I. T. The role of riparian vegetation and woody debris in the development of macroinvertebrate assemblages in streams. River Res. Appl. 21, 403–420 (2005).
Google Scholar 
Vannote, R. L., Minshall, G. W., Cummins, K. W., Sedell, J. R. & Cushing, C. E. The river continuum concept. Can. J. Fish. Aquat. Sci. 37, 130–137 (1980).
Google Scholar 
Malmqvist, B. & Hoffsten, P.-O. Influence of drainage from old mine deposits on benthic macroinvertebrate communities in central Swedish streams. Water Res. 33, 2415–2423 (1999).CAS 

Google Scholar 
Jost, L. Independence of alpha and beta diversities. Ecology 91, 1969–1974 (2010).PubMed 

Google Scholar 
Cottenie, K. Integrating environmental and spatial processes in ecological community dynamics. Ecol. Lett. 8, 1175–1182 (2005).PubMed 

Google Scholar 
Jerves-Cobo, R. et al. Biological impact assessment of sewage outfalls in the urbanized area of the Cuenca River basin (Ecuador) in two different seasons. Limnologica 71, 8–28 (2018).CAS 

Google Scholar 
US Environmental Protection Agency. National Recommended Water Quality Criteria-Aquatic Life Criteria Table. Arsenic. (US Environmental Protection Agency, 1995).DeNicola, D. M. & Lellock, A. J. Nutrient limitation of algal periphyton in streams along an acid mine drainage gradient. J. Phycol. 51, 739–749 (2015).CAS 
PubMed 

Google Scholar 
Younos, T. & Schreiber, M. The Handbook of Environmental Chemistry 68. Tamim Younos, Madeline Schreiber, Katarina Kosič Ficco-Karst Water Environment-Springer International Publishing (2019).pdf. (Springer, 2019).Robles, I. et al. Characterization and remediation of soils and sediments polluted with Mercury: Occurrence, transformations, environmental considerations and San Joaquin’s Sierra Gorda case. in Environmental Risk Assessment of Soil Contamination. https://doi.org/10.5772/57284. (InTech, 2014).Hernández-Silva, G. et al. Presencia Del Hg total En Una Relación Suelo-Planta-Atmósfera Al Sur De La Sierra Gorda De Querétaro, México. TIP Rev. Espec. Ciencias Químico-Biol. 15, 5–15 (2012).
Google Scholar 
Campos, E. M. P. & Muñoz, A. J. H. Minas y mineros: Presencia de metales en sedimentos y restos humanos al sur de la sierra gorda de Querétaro en México. Chungara 45, 161–176 (2013).
Google Scholar 
Carrillo-Martínez, M. & Suter-Cargneluti, M. Tectónica de los alrededores de Zimapán, Hidalgo y Querétaro, Libro Guía de la excursión geológica a la región de Zimapán y áreas circundantes, estados de Hidalgo y Querétaro, Hidalgo, México. in VI Convención Geológica Nacional México, DF, Society Geológica Mexico. 1–20. (1982).Allan, J. D. Stream ecology: Structure and function of running waters. Stream Ecol. Struct. Funct. Run. Waters https://doi.org/10.2307/2261644 (2007).Article 

Google Scholar 
Trang, N. T. T., Shrestha, S., Shrestha, M., Datta, A. & Kawasaki, A. Evaluating the impacts of climate and land-use change on the hydrology and nutrient yield in a transboundary river basin: A case study in the 3S River Basin (Sekong, Sesan, and Srepok). Sci. Total Environ. 576, 586–598 (2017).ADS 
CAS 
PubMed 

Google Scholar 
Khatri, N. & Tyagi, S. Influences of natural and anthropogenic factors on surface and groundwater quality in rural and urban areas. Front. Life Sci. 8, 23–39 (2015).CAS 

Google Scholar 
Simões, N. R. et al. Impact of reservoirs on zooplankton diversity and implications for the conservation of natural aquatic environments. Hydrobiologia 758, 3–17 (2015).
Google Scholar 
Dallas, H. F. & Rivers-Moore, N. A. Critical thermal maxima of aquatic macroinvertebrates: Towards identifying bioindicators of thermal alteration. Hydrobiologia 679, 61–76 (2012).
Google Scholar 
Struijs, J., De Zwart, D., Posthuma, L., Leuven, R. S. & Huijbregts, M. A. Field sensitivity distribution of macroinvertebrates for phosphorus in inland waters. Integr. Environ. Assess. Manag. 7, 280–286 (2011).CAS 
PubMed 

Google Scholar 
Molina, C. I. et al. Transfer of mercury and methylmercury along macroinvertebrate food chains in a floodplain lake of the Beni River, Bolivian Amazonia. Sci. Total Environ. 408, 3382–3391 (2010).ADS 
CAS 
PubMed 

Google Scholar 
Corkum, L. D. Patterns of benthic invertebrate assemblages in rivers of northwestern North America. Freshw. Biol. 21, 191–205 (1989).
Google Scholar 
Dalu, T. et al. Assessing drivers of benthic macroinvertebrate community structure in African highland streams: An exploration using multivariate analysis. Sci. Total Environ. 601–602, 1340–1348 (2017).ADS 
PubMed 

Google Scholar 
Eriksen, T. E. et al. A global perspective on the application of riverine macroinvertebrates as biological indicators in Africa, South-Central America, Mexico and Southern Asia. Ecol. Indic. 126, 107609 (2021).
Google Scholar 
Mercado-Garcia, D. et al. Assessing the freshwater quality of a large-scale mining watershed: The need for integrated approaches. Water 11, 1797 (2019).CAS 

Google Scholar 
Gerhardt, A., Janssens De Bisthoven, L. & Soares, A. M. V. M. Effects of acid mine drainage and acidity on the activity of Choroterpes picteti (Ephemeroptera: Leptophlebiidae). Arch. Environ. Contam. Toxicol. 48, 450–458 (2005).CAS 
PubMed 

Google Scholar 
Qu, X., Wu, N., Tang, T., Cai, Q. & Park, Y.-S. Effects of heavy metals on benthic macroinvertebrate communities in high mountain streams. Ann. Limnol. Int. J. Limnol. 46, 291–302 (2010).
Google Scholar 
Soucek, D. J., Denson, B. C., Schmidt, T. S., Cherry, D. S. & Zipper, C. E. Impaired Acroneuria sp. (Plecoptera, Perlidae) populations associated with aluminum contamination in neutral pH surface waters. Arch. Environ. Contam. Toxicol. 42, 416–422 (2002).CAS 
PubMed 

Google Scholar 
Ankley, G. T. Evaluation of metal/acid-volatile sulfide relationships in the prediction of metal bioaccumulation by benthic macroinvertebrates. Environ. Toxicol. Chem. 15, 2138–2146 (1996).CAS 

Google Scholar 
Croteau, M. N., Luoma, S. N. & Stewart, A. R. Trophic transfer of metals along freshwater food webs: Evidence of cadmium biomagnification in nature. Limnol. Oceanogr. 50, 1511–1519 (2005).ADS 
CAS 

Google Scholar 
Specht, W. L., Cherry, D. S., Lechleitner, R. A. & Cairns, J. Structural, functional, and recovery responses of stream invertebrates to fly ash effluent. Can. J. Fish. Aquat. Sci. 41, 884–896 (1984).CAS 

Google Scholar 
Corbi, J. J., Froehlich, C. G., Strixino, S. T. & Dos Santos, A. Bioaccumulation of metals in aquatic insects of streams located in areas with sugar cane cultivation. Quim. Nova 33, 644–648 (2010).CAS 

Google Scholar 
Poff, N. L., Bledsoe, B. P. & Cuhaciyan, C. O. Hydrologic variation with land use across the contiguous United States: Geomorphic and ecological consequences for stream ecosystems. Geomorphology 79, 264–285 (2006).ADS 

Google Scholar 
Chang, F. H., Lawrence, J. E., Rios-Touma, B. & Resh, V. H. Tolerance values of benthic macroinvertebrates for stream biomonitoring: Assessment of assumptions underlying scoring systems worldwide. Environ. Monit. Assess. 186, 2135–2149 (2014).CAS 
PubMed 

Google Scholar 
Brittain, J. E. Life History Strategies in Ephemeroptera and Plecoptera. in Mayflies and Stoneflies: Life Histories and Biology. 1–12. https://doi.org/10.1007/978-94-009-2397-3_1 (Springer Netherlands, 1990).Bispo, P. C., Oliveira, L. G., Bini, L. M. & Sousa, K. G. Ephemeroptera, Plecoptera and Trichoptera assemblages from riffles in mountain streams of central Brazil: Environmental factors influencing the distribution and abundance of immatures. Braz. J. Biol. 66, 611–622 (2006).CAS 
PubMed 

Google Scholar 
Jacobsen, D. Tropical high-altitude streams. in Tropical Stream Ecology. 219–256. https://doi.org/10.1016/B978-012088449-0.50010-8 (Elsevier, 2008).Jacobsen, D., Rostgaard, S. & Vasconez, J. J. Are macroinvertebrates in high altitude streams affected by oxygen deficiency?. Freshw. Biol. 48, 2025–2032 (2003).
Google Scholar 
Courtney, L. A. & Clements, W. H. Assessing the influence of water and substratum quality on benthic macroinvertebrate communities in a metal-polluted stream: An experimental approach. Freshw. Biol. 47, 1766–1778 (2002).CAS 

Google Scholar 
Buss, D. F. & Salles, F. F. Using Baetidae species as biological indicators of environmental degradation in a Brazilian river basin. Environ. Monit. Assess. 130, 365–372 (2007).CAS 
PubMed 

Google Scholar 
Ristau, K., Faupel, M. & Traunspurger, W. The effects of nutrient enrichment on a freshwater meiofaunal assemblage. Freshw. Biol. 57, 824–834 (2012).CAS 

Google Scholar 
Cornelis, R. & Nordberg, M. General chemistry, sampling, analytical methods, and speciation. in Handbook on the Toxicology of Metals. 11–38. https://doi.org/10.1016/B978-012369413-3/50057-4 (Elsevier, 2007).Santore, R. C., Di Toro, D. M., Paquin, P. R., Allen, H. E. & Meyer, J. S. Biotic ligand model of the acute toxicity of metals. 2. Application to acute copper toxicity in freshwater fish and Daphnia. Environ. Toxicol. Chem. 20, 2397–2402 (2001).CAS 
PubMed 

Google Scholar 
Kozlova, T., Wood, C. M. & McGeer, J. C. The effect of water chemistry on the acute toxicity of nickel to the cladoceran Daphnia pulex and the development of a biotic ligand model. Aquat. Toxicol. 91, 221–228 (2009).CAS 
PubMed 

Google Scholar 
Valdez, R., Guzmán-Aranda, J. C., Abarca, F. J., Tarango-Arámbula, L. A. & Sánchez, F. C. Wildlife conservation and management in Mexico. Wildl. Soc. Bull. 34, 270–282 (2006).
Google Scholar 
INEGI. Por Actividad Económica. https://www.inegi.org.mx/temas/pib/. Accessed 6 Jan 2021. (2020).García, E. Modificaciones Al Sistema de Classificación Climática de Koppen. (Institute of Geography, UNAM, 1988).HACH. User Manual—HACH DR 3900. in 1–148 (2013).APHA. Standard Methods for the Examination of Water and Wastewater. (Association, American Public Health, 2005).NMX-AA-051-SCFI-2001. Análisis de agua—Determinación de metales por absorción atómica en aguas naturales, potables, residuales y residuales tratadas. Norma Mex. 1–47 (2001).Helsel, D. R. Less than obvious: Statistical treatment of data below the detection limit. Environ. Sci. Technol. 24, 1766–1774 (1990).ADS 
CAS 

Google Scholar 
Barbour, M. T., Stribling, J. B. & Verdonschot, P. F. M. The multihabitat approach of USEPA’s rapid bioassessment protocols: benthic macroinvertebrates. Limnetica 25, 839–850 (2006).
Google Scholar 
USEPA. National Rivers and Streams Assessment 2018/19: Field Operations Manual—Wadeable. Vol. EPA-841-B-. 169. (2017).Michaud, J. P. & Wierenga, M. Estimating Discharge and Stream Flows (Ecology Publication, 2005).
Google Scholar 
Hering, D., Moog, O., Sandin, L. & Verdonschot, P. F. M. Overview and application of the AQEM assessment system. Hydrobiologia 516, 1–20 (2004).
Google Scholar 
Merrit, R. & Cummins, K. W. An Introduction to the Aquatic Insects of North America 3rd edn. (Kendall Hunt, 1996).
Google Scholar 
Thorp, J. H. & Covich, A. P. Ecology and Classification of North American Freshwater Invertebrates (Academic Press, 2009).
Google Scholar 
Bueno-Soria, J. Guía de Identificación Ilustrada de Losgéneros de Larvas de Insectos del Orden Trichoptera de México (Universidad Nacional Autónoma de México, 2010).
Google Scholar 
Springer, M., Ramírez, A. & Hanson, P. Macroinvertebrados de agua dulce I. Rev. Biol. Trop. 58, 198 (2010).
Google Scholar 
Hamada, N., Thorp, J. H. & Rogers, D. C. Thorp and Covich’s Freshwater Invertebrates (Elsevier, 2018).
Google Scholar 
Jost, L. et al. Partitioning diversity for conservation analyses. Divers. Distrib. 16, 65–76 (2010).
Google Scholar 
Lavit, C., Escoufier, Y. & Sabatier, R. The ACT (STATIS method) J q G G Fl { q K q *. Comput. Stat. Data Anal. 18, 97–119 (1994).MATH 

Google Scholar  More