Ecology

Tsunekawa, A., Liu, G., Yamanaka, N. & Du, S. Restoration and Development of the Degraded Loess Plateau China 3–21 (Springer Press, 2017).
Google Scholar 
Kimura, R., Kamichika, M., Takayama, N., Matsuoka, N. & Zhang, X. C. Heat balance and soil moisture in the Loess Plateau, China. J. Agric Meteorol. 60(2), 103–113 (2004).
Google Scholar 
Deng, X. P., Lun, S., Zhang, H. P. & Turner, N. C. Improving agricultural water use efficiency in arid and semiarid areas of China. Agric. Water Manage. 80(1), 23–40 (2006).
Google Scholar 
Liu, C. A. et al. Maize yield and water balance is affected by nitrogen application in a film-mulching ridge-furrow system in a semiarid region of China. Eur. J. Agron. 52, 103–111 (2014).CAS 

Google Scholar 
Bu, L. D. et al. The effects of mulching on maize growth, yield and water use in a semi-arid region. Agric. Water Manage. 123, 71–78 (2013).
Google Scholar 
Hou, F. Y. et al. Effect of plastic mulching on the photosynthetic capacity, endogenous hormones and root yield of summer-sown sweet potato (Ipomoea batatas (L.) Lam.) in Northern China. Acta Physiol. Plant. 37, 164 (2015).
Google Scholar 
Jensen, K., Kimball, E. R. & Ricketson, C. L. Effect of perforated plastic row covers on residues of the herbicide DCPA in soil and broccoli. Environ Contam. Toxicol. B 35(6), 716–722 (1985).CAS 

Google Scholar 
Li, F. M., Guo, A. H. & Wei, H. Effects of clear plastic film mulch on yield of spring wheat. Field Crop. Res. 63(1), 79–86 (1999).
Google Scholar 
Liu, J. L. et al. Response of nitrogen use efficiency and soil nitrate dynamics to soil mulching in dryland maize (Zea mays L.) fields. Nutr. Cycl. Agroecosyst. 101(2), 271–283 (2015).CAS 

Google Scholar 
Li, R. et al. Effects on soil temperature, moisture, and maize yield of cultivation with ridge and furrow mulching in the rained area of the Loess Plateau, China. Agric. Water Manage. 116, 101–109 (2013).
Google Scholar 
Anzalone, A., Cirujeda, A., Aibar, J., Pardo, G. & Zaragoza, C. Effect of biodegradable mulch materials on weed control in processing tomatoes. Weed Technol. 24(3), 369–377 (2010).
Google Scholar 
Summers, C. G. & Stapleton, J. J. Use of UV reflective mulch to delay the colonization and reduce the severity of Bemisia argentifolii (Homoptera: Aleyrodidae) infestations in cucurbits. Crop Prot. 21(10), 921–928 (2002).
Google Scholar 
Chen, Y. S. et al. Empirical estimation of pollution load and contamination levels of phthalate esters in agricultural soils from plastic film mulching in China. Environ. Earth Sci. 70(1), 239–247 (2013).CAS 

Google Scholar 
Wang, S. Y. et al. Occurrence of macroplastic debris in the long-term plastic film-mulched agricultural soil: A case study of Northwest China. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2022.154881 (2003).Article 
PubMed 

Google Scholar 
Hu, X. Y., Wen, B. & Shan, X. Q. Survey of phthalate pollution in arable soils in China. J. Environ Monit. 5(4), 649–653 (2003).CAS 
PubMed 

Google Scholar 
Zhou, X. Y. et al. Effects of residual mulch film on the growth and fruit quality of tomato (Lycopersicon esculentum Mill.). Water Air Soil. Pollut. 228(2), 1–18 (2017).ADS 

Google Scholar 
Hu, Q. et al. Effects of residual plastic-film mulch on field corn growth and productivity. Sci. Total Environ. 729, 1–10 (2020).
Google Scholar 
Wang, J. Z. et al. Crop yield and soil organic matter after long-term straw return to soil in China. Nutr. Cycl Agroecosyst. 102(3), 371–381 (2015).
Google Scholar 
Huang, Y. L., Chen, L. D., Fu, B. J., Huang, Z. L. & Gong, J. The wheat yields and water-use efficiency in the Loess Plateau: Straw mulch and irrigation effects. Agric. Water Manage. 72(3), 209–222 (2005).
Google Scholar 
Su, Z. Y. et al. Effects of conservation tillage practices on winter wheat water-use efficiency and crop yield on the Loess Plateau, China. Agric. Water Manage. 87(3), 307–314 (2007).
Google Scholar 
Ibrahim, A., Abaidoo, R. C., Fatondji, D. & Opoku, A. Integrated use of fertilizer micro-dosing and Acacia tumida mulching increases millet yield and water use efficiency in Sahelian semi-arid environment. Nutr. Cycl Agroecosys. 103(3), 375–388 (2015).CAS 

Google Scholar 
Zhang, D. K. et al. Suitable furrow mulching material for maize and sorghum production with ridge-furrow rainwater harvesting in semiarid regions of China. Agric. Water Manage. 228, 105928 (2020).
Google Scholar 
Myint, T. et al. Mulching improved soil water, root distribution and yield of maize in the Loess Plateau of Northwest China. Agric. Water Manage. 241, 106340 (2020).
Google Scholar 
Bai, Y. L. et al. Effects of long-term full straw return on yield and potassium response in wheat-maize rotation. J. Integr. Agric. 14(012), 2467–2476 (2015).CAS 

Google Scholar 
Liu, Z. J., Meng, Y., Cai, M. & Zhou, J. B. Coupled effects of mulching and nitrogen fertilization on crop yield, residual soil nitrate, and water use efficiency of summer maize in the Chinese Loess Plateau. Environ Sci. Pollut R. 24(33), 25849–25860 (2017).CAS 

Google Scholar 
Thomas, F. D., Michael, B., Jürgen, H., Maria, R. F. & Helmut, S. Effects of straw mulch on soil nitrate dynamics, weeds, and yield and soil erosion in organically grown potatoes. Field Crop. Res. 94(2–3), 238–249 (2005).
Google Scholar 
Tu, C., Ristaino, J. B. & Hu, S. J. Soil microbial biomass and activity in organic tomato farming systems: Effects of organic inputs and straw mulching. Soil Biol. Biochem. 38(2), 247–255 (2006).CAS 

Google Scholar 
Rao, Z. X. et al. Effect of rice straw mulching on migration and transportation of Cd, Cu, Zn, and Ni in surface runoff under simulated rainfall. J. Soils Sediment. 16(8), 2021–2029 (2016).CAS 

Google Scholar 
Ma, J., Xu, H., Yagi, K. & Cai, Z. C. Methane emission from paddy soils as affected by wheat straw returning mode. Plant Soil. 313, 167–174 (2008).CAS 

Google Scholar 
Xue, L. L. et al. Influence of straw mulch on yield, chlorophyll contents, lipid peroxidation and antioxidant enzymes activities of soybean under drought stress. J. Food Agric. Environ. 9(2), 699–704 (2011).
Google Scholar 
Wu, Y., Huang, F. Y., Jia, Z. K., Ren, X. R. & Cai, T. Response of soil water, temperature, and maize (Zea mays L.) production to different plastic film mulching patterns in semi-arid areas of Northwest China. Soil Tillage Res. 166, 113–121 (2017).
Google Scholar 
Blake, G. R. & Hartge, K. H. Bulk density. In Methods of Soil Analysis Part 1: Physical and Mineralogical Methods (ed. Klute, A.) 363–375 (American Society of Agronomy, Soil Science Society of America, 1986).
Google Scholar 
Li, F. M., Song, Q. H., Jjemba, P. & Shi, Y. Dynamics of soil microbial biomass and soil fertility in cropland mulched with plastic film in a semiarid agro-ecosystem. Soil Biol. Biochem. 36(11), 1893–1902 (2004).CAS 

Google Scholar 
Zhang, P. et al. Plastic-film mulching for enhanced water-use efficiency and economic returns from maize fields in semiarid China. Front. Plant Sci. 8, 512 (2017).PubMed 
PubMed Central 

Google Scholar 
Custodio, R. P. et al. The effects of increased temperature on crop growth and yield of soybean grown in a temperature gradient chamber. Field Crop. Res. 154, 74–81 (2013).
Google Scholar 
He, G. et al. Wheat yield affected by soil temperature and water under mulching in dryland. Agron. J. 109(6), 2998–3006 (2017).CAS 

Google Scholar 
Zhang, S. L. et al. Effects of mulching and catch cropping on soil temperature, soil moisture and wheat yield on the Loess Plateau of China. Soil Tillage Res. 102(1), 78–86 (2008).
Google Scholar 
Li, R., Hou, X. Q., Jia, Z. K. & Han, Q. F. Soil environment and maize productivity in semi-humid regions prone to drought of Weibei Highland are improved by ridge-and-furrow tillage with mulching. Soil Tillage Res. 196, 104476 (2020).
Google Scholar 
Wang, S. H. et al. Change in the bio-uptake of soil phthalates with increasing mulching years: Underlying mechanism and response to temperature rise. J. Clean Prod. 287(2021), 125049 (2020).
Google Scholar 
Li, W. W., Xiong, L., Wang, C. J., Liao, Y. C. & Wu, W. Optimized ridge–furrow with plastic film mulching system to use precipitation efficiently for winter wheat production in dry semi-humid areas. Agric. Water Manage. 218, 211–221 (2019).
Google Scholar 
Kader, M. A., Nakamura, K., Senge, M., Mojid, M. A. & Kawashima, S. Effects of colored plastic mulch on soil hydrothermal characteristics, growth and water productivity of rain-fed soybean. Irrig. Drain. 69(3), 483–494 (2020).
Google Scholar 
Luo, C. L. et al. Dual plastic film and straw mulching boosts wheat productivity and soil quality under the El Nino in semiarid Kenya. Sci. Total Environ. 738, 139808 (2020).ADS 
CAS 
PubMed 

Google Scholar 
Gouranga, K. & Ashwani, K. Effects of irrigation and straw mulch on water use and tuber yield of potato in Eastern India. Agric. Water Manage. 94(1), 109–116 (2007).
Google Scholar 
Lu, X. J., Li, Z. Z., Sun, Z. G. & Bu, Q. G. Straw mulching reduces maize yield, water, and nitrogen use in Northeastern China. Agron. J. 107(1), 406–414 (2015).
Google Scholar 
Zhou, L. F., Zhao, W. Z., He, J. Q., Flerchinger, G. N. & Feng, H. Simulating soil surface temperature under plastic film mulching during seedling emergence of spring maize with the RZ–SHAW and DNDC models. Soil Tillage Res. 197, 104517 (2020).
Google Scholar 
Chang, L. et al. Straw strip mulching affects soil moisture and temperature for potato yield in semiarid regions. Agron. J. 112(2), 1126–1139 (2020).CAS 

Google Scholar 
Zhang, P. et al. Effects of straw mulch on soil water and winter wheat production in dryland farming. Sci. Rep. 5(1), 209–222 (2015).
Google Scholar 
Ren, X. L., Zhang, P., Chen, X. L., Guo, J. J. & Jia, Z. K. Effect of different mulches under rainfall concentration system on corn production in the semi-arid areas of the Loess Plateau. Sci. Rep. 6(1), 47–50 (2016).
Google Scholar 
Akhtar, K. et al. Integrated use of straw mulch with nitrogen fertilizer improves soil functionality and soybean production. Environ. Int. 132, 105092 (2019).CAS 
PubMed 

Google Scholar 
Eden, G. R. S. M. The impact of organic amendments, mulching and tillage on plant nutrition, Pythium root rot, root-knot nematode and other pests and diseases of capsicum in a subtropical environment, and implications for the development of more sustainable vegetable farming. Australas. Plant Path. 37(2), 123–131 (2008).
Google Scholar 
Kader, M. A., Senge, M., Mojid, M. A., Takeo, O. & Kengo, I. Effects of plastic-hole mulching on effective rainfall and readily available soil moisture under soybean (Glycine max) cultivation. Paddy Water Environ. 15(3), 659–668 (2017).
Google Scholar 
Zhang, Z. et al. Plastic film cover during the fallow season preceding sowing increases yield and water use efficiency of rain-fed spring maize in a semi-arid climate. Agric. Water Manage. 212, 203–210 (2019).
Google Scholar 
Kader, M. A., Nakamura, K., Senge, M., Mojid, M. A. & Kawashima, S. Numerical simulation of water- and heat-flow regimes of mulched soil in rain-fed soybean field in central Japan. Soil Tillage Res. 191, 142–155 (2019).
Google Scholar 
Ryu, J. H. et al. Effects of straw mulching on soil physicochemical properties in Saemangeum reclaimed land. Korean J. Soil Sci. Fert. 49(1), 12–16 (2016).CAS 

Google Scholar 
Yin, W. et al. Growth trajectories of wheat–maize intercropping with straw and plastic management in arid conditions. Agron. J. 112(4), 2777–2790 (2020).
Google Scholar 
Wang, J. et al. Responses of runoff and soil erosion to planting pattern, row direction, and straw mulching on sloped farmland in the corn belt of northeast China. Agric. Water Manage. 25, 106935 (2021).
Google Scholar 
Cao, B. et al. Future landscape of renewable fuel resources: Current and future conservation and utilization of main biofuel crops in China. Sci. Total Environ. 806, 150946 (2022).ADS 
CAS 
PubMed 

Google Scholar 
Khawar, J. et al. Economic assessment of different mulches in conventional and water-saving rice production systems. Environ. Sci. Pollut. Res. 23, 9156–9163 (2016).
Google Scholar  More

Sites description and field operationsA total of six field studies were conducted in two different regions over two seasons. Four studies (two dryland and two irrigated) were in Kansas, United States (dryland: 39°4′30″ N, − 96°44′43″ W, irrigated: 39°4′25″N, − 96°43′12″ W) during the 2019 and 2020 growing seasons (hereafter referred to as USDry19, USIrr19, USDry20, and USIrr20 studies). The remaining two studies (dryland) were in Entre Rios, Argentina (31°50′49″ S; 60°32′16″ W) during the 2018/2019 and 2019/2020 growing seasons (hereafter referred to as Arg19 and Arg20 studies). The soils were Fluventic Hapludolls [silt loam, 40% sand, 13% clay, 47% silt, organic matter (OM) 1.7%, 7.7 pH, 31.1 ppm P (Bray−1)] at the US dryland studies, and Pachic Argiudolls [silty clay loam, 10.1% sand, 30.6% clay and 59.3% silt, OM 3.2%, 6.8 pH, 34.7 ppm P (Bray−1)] at the US irrigated studies. At the Argentinian studies soil was a Vertic Argiudoll in 2019 [silty clay loam to clay loam, 3.9% sand, 27.6% clay, 67.9% silt, OM 2.65%, 7.2 pH, 12.5 ppm P (Bray−1)] and an Acuic Argiudoll in 2020 [silt loam to silty-clay-loam, 5.6% sand, 28.6% clay, 65.8% silt, OM 3.33%].The US dryland and irrigated studies were sown on June 4, 2019, and May 20, 2020. In 2019, the dryland study was replanted on June 29 due to poor emergence after the first sowing. The studies in Argentina were sown on December 5 in 2018 and November 20 in 2019. At all six studies, plots were kept free of weeds, pests, and diseases through recommended chemical control.The genotypes used in the US were P40A47X (MG 4.0) and P39A58X (MG 3.9) (Corteva Agriscience, Johnston, IA, USA) in 2019 and 2020, respectively. Both varieties are tolerant to glyphosate and dicamba herbicides (RR2X) and have low lodging probability. For the northeast region of Kansas, recommended sowing dates range from May 15 to June 15 along with MG 421. In addition, recommended seeding rates are between 270 and 355 thousand seeds ha−1 for low-yielding environments and 190 to 285 thousand seeds ha−1 for medium- and high-yielding environments13. In Argentina, the genotype AW5815IPRO (MG 5.8, Bayer, Leverkusen, Germany) was used both in 2020 and 2021, it is tolerant to glyphosate and sulfonylureas, and has low lodging probability. Recommended sowing dates for Entre Rios considering soybeans as a single crop range from October 20 to December 10, and MG usually range from 4 to 6; lastly, seeding rate recommendations are between 200 and 250 thousand seeds ha−1 in the region22.Study designThe studies carried out in the US were arranged as a split plot design with three replicates in both 2019 and 2020. In 2019, the main plot treatment factor was planter type with two levels [John Deere (Moline, Illinois, US) Max Emerge planter (ME, 12 rows), and John Deere Exact Emerge Planter (EE, 16 rows)], and the split-plot treatment factor was seeding rate with two levels (160 and 321 thousand seeds ha−1). In 2020 the main plot treatment factor was also planter type with two levels (ME and EE), and the split-plot treatment factor was seeding rate with four levels (160, 215, 270 and 321 thousand seeds ha−1). Planting speed was 7 km h−1 in both studies and years, plots were 24 and 32 rows wide when planted with ME and EE, respectively, with 0.76 m row spacing. Plot length was 80 m in the dryland studies and 160 m in the irrigated studies. The studies in Argentina were arranged as a single replicate of each seeding rate (100, 230, 360 and 550 thousand seeds ha−1) in both years. Planting speed was 5.5 km h−1 in both years, and plots were 10 rows wide with 0.52 m row spacing and 350 m in length.All treatment factors in US studies were evaluated with the overall goal of producing substantial variation in the variable of interest, plant-to-plant spatial uniformity, rather than to make an inference of their effect on yield. The Argentinian studies were only used for selection of stand uniformity variables due to the single replicate. Plant spatial uniformity variables were first fitted using the data from US studies (details below), and then the best explanatory metrics were selected to re-fit the relationships combining both data sets from US and Argentina. Finally, sowing dates, maturity groups, and seeding rates evaluated in this study at both locations (Arg and US) were aligned with those recommended for each region.Data collection and spacing uniformity variablesTwo segments of 2 m in length were established early in the season inside each plot. At the V5 (US studies) and R1 (Arg studies) soybean development stage23, the cumulative distance of the plants within each segment was measured and then used to calculate multiple derived variables. Plant spacing (cm) was calculated as the average distance between neighboring plants. In addition, the distance from a plant to each neighboring plant was classified as shorter or longer than the plant spacing (named nearest and farthest neighbor distance, respectively). Achieved versus Target Evenness Index (ATEI, dimensionless) was calculated as the ratio between the observed plant spacing and the theoretical plant spacing (TPS, cm), where TPS is the expected plant spacing derived from a specific seeding rate and row width (Eq. 1).$$ATEI = frac{Spacing;(cm) }{{TPS;(cm)}}$$
(1)
The ATEI index was designed to account for the proximity of the observed plant spacing to the TPS. Values closer to 1 indicate that the plant spacing is close to the TPS and values that are below or above 1 indicate that the plant spacing is lower or higher than the TPS, respectively; thereby departing from an ideal plant spacing. Hence, ATEI values greater than 1 depict both (i) non-uniform plant-to-plant spacing distribution and (ii) plant densities below the target (seeding rate). To further understand the meaning of ATEI, the relative density (rd) was calculated as the ratio between plant density (based on the number of plants in the 2 m segment) and seeding rate.To account for the unevenness of distance from a plant to both neighboring plants within the row, we used the Evenness Index (EI, dimensionless), calculated as the ratio between the distance to the nearest neighbor (cm) and the plant spacing (cm) of a given plant (Eq. 2). The Evenness Index values range from 0 to 1, a value closer to 1 indicates that a plant is equidistantly spaced to both of its neighboring plants within the row, if zero then those plants are occupying the same position (as doubles). It is important to note that EI does not provide information on the spacing (in distance, cm) or how close the spacing is compared to the TPS, but only describes the unevenness distance of a plant to its neighboring plants within a row.$$Evenness ;Index; (EI) = frac{nearest; neighbor ;(cm)}{{Spacing; (cm)}}$$
(2)
In addition, the distance from a plant to its preceding neighboring plant, and the TPS were used to classify the position of each plant into one of eight classes (Fig. 1). Plants were classified in classes ranging from “double” (preceding plant distance  Double-skip) as a function of seeding rate, planter type and their interaction (fixed effects), and block nested in site-year (random effect) (Tables 1 and 2). Independent models for each of the 4 US studies were built assessing the effects of planter type, seeding rate, and their interaction (fixed effects), and seeding rate nested in planter type, and in block (random effects) on the same variables previously mentioned (Supplementary Table 1). The models were run using the lmer function from lme4 package in R (R Core Team, 2021). In addition, the US and Arg studies were combined to evaluate the effect of site-year on yield, plant density, and all stand uniformity variables (Supplementary Fig. 1) using the lm function from package stats. Means separation were performed using Fisher’s LSD (Least Significance Difference) test (alpha = 0.05) with emmeans function from package emmeans.Table 1 Effect of planter type, seeding rate, and their interaction on variables from plant position classification for all US studies. References: percentage of perfectly spaced plants (Perfect), percentage of plants misplaced by 66% (Mis 66), percentage of plants misplaced by 33% (Mis 33), percentage of double plants (Double), percentage of short skips plants (Short-skip), percentage of long skip plants (Long-skip), percentage of double skips plants (Double-skip), and percentage of greater than double skip plants ( > Double-skip).Full size tableTable 2 Effect of planter type, seeding rate, and their interaction on yield and stand uniformity variables for all US studies. References: Spacing between plants standard deviation (Spacing sd), achieved versus targeted evenness index mean and standard deviation (ATEI and ATEI sd, respectively), and evenness index mean and standard deviation (EI and EI sd, respectively).Full size tableCommunity-scale data from the four US studies were combined and fitted to bivariate linear regression models with yield as the response variable and each of the stand spatial uniformity variables as the explanatory variable. Significant models (alpha = 0.05) were further evaluated by calculating the coefficient of determination (R2) and root mean squared error (RMSE) (Fig. 2). Models with the lower RMSE and higher R2 were selected as those that best captured the effect of non-uniform stands on soybean yield. After variables were selected, both US and Arg data sets were combined and the linear regressions between the selected variables and yield were re-fitted to assess the consistency of the relationships when an independent data set was included. Community-scale yield from US and Arg studies was modelled as a function of the selected stand uniformity variable, country (US and Arg), and their interaction (fixed effects) (Fig. 3). The spatial uniformity metric showing the most consistent relationship for both US and Arg studies (i.e., non-significant interaction between stand uniformity metric and country), was selected to continue the analysis. The bivariate linear regression models were run with function lm.Figure 2Relationship between stand uniformity variables and soybean yield for US studies. ATEI mean and sd achieved versus targeted evenness index mean and standard deviation, EI mean and sd evenness index mean and standard deviation, Perfect percentage of perfectly spaced plants, R2 coefficient of determination, RMSE root mean square error. All stand uniformity variables presented a significant slope at alpha = 0.05.Full size imageFigure 3Relationship of spacing standard deviation (Spacing sd, cm) and achieved versus targeted evenness index standard deviation (ATEI sd) to soybean yield. Different colors and line types denote different countries (Argentina, Arg—full line, red points; United States, US—dashed line, blue points). R2 coefficient of determination, RMSE root mean square error.Full size imageDifferent environmental conditions and seeding rate levels may modify the effect of plant spatial uniformity on yield. To explore this, each of the studies from Arg and US were separated into low- (USDry19 and ArgDry20, mean of 2.7 Mg ha−1), medium- (USIrr19, USDry20 and ArgDry19, mean of 3.0 Mg ha−1), and high- (USIrr20, mean of 4.3 Mg ha−1) yield environments based on the effect of site-year on yield (Supplementary Fig. 1). Additionally, the tested seeding rates were separated in low ( 300 thousand seeds ha−1) levels based on the current optimal seeding rate for medium yielding environments (235 thousand seeds ha−1, 4 Mg ha−1)13 and the extreme values proposed by Suhre et al.11 (148 and 445 thousand seeds ha−1). This classification was used to model yield as a function of (i) the selected stand uniformity metric, yield environment, and their interaction, and (ii) the selected stand uniformity metric, seeding rate levels, and their interaction. These models were tested to obtain a robust conclusion on the overall effect of yield environment and seeding rate levels, and their interactions (all treated as fixed effects) with plant-to-plant spatial uniformity relative to the response variable, soybean yield. The Akaike information criteria (AIC) was used to compare the full (with interactions) relative to the reduced models (single effects).Ethics declarationsExperimental research and field studies on plants including the collection of plant material, complied with relevant institutional, national, and international guidelines and legislation. More

Dobzhansky, T. Geographical variation in lady-beetles. Am. Nat. 67, 97–126 (1933).Article 

Google Scholar 
Jablonski, N. G. & Chaplin, G. Colloquium paper: human skin pigmentation as an adaptation to UV radiation. Proc. Natl. Acad. Sci. 107, 8962–8968 (2010).ADS 
CAS 
Article 

Google Scholar 
Wallace, A. R. The colors of animals and plants. Am. Nat. 11, 641–662. https://doi.org/10.1086/271979 (1877).Article 

Google Scholar 
Cuthill, I. C. et al. The biology of color. Science 357, eaan0221 (2017).Article 

Google Scholar 
Branham, M. A. & Wenzel, J. W. The origin of photic behavior and the evolution of sexual communication in fireflies (Coleoptera: Lampyridae). Cladistics 19, 1–22. https://doi.org/10.1016/s0748-3007(02)00131-7 (2003).Article 
PubMed 

Google Scholar 
Maan, M. E. & Cummings, M. E. Female preferences for aposematic signal components in a polymorphic poison frog. Evolution 62, 2334–2345. https://doi.org/10.1111/j.1558-5646.2008.00454.x (2008).Article 
PubMed 

Google Scholar 
Poulton, E. B. The Colours of Animals: Their Meaning and Use, Especially Considered in the Case of Insects (D. Appleton, 1890).
Google Scholar 
Ruxton, G. D., Sherratt, T. N. & Michael, P. Avoiding Attack: The Evolutionary Ecology of Crypsis, Warning Signals and Mimicry (Oxford University Press, 2004).Book 

Google Scholar 
Mappes, J., Marples, N. & Endler, J. A. The complex business of survival by aposematism. Trends Ecol. Evol. 20, 598–603 (2005).Article 

Google Scholar 
Joron, M. & Mallet, J. L. Diversity in mimicry: paradox or paradigm?. Trends Ecol. Evol. 13, 461–466 (1998).CAS 
Article 

Google Scholar 
Summers, R. W. et al. An experimental study of the effects of predation on the breeding productivity of capercaillie and black grouse. J. Appl. Ecol. 41, 513–525 (2004).Article 

Google Scholar 
Nokelainen, O., Hegna, R. H., Reudler, J. H., Lindstedt, C. & Mappes, J. Trade-off between warning signal efficacy and mating success in the wood tiger moth. Proc. R. Soc. B Biol. Sci. 279, 257–265 (2012).Article 

Google Scholar 
Ronka, K. et al. Geographic mosaic of selection by avian predators on hindwing warning colour in a polymorphic aposematic moth. Ecol. Lett. 23, 1654–1663. https://doi.org/10.1111/ele.13597 (2020).Article 
PubMed 

Google Scholar 
Abram, P. K. et al. An insect with selective control of egg coloration. Curr. Biol. 25, 2007–2011. https://doi.org/10.1016/j.cub.2015.06.010 (2015).CAS 
Article 
PubMed 

Google Scholar 
Briolat, E. S. et al. Diversity in warning coloration: selective paradox or the norm?. Biol. Rev. 94, 388–414. https://doi.org/10.1111/brv.12460 (2019).Article 
PubMed 

Google Scholar 
Frost-Mason, S. K. & Mason, K. A. What insights into vertebrate pigmentation has the axolotl model system provided?. Int. J. Dev. Biol. 40, 685–693 (1996).CAS 
PubMed 

Google Scholar 
Stückler, S., Cloer, S., Hödl, W. & Preininger, D. Carotenoid intake during early life mediates ontogenetic colour shifts and dynamic colour change during adulthood. Anim. Behav. 187, 121–135. https://doi.org/10.1016/j.anbehav.2022.03.007 (2022).Article 

Google Scholar 
Benito, M. M., Gonzalez-Solis, J. & Becker, P. H. Carotenoid supplementation and sex-specific trade-offs between colouration and condition in common tern chicks. J. Comp. Physiol. B 181, 539–549. https://doi.org/10.1007/s00360-010-0537-z (2011).CAS 
Article 
PubMed 

Google Scholar 
Stuckert, A. M. M. et al. Variation in pigmentation gene expression is associated with distinct aposematic color morphs in the poison frog Dendrobates auratus. BMC Evol. Biol. 19, 15. https://doi.org/10.1186/s12862-019-1410-7 (2019).Article 

Google Scholar 
Ohsaki, N. A common mechanism explaining the evolution of female-limited and both-sex Batesian mimicry in butterflies. J. Anim. Ecol. 74, 728–734 (2005).Article 

Google Scholar 
Grill, C. P. & Moore, A. J. Effects of a larval antipredator response and larval diet on adult phenotype in an aposematic ladybird beetle. Oecologia 114, 274–282 (1998).ADS 
Article 

Google Scholar 
Friman, V. P., Lindstedt, C., Hiltunen, T., Laakso, J. & Mappes, J. Predation on multiple trophic levels shapes the evolution of pathogen virulence. PLoS ONE 4, e6761 (2009).ADS 
Article 

Google Scholar 
Rojas, B. Behavioural, ecological, and evolutionary aspects of diversity in frog colour patterns. Biol. Rev. 92, 1059–1080. https://doi.org/10.1111/brv.12269 (2017).Article 
PubMed 

Google Scholar 
Hegna, R. H., Saporito, R. A. & Donnelly, M. A. Not all colors are equal: predation and color polytypism in the aposematic poison frog Oophaga pumilio. Evol. Ecol. 27, 831–845 (2013).Article 

Google Scholar 
Pizzigalli, C. et al. Eco-geographical determinants of the evolution of ornamentation in vipers. Biol. J. Linnean Soc. 130, 345–358 (2020).Article 

Google Scholar 
Nielsen, M. E. & Mappes, J. Out in the open: behavior’s effect on predation risk and thermoregulation by aposematic caterpillars. Behav. Ecol. 31, 1031–1039 (2020).Article 

Google Scholar 
Lindstedt, C., Suisto, K., Burdfield-Steel, E., Winters, A. E. & Mappes, J. Defense against predators incurs high reproductive costs for the aposematic moth Arctia plantaginis. Behav. Ecol. 31, 844–850. https://doi.org/10.1093/beheco/araa033 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Freeborn, L. R. The Genetic, Cellular, and Evolutionary Basis of Skin Coloration in the Highly Polymorphic Poison Frog, Oophaga pumilio (University of Pittsburgh, 2021).
Google Scholar 
Garcia, T. S., Straus, R. & Sih, A. Temperature and ontogenetic effects on color change in the larval salamander species Ambystoma barbouri and Ambystoma texanum. Can. J. Zool. 81, 710–715. https://doi.org/10.1139/z03-036 (2003).Article 

Google Scholar 
Caspers, B. A. et al. Developmental costs of yellow colouration in fire salamanders and experiments to test the efficiency of yellow as a warning colouration. Amphibia-Reptilia 41, 373–385. https://doi.org/10.1163/15685381-bja10006 (2020).Article 

Google Scholar 
Wells, K. D. The Ecology and Behaviour of Amphibians (The University of Chicago Press, 2007).Book 

Google Scholar 
Balogova, M., Kyselova, M. & Uhrin, M. Changes in dorsal spot pattern in adult Salamandra salamandra (LINNAEUS, 1758). Herpetozoa 28, 167–171 (2016).
Google Scholar 
Brejcha, J. et al. Variability of colour pattern and genetic diversity of Salamandra salamandra (Caudata: Salamandridae) in the Czech Republic. J. Vertebr. Biol. https://doi.org/10.25225/jvb.21016 (2021).Article 

Google Scholar 
Romeo, G., Giovine, G., Ficetola, G. F. & Manenti, R. Development of the fire salamander larvae at the altitudinal limit in Lombardy (north-western Italy): effect of two cohorts occurrence on intraspecific aggression. North-West J. Zool. 11, 234–240 (2015).
Google Scholar 
Manenti, R. & Ficetola, G. F. Salamanders breeding in subterranean habitats: local adaptations or behavioural plasticity?. J. Zool. 289, 182–188. https://doi.org/10.1111/j.1469-7998.2012.00976.x (2013).Article 

Google Scholar 
Manenti, R., Conti, A. & Pennati, R. Fire salamander (Salamandra salamandra) males’ activity during breeding season: effects of microhabitat features and body size. Acta Herpetol. 12, 29–36 (2017).
Google Scholar 
Weitere, M., Tautz, D., Neumann, D. & Steinfartz, S. Adaptive divergence vs. environmental plasticity: tracing local genetic adaptation of metamorphosis traits in salamanders. Mol. Ecol. 13, 1665–1677. https://doi.org/10.1111/j.1365-294X.2004.02155.x (2004).Article 
PubMed 

Google Scholar 
Manenti, R., Denoel, M. & Ficetola, G. F. Foraging plasticity favours adaptation to new habitats in fire salamanders. Anim. Behav. 86, 375–382. https://doi.org/10.1016/j.anbehav.2013.05.028 (2013).Article 

Google Scholar 
Fernandez-Conradi, P., Mocellin, L., Desfossez, E. & Rasmann, S. Seasonal changes in arthropod diversity patterns along an Alpine elevation gradient. Ecol. Entomol. 45(5), 1035–1043 (2020).Article 

Google Scholar 
Roslin, T. et al. Higher predation risk for insect prey at low latitudes and elevations. Science 356, 742–744. https://doi.org/10.1126/science.aaj1631 (2017).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Ficetola, G. F., Manenti, R., De Bernardi, F. & Padoa-Schioppa, E. Can patterns of spatial autocorrelation reveal population processes? An analysis with the fire salamander. Ecography 35, 693–703. https://doi.org/10.1111/j.1600-0587.2011.06483.x (2012).Article 

Google Scholar 
Maiorano, L., Montemaggiori, A., Ficetola, G. F., O’Connor, L. & Thuiller, W. Tetra-EU 1.0: a species-level trophic meta-web of European tetrapods. Glob. Ecol. Biogeogr. 29, 1452–1457 (2020).Article 

Google Scholar 
Caldonazzi, M., Nistri, A. & Tripepi, S. in Amphibia Vol. XLII (eds B. Lanza et al.) 221–227 (2007).Morales-Castilla, I., Matias, M. G., Gravel, D. & Araújo, M. B. Inferring biotic interactions from proxies. Trends Ecol. Evol. 30, 347–356 (2015).Article 

Google Scholar 
Bernini, F. et al. Atlante degli Anfibi e dei Rettili della Lombardia (Provincia di Cremona, 2004).Peñalver-Alcázar, M., Galán, P. & Aragón, P. Assessing Rensch’s rule in a newt: roles of primary productivity and conspecific density in interpopulation variation of sexual size dimorphism. J. Biogeogr. 46, 2558–2569. https://doi.org/10.1111/jbi.13680 (2019).Article 

Google Scholar 
Limongi, L., Ficetola, G. F., Romeo, G. & Manenti, R. Environmental factors determining growth of salamander larvae: a field study. Curr. Zool. 61, 421–427. https://doi.org/10.1093/czoolo/61.3.421 (2015).Article 

Google Scholar 
Czeczuga, B. Some carotenoids in Chironomus annularius Meig. larvae (Diptera: Chironomidae). Hydrobiologia 36, 353–360. https://doi.org/10.1007/BF00039794 (1970).CAS 
Article 

Google Scholar 
Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

Google Scholar 
Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest package: tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).Article 

Google Scholar 
visreg: Visualization of regression models. R package version 2.2-0. http://CRAN.R-project.org/package=visreg (2015).Preißler, K. et al. More yellow more toxic? Sex rather than alkaloid content is correlated with yellow coloration in the fire salamander. J. Zool. 308, 293–300. https://doi.org/10.1111/jzo.12676 (2019).Article 

Google Scholar 
Kikuchi, D. W., Herberstein, M. E., Barfield, M., Holt, R. D. & Mappes, J. Why aren’t warning signals everywhere? On the prevalence of aposematism and mimicry in communities. Biol. Rev. 96, 2446–2460 (2021).Article 

Google Scholar 
Abd El-Wakeil, K. F. Trophic structure of macro- and meso-invertebrates in Japanese coniferous forest: carbon and nitrogen stable isotopes analyses. Biochem. Systematics Ecol. 37, 317–324. https://doi.org/10.1016/j.bse.2009.05.008 (2009).CAS 
Article 

Google Scholar 
Frelich, L. E. et al. Trophic cascades, invasive species and body-size hierarchies interactively modulate climate change responses of ecotonal temperate-boreal forest. Philos. Trans. R. Soc. B Biol. Sci. 367, 2955–2961. https://doi.org/10.1098/rstb.2012.0235 (2012).Article 

Google Scholar 
Umbers, K. D. L., Silla, A. J., Bailey, J. A., Shaw, A. K. & Byrne, P. G. Dietary carotenoids change the colour of Southern corroboree frogs. Biol. J. Linnean Soc. 119, 436–444. https://doi.org/10.1111/bij.12818 (2016).Article 

Google Scholar 
Balogova, M. & Uhrin, M. Sex-biased dorsal spotted patterns in the fire salamander (Salamandra salamandra). Salamandra 51, 12–18 (2015).
Google Scholar 
Arenas, L. M. & Stevens, M. Diversity in warning coloration is easily recognized by avian predators. J. Evol. Biol. 30, 1288–1302. https://doi.org/10.1111/jeb.13074 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Gilby, B. L., Burfeind, D. D. & Tibbetts, I. R. Better red than dead? Potential aposematism in a harpacticoid copepod, Metis holothuriae. Mar. Environ. Res. 74, 73–76. https://doi.org/10.1016/j.marenvres.2011.12.001 (2012).CAS 
Article 
PubMed 

Google Scholar 
Przeczek, K., Mueller, C. & Vamosi, S. M. The evolution of aposematism is accompanied by increased diversification. Integr. Zool. 3, 149–156. https://doi.org/10.1111/j.1749-4877.2008.00091.x (2008).Article 
PubMed 

Google Scholar 
Moore, M. P. & Martin, R. A. On the evolution of carry-over effects. J Anim. Ecol. 88, 1832–1844. https://doi.org/10.1111/1365-2656.13081 (2019).Article 
PubMed 

Google Scholar 
Raffaëlli, J. Les Urodeles du monde (Penclen Edition, 2007).Velo-Anton, G., Zamudio, K. R. & Cordero-Rivera, A. Genetic drift and rapid evolution of viviparity in insular fire salamanders (Salamandra salamandra). Heredity 108, 410–418. https://doi.org/10.1038/Hdy.2011.91 (2012).CAS 
Article 
PubMed 

Google Scholar 
Rodriguez, A. et al. Inferring the shallow phylogeny of true salamanders (Salamandra) by multiple phylogenomic approaches. Mol. Phylogenet. Evol. 115, 16–26. https://doi.org/10.1016/j.ympev.2017.07.009 (2017).Article 
PubMed 

Google Scholar 
Speed, M. P. & Ruxton, G. D. Aposematism: what should our starting point be?. Proc. Biol. Sci. 272, 431–438. https://doi.org/10.1098/rspb.2004.2968 (2005).Article 
PubMed 
PubMed Central 

Google Scholar 
Tarvin, R. D., Powell, E. A., Santos, J. C., Ron, S. R. & Cannatella, D. C. The birth of aposematism: high phenotypic divergence and low genetic diversity in a young clade of poison frogs. Mol. Phylogenet. Evol. 109, 283–295. https://doi.org/10.1016/j.ympev.2016.12.035 (2017).Article 
PubMed 

Google Scholar 
Jusczcyk, W. & Zakrzewski, M. External morphology of larval stages of the spotted salamander Salamandra salamandra (L.). Acta Biol. Crac. 23, 127–135. https://doi.org/10.1111/jzo.12676 (1981).Article 

Google Scholar  More

Walas, Ł, Ganatsas, P., Iszkuło, G., Thomas, P. A. & Dering, M. Spatial genetic structure and diversity of natural populations of Aesculus hippocastanum L. in Greece. PLoS ONE 14, e0226225 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Song, Y. G. et al. Past, present and future suitable areas for the relict tree Pterocarya fraxinifolia (Juglandaceae): Integrating fossil records, niche modeling, and phylogeography for conservation. Eur. J. For. Res. 140, 1323–1339 (2021).Article 

Google Scholar 
Dyderski, M. K., Paź, S., Frelich, L. E. & Jagodziński, A. M. How much does climate change threaten European forest tree species distributions?. Glob. Change Biol. 24, 1150–1163 (2018).ADS 
Article 

Google Scholar 
Chakraborty, D., Móricz, N., Rasztovits, E., Dobor, L. & Schueler, S. Provisioning forest and conservation science with high-resolution maps of potential distribution of major European tree species under climate change. Ann. For. Sci. 78, 1–18 (2021).Article 

Google Scholar 
Williams, J. N. et al. Using species distribution models to predict new occurrences for rare plants. Divers. Distrib. 15, 565–576 (2009).Article 

Google Scholar 
Watling, J. I. et al. Performance metrics and variance partitioning reveal sources of uncertainty in species distribution models. Ecol. Modell. 309, 48–59 (2015).ADS 
Article 

Google Scholar 
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).Article 

Google Scholar 
Phillips, S. J., Dudík, M. & Schapire, R. E. [Internet] Maxent software for modeling species niches and distributions. url: http://biodiversityinformatics.amnh.org/open_source/maxent/. Accessed 13 July 2022.Elith, J. et al. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 17, 43–57 (2011).Article 

Google Scholar 
Marcer, A., Sáez, L., Molowny-Horas, R., Pons, X. & Pino, J. Using species distribution modelling to disentangle realised versus potential distributions for rare species conservation. Biol. Conserv. 166, 221–230 (2013).Article 

Google Scholar 
Rigling, A., Eilmann, B., Koechli, R. & Dobbertin, M. Mistletoe-induced crown degradation in Scots pine in a xeric environment. Tree Physiol. 30, 845–852 (2010).PubMed 
Article 

Google Scholar 
Sangüesa-Barreda, G., Linares, J. C. & Camarero, J. J. Mistletoe effects on Scots pine decline following drought events: Insights from within-tree spatial patterns, growth and carbohydrates. Tree Physiol. 32, 585–598 (2012).PubMed 
Article 

Google Scholar 
Kollas, C., Gutsch, M., Hommel, R., Lasch-Born, P. & Suckow, F. Mistletoe-induced growth reductions at the forest stand scale. Tree Physiol. 38, 735–744 (2018).PubMed 
Article 

Google Scholar 
Schulze, E. D. & Ehleringer, J. R. The effect of nitrogen supply on growth and water-use efficiency of xylem-tapping mistletoes. Planta 162, 268–275 (1984).PubMed 
Article 

Google Scholar 
Escher, P. et al. Transpiration, CO2 assimilation, WUE, and stomatal aperture in leaves of Viscum album L: Effect of abscisic acid (ABA) in the xylem sap of its host (Populus x euamericana). Plant Physiol. Biochem. 46, 64–70 (2008).PubMed 
Article 

Google Scholar 
Zweifel, R., Bangerter, S., Rigling, A. & Sterck, F. J. Pine and mistletoes: How to live with a leak in the water flow and storage system?. J. Exp. Bot. 63, 2565–2578 (2012).PubMed 
Article 

Google Scholar 
Mutlu, S., Osma, E., Ilhan, V., Turkoglu, H. I. & Atici, O. Mistletoe (Viscum album) reduces the growth of the Scots pine by accumulating essential nutrient elements in its structure as a trap. Trees 30, 815–824 (2016).Article 

Google Scholar 
Tsopelas, P., Angelopoulos, A., Economou, A. & Soulioti, N. Mistletoe (Viscum album) in the fir forest of Mount Parnis Greece. For. Ecol. Manag. 202, 59–65 (2004).Article 

Google Scholar 
Dobbertin, M. & Rigling, A. Pine mistletoe (Viscum album ssp. austriacum) contributes to Scots pine (Pinus sylvestris) mortality in the Rhone valley of Switzerland. For. Pathol. 36, 309–322 (2006).Article 

Google Scholar 
Lech, P., Żółciak, A. & Hildebrand, R. Occurrence of European mistletoe (Viscum album L.) on forest trees in Poland and its dynamics of spread in the period 2008–2018. Forests 11, 83 (2020).Article 

Google Scholar 
Iszkuło, G. et al. Jemioła jako zagrożenie dla zdrowotności drzewostanów iglastych. Sylwan 164, 226–236 (2020) ([In Polish]).
Google Scholar 
Mellado, A., Morillas, L., Gallardo, A. & Zamora, R. Temporal dynamic of parasite-mediated linkages between the forest canopy and soil processes and the microbial community. New Phytol. 211, 1382–1392 (2016).PubMed 
Article 

Google Scholar 
Mellado, A. & Zamora, R. Generalist birds govern the seed dispersal of a parasitic plant with strong recruitment constraints. Oecologia 176, 139–147 (2014).ADS 
PubMed 
Article 

Google Scholar 
Hódar, J. A., Lázaro-González, A. & Zamora, R. Beneath the mistletoe: parasitized trees host a more diverse herbaceous vegetation and are more visited by rabbits. Ann. For. Sci. 75, 1–8 (2018).Article 

Google Scholar 
Zuber, D. Biological flora of Central Europe: Viscum album L. Flora Morphol. Distrib Funct. Ecol. Plants 199, 181–203 (2004).Article 

Google Scholar 
Urech, K. & Baumgartner, S. Chemical constituents of Viscum album L.: Implications for the pharmaceutical preparation of mistletoe. In: Mistletoe: From mythology to evidence-based medicine. (eds. Zänker, K.S. & Kaveri, S. V.), 11–23. (S. Karger AG, Basel, Switzerland, 2015).Singh, B. N. et al. European Viscum album: a potent phytotherapeutic agent with multifarious phytochemicals, pharmacological properties and clinical evidence. RSC Adv. 6, 23837–23857 (2016).ADS 
Article 

Google Scholar 
Jeffree, C. E. & Jeffree, E. P. Redistribution of the potential geographical ranges of mistletoe and colorado beetle in Europe in response to the temperature component of climate change. Funct. Ecol. 10, 562–577 (1996).Article 

Google Scholar 
Troels-Smith, J. Ivy, mistletoe and elm climate indicators-fodder plants. A contribution to the interpretation of the pollen zone border VII-VIII. Dan. Geol. Undersøg. IV Række 4, 1–32 (1960).
Google Scholar 
Dobbertin, M. et al. The upward shift in altitude of pine mistletoe (Viscum album ssp. austriacum) in Switzerland—the result of climate warming?. Int. J. Biometeorol. 50, 40–47 (2005).ADS 
PubMed 
Article 

Google Scholar 
Zamora, R. & Mellado, A. Identifying the abiotic and biotic drivers behind the elevational distribution shift of a parasitic plant. Plant Biol. 21, 307–317 (2019).PubMed 
Article 

Google Scholar 
Barney, C. W., Hawksworth, F. G. & Geils, B. W. Hosts of Viscum album. Eur. J. Plant Pathol. 28, 187–208 (1998).
Google Scholar 
Böhling, N. et al. Notes on the Cretan mistletoe, Viscum album subsp. creticum subsp. nova (Loranthaceae/Viscaceae). Isr. J. Plant Sci. 50, 77–84 (2002).
Google Scholar 
Plants of the World Online [Internet] url: https://powo.science.kew.org/taxon/urn:lsid:ipni.org:names:921668-1. Accessed 13 July 2022.Zuber, D. & Widmer, A. Phylogeography and host race differentiation in the European mistletoe (Viscum album L.). Mol. Ecol. 18, 1946–1962 (2009).PubMed 
Article 

Google Scholar 
Schaller, G., Urech, K., Grazi, G. & Giannattasio, M. Viscotoxin composition of the three European subspecies of Viscum album. Planta Med 64, 677–678 (1998).PubMed 
Article 

Google Scholar 
Kahle-Zuber, D. Biology and evolution of the European mistletoe (Viscum album). Doctoral Thesis. ETH Zurich. (2008).Zuber, D. & Widmer, A. Genetic evidence for host specificity in the hemi-parasitic Viscum album L. (Viscaceae). Mol. Ecol. 9, 1069–1073 (2000).PubMed 
Article 

Google Scholar 
Mejnartowicz, L. Relationship and genetic diversity of mistletoe [Viscum album L.] subspecies. Acta Soc. Bot. Pol. Pol. 75, 39–49 (2006).Article 

Google Scholar 
Xie, W., Adolf, J. & Melzig, M. F. Identification of Viscum album L. miRNAs and prediction of their medicinal values. PLoS ONE 12, e0187776 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Valle, A. C. V., de Carvalho, A. C. & Andrade, R. V. Viscum album-literature review. Int. J. Sci. Res 10, 63–71 (2021).
Google Scholar 
Schröder, L. et al. The gene space of European mistletoe (Viscum album). Plant J. 109, 278–294 (2022).PubMed 
Article 

Google Scholar 
Sangüesa-Barreda, G. et al. Delineating limits: Confronting predicted climatic suitability to field performance in mistletoe populations. J. Ecol. 106, 2218–2229 (2018).Article 

Google Scholar 
GBIF.org [Internet] GBIF Occurrence Download Doi: https://doi.org/10.15468/dl.zw6f5q. Accessed 27 July 2021.GBIF.org [Internet] GBIF Occurrence Download Doi: https://doi.org/10.15468/dl.6wmc9d. Accessed 6 August 2021.FloraWeb [Internet] url: https://www.floraweb.de. Accessed 10 December 2021.Pladias – Database of the Czech Flora and Vegetation. [Internet] url: www.pladias.cz. Accessed 14 July 2022.Zając, A., Zając, M., Tertil, R. & Harman, I. Atlas rozmieszczenia roślin naczyniowych w Polsce. 593 (Instytut Botaniki Uniwersytetu Jagiellońskiego, Kraków, 2001) [In Polish].Idžojtić, M., Kogelnik, M., Franjić, J. & Škvorc, Ž. Hosts and distribution of Viscum album L. ssp. album in Croatia and Slovenia. Plant Biosyst. 140, 50–55 (2006).Article 

Google Scholar 
Varga, I. et al. Changes in the Distribution of European Mistletoe (Viscum album) in Hungary During the Last Hundred Years. Folia Geobot 49, 559–577 (2014).Article 

Google Scholar 
Wild, J. et al. Plant distribution data for the Czech Republic integrated in the Pladias database. Preslia 91, 1–24 (2019).Article 

Google Scholar 
Krasylenko, Y. et al. The European mistletoe (Viscum album L.): Distribution, host range, biotic interactions, and management worldwide with special emphasis on Ukraine. Botany 98, 499–516 (2020).Article 

Google Scholar 
Karger, D. N. et al. Climatologies at high resolution for the Earth land surface areas. Sci. Data 4, 170122 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Karger D. N., et al. Data from: Climatologies at high resolution for the earth’s land surface areas. Dryad Digital Repository (2018).Gutjahr, O. et al. Max planck institute earth system model (MPI-ESM1. 2) for the high-resolution model intercomparison project (HighResMIP). Geosci. Model Dev. 12, 3241–3281 (2019).ADS 
Article 

Google Scholar 
Hijmans, R. J., & van Etten, J. raster: Geographic analysis and modeling with raster data. R package version 2.0-12. (2012).R Core Team. The Comprehensive R Archive Network. [Internet] url: https://cran.r-project.org/ Accessed 14 July 2022.Chakraborty, D., Móricz, N., Rasztovits, E., Dobor, L. & Schueler, S. Provisioning forest and conservation science with European tree species distribution models under climate change (Version v1). Zenodo https://doi.org/10.5281/zenodo.3686918 (2020).Wang, Z., Chang, Y. I., Ying, Z., Zhu, L. & Yang, Y. A parsimonious threshold-independent protein feature selection method through the area under receiver operating characteristic curve. Bioinformatics 23, 2788–2794 (2007).PubMed 
Article 

Google Scholar 
Lobo, J. M., Jiménez-Valverde, A. & Hortal, J. The uncertain nature of absences and their importance in species distribution modelling. Ecography 33, 103–114 (2010).Article 

Google Scholar 
QGIS Development Team. QGIS Geographic Information Sys-tem. Open Source Geospatial Foundation Project. [Internet]. url: https://www.qgis.org/en/site/. Accessed 14 July 2022.Fischer, J. T. Water relations of mistletoes and their hosts. In: The biology of mistletoes. (eds. Calder, M., & Bernhard, T.), 163–184 (Academic Press, Sydney, 1983).Skre, O. The regional distribution of vascular plants in Scandinavia with requirements for high summer temperatures. Norweg. J. Bot. 26, 295–318 (1979).
Google Scholar 
Wangerin, B. Loranthaceae. In: Lebensgeschichte der Blütenpflanzen Mitteleuropas (eds. Kirchner, O. V., Loew, E., & Schroeter, C.) 2, 953–1146 (E. Ulmer, Stuttgart, 1937).Rybalka, I. A. Relationship between density of the white mistletoe (Viscum album L.) and some landscape and environmental characteristics of urban areas in the case of Kharkiv. Ekologicheskiy Vestnik 1, 87–97 (2017).
Google Scholar 
Patykowski, J. & Kołodziejek, J. Comparative analysis of antioxidant activity in leaves of different hosts infected by mistletoe (Viscum album L. subsp. album). Arch. Biol. Sci. 65, 851–861 (2013).Article 

Google Scholar 
Skrypnik, L., Maslennikov, P., Feduraev, P., Pungin, A. & Belov, N. Ecological and landscape factors affecting the spread of European mistletoe (Viscum album L.) in urban areas (A Case Study of the Kaliningrad City, Russia). Plants 9, 394 (2020).PubMed Central 
Article 

Google Scholar 
Kunick, W. Veränderungen von Flora und Vegetation einer Grosstadt dargestellt am Beispiel von Berlin (West). PhD Thesis, Technische Universität (1974). [In German].Kołodziejek, J., Patykowski, J. & Kołodziejek, R. Distribution, frequency and host patterns of European mistletoe (Viscum album subsp. album) in the major city of Lodz Poland. Biol. 68, 55–64 (2013).
Google Scholar 
Caudullo, G., Welk, E. & San-Miguel-Ayanz, J. Chorological maps for the main European woody species. Data Brief 12, 662–666 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
O’Donnell, M. S. & Ignizio, D. A. Bioclimatic predictors for supporting ecological applications in the conterminous United States. US Geol. Surv. Data Ser. 691, 4–9 (2012).
Google Scholar 
Luther, P., Becker, H. & Leroi, R. Die Mistel: Botanik, Lektine, medizinische Anwendung. Springer (1987).Gazol, A. et al. Distinct effects of climate warming on populations of silver fir (Abies alba) across Europe. J. Biogeogr. 42, 1150–1162 (2015).Article 

Google Scholar 
Tikkanen, O. P. et al. Freezing tolerance of seeds can explain differences in the distribution of two widespread mistletoe subspecies in Europe. For. Ecol. Manag. 482, 118806 (2021).Article 

Google Scholar 
Pilichowski, S. et al. Wpływ Viscum album ssp. austriacum (Wiesb.) Vollm. na przyrost radialny Pinus sylvestris L. Sylwan 162, 452–459 (2018) ([In Polish]).
Google Scholar 
Szmidla, H., Tkaczyk, M., Plewa, R., Tarwacki, G. & Sierota, Z. Impact of common mistletoe (Viscum album L.) on scots pine forests—A call for action. Forests 10, 847 (2019).Article 

Google Scholar 
Wójcik, R. & Kędziora, W. Abundance of Viscum in central Poland: Results from a large-scale mistletoe inventory. Environ. Sci. Proc. 3, 98 (2020).
Google Scholar 
Sangüesa-Barreda, G., Linares, J. C. & Camarero, J. J. Drought and mistletoe reduce growth and water-use efficiency of Scots pine. For. Ecol. Manag. 296, 64–73 (2013).Article 

Google Scholar 
Mathiasen, R. L., Nickrent, D. L., Shaw, D. C. & Watson, D. M. Mistletoes: Pathology, systematics, ecology, and management. Plant Dis. 92, 988–1006 (2008).PubMed 
Article 

Google Scholar 
Catal, Y. & Carus, S. Effect of pine mistletoe on radial growth of crimean pine (Pinus nigra) in Turkey. J. Environ. Biol. 32, 263 (2011).PubMed 

Google Scholar 
Skre, O. High temperature demands for growth and development in Norway Spruce [Picea abies (L.) Karst.] in Scandinavia. Meld Nor Landbrukshøgsk 51, 1–29 (1971).
Google Scholar 
Utaaker, K. A temperature-growth index—the respiration equivalent—used in climatic studies on the meso-scale in Norway. Agric. Meteorol. 5, 351–359 (1968).Article 

Google Scholar 
Iversen, J. Viscum, Hedera and Ilex as climate indicators: A contribution to the study of the post-glacial temperature climate. Geol. fören. Stockh. förh. 66, 463–483 (1944).Article 

Google Scholar 
Briggs, J. Mistletoe, Viscum album (Santalaceae), in Britain and Ireland; a discussion and review of current status and trends. Brit. Ir. Bot. 3, 419–454 (2021).
Google Scholar  More

Barnosky, A. D. et al. Has the Earth’s sixth mass extinction already arrived? Nature 471, 51–57. https://doi.org/10.1038/nature09678 (2011).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Ceballos, G. et al. Accelerated human-induced species losses: Entering the sixth mass extinction. Sci. Adv. 1, 5. https://doi.org/10.1126/sciadv.1400253 (2015).Article 

Google Scholar 
Purvis, A. et al. IPBES global assessment on biodiversity and ecosystem services chapter 2.2 status and trends. Nature https://doi.org/10.5281/zenodo.5517457.svg (2019).Balvernara, P. et al. IPBES global assessment on biodiversity and ecosystem services chapter 2.2 status and trends. Drivers. Change https://doi.org/10.5281/zenodo.5517423 (2019).Carrol, C. & Noss, R. F. Rewilding in the face of climate change. Conserv. Biol. 35, 155–167. https://doi.org/10.1111/cobi.13531 (2020).Article 

Google Scholar 
Barr, S. L., Larson, B. M. H., Beechey, T. J. & Scott, D. J. Assessing climate change adaptation progress in Canada’s protected areas. Can. Geog. 65, 152–165. https://doi.org/10.1111/cag.12635 (2020).Article 

Google Scholar 
Convention on Biological Diversity. Aichi Target 11, Convention on Biological Diversity. https://www.cbd.int/aichi-targets/target/11. Accessed 14 May 2021.United Nations. Climate Change Pathways. https://unfccc.int/climate-action/marrakech-partnership/reporting-and-tracking/climate_action_pathways. Accessed 12 Sept 2022.Government of Canada. Canada’s nature legacy: Protecting our nature conservation/nature-legacy.html (2021).Coristine, L. E. et al. Informing Canada’s commitment to biodiversity conservation: A science-based framework to help guide protected areas designation through Target 1 and beyond. Facets 3, 531–562. https://doi.org/10.1139/facets-2017-0102 (2017).Article 

Google Scholar 
De Barros, A. E. et al. Identification of areas in Brazil that optimize areas that optimize conservation of forest carbon, Jaguars and Biodiversity. Conserv. Biol. 28, 580–593. https://doi.org/10.1111/cobi.12202 (2013).Article 
PubMed 

Google Scholar 
Jantz, P., Scott, S. & Laporte, N. Carbon stock corridors to mitigate climate change and promote biodiversity in the tropics. Nat. Clim. Change 4, 138–142. https://doi.org/10.1038/nclimate2105 (2014).ADS 
CAS 
Article 

Google Scholar 
Beaudrot, L. et al. Limited carbon and biodiversity co-benefits for tropical mammals and birds. Ecol. Appl. 26, 10998–11111. https://doi.org/10.1890/15-0935 (2016).Article 

Google Scholar 
Morelli, T. L. et al. Climate-change refugia: Biodiversity in a slow lane. Front. Ecol. Environ. 18, 228–234. https://doi.org/10.1002/fee.2189 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Stralberg, et al. Macrorefugia for North American trees ad songbirds: Climatic limiting factors and multi-scale topographic influences. Glob. Ecol. Biogeogr. 27, 690–703. https://doi.org/10.1111/geb.12731 (2018).Article 

Google Scholar 
Caroll, C. & Ray, J. C. Maximizing the effectiveness of national commitments to protected area expansion for conserving biodiversity and ecosystem carbon under climate change. Glob. Chang Biol. 27, 3395–3414. https://doi.org/10.1111/gcb.15645 (2020).Article 

Google Scholar 
Bradshaw, C. J., Warkentin, I. G. & Sodhi, N. S. Urgent preservation of boreal carbon stocks and biodiversity. Trends Ecol. Evol. 24, 541–548. https://doi.org/10.1016/j.tree.2009.03.019 (2009).Article 
PubMed 

Google Scholar 
Harris, L. I. et al. The essential carbon service provided by northern peatlands. Front. Ecol. Environ. 20, 222–230 (2022).Article 

Google Scholar 
Environment and Climate Change Canada. Canadian Environmental Sustainability Indicators: Canada’s conserved areas. environmental-indicators/conserved-areas.html (2020).Office of the Auditor General of Canada. Lessen learnt from 30 years of climate change challenges and opportunities. https://www.oag-bvg.gc.ca/internet/English/att__e_43948.html#hd3l (2020).Shea, T. et al. Canada’s Conservation Vision: A report of the National Advisory Panel. Government of Canada, 43 pp (2018).Environment and Climate Change Canada. Pan-Canadian Approach to transforming species at risk conservation in Canada. species-at-risk-conservation.html (2018).Bergerund, A. T. Caribou, wolves and man. Trends Ecol. Evol. 3, 68–72. https://doi.org/10.1016/0169-5347(88)90019-5 (1988).Article 

Google Scholar 
Vernier, L. A. et al. Effects of natural resource development on the terrestrial biodiversity of Canadian boreal forests. Environ. Rev. 22, 457–490. https://doi.org/10.1139/er-2013-0075 (2014).Article 

Google Scholar 
Wells, J. V., Dawson, N., Culver, N., Reid, F. A. & Slegers, S. M. The state of conservation in North America’s Borel Forest: Issues and opportunities. Front. For. Glob. Change 3, 90. https://doi.org/10.3389/ffgc.2020.00090/full (2020).Article 

Google Scholar 
COSEWIC. COSEWIC assessment and update status report on the woodland caribou Rangifer tarandus caribou in Canada. Committee on the Status of Endangered Wildlife in Canada. Ottawa. xi + 98 pp. (2002).COSEWIC. COSEWIC assessment and status report on the caribou Rangifer tarandus, Newfoundland population, Atlantic-Gaspésie population and Boreal population, in Canada. Committee on the Status of Endangered Wildlifein Canada. Ottawa. xxiii + 128 pp. (2014).Environment and Climate Change Canada. Amended Recovery Strategy for the Woodland Caribou (Rangifer tarandus caribou), Boreal Population, in Canada. Species at Risk Act Recovery Strategy Series. Environment and Climate Change Canada, Ottawa. xiii + 143pp. (2020).Environment and Climate Change Canada. Report on the Progress of Recovery Strategy Implementation for the Woodland Caribou (Rangifer tarandus caribou), Boreal population in Canada for the Period 2012–2017. Species at Risk Act Recovery Strategy Series. Environment and Climate Change Canada, Ottawa. ix + 94 (2017).Hebblewhite, M. Billion dollar boreal woodland caribou and the biodiversity impacts of the global oil and gas industry. Biol. Conserv. 206, 102–111. https://doi.org/10.1016/j.biocon.2016 (2017).Article 

Google Scholar 
Fortin, D., McLoughlin, P. D. & Hebblewhite, M. When the protection of a threatened species depends on the economy of a foreign nation. PLoS ONE 15, e0229555. https://doi.org/10.1371/journal.pone.0229555 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Drever, R. C. et al. Conservation through co-occurrence: Woodland caribou as a focal species for boreal biodiversity. Biol. Conserv. 232, 238–252. https://doi.org/10.1016/j.biocon.2019.01.026 (2019).Article 

Google Scholar 
Government of Canada. Pan-Canadian Framework on clean growth and climate change climatechange/pan-canadian-framework.html.Bradshaw, C. J. & Warkentin, I. G. Global estimates of boreal forest carbon stocks and flux. Glob. Planet Chang 128, 24–30. https://doi.org/10.1016/j.gloplacha.2015.02.004 (2015).ADS 
Article 

Google Scholar 
Jennings, M. D. Gap analysis: Concept, methods, recent results. Land Ecol. 5, 15–20 (2010).
Google Scholar 
Environment and Climate Change Canada. Canadian Protected and Conserved Areas database. national-wildlife-areas/protected-conserved-areas-database (2019).DeLuca, T. H. & Boisvenue, C. Boreal forest soil carbon: Distribution function and modelling. Forestry 85, 161–184. https://doi.org/10.1093/forestry/cps003 (2012).Article 

Google Scholar 
Price, et al. Anticipating the consequences of climate change for Canada’s boreal forest ecosystems. Environ. Rev. 21, 322–365. https://doi.org/10.1139/er-2013-0042 (2013).Article 

Google Scholar 
Southee, F. M., Edwards, B. A., Chetkiewicz, C. B. & O’Connor, C. M. Freshwater conservation planning in the far north of Ontario, Canada: Identifying priority watersheds for conservation of fish biodiversity in an intact boreal landscape. Facets 6, 90–117. https://doi.org/10.1139/facets-2020-0015 (2021).Article 

Google Scholar 
Mitchell, M. G. E. et al. Identifying key ecosystem service providing areas to inform national-scale conservation planning. Environ. Res. Lett. 16, 014038. https://doi.org/10.1088/1748-9326/abc121 (2021).ADS 
CAS 
Article 

Google Scholar 
Labadie, G. P. D., McLoughlin, M. H. & Fortin, D. Insect-mediated apparent competition between mammals in a boreal food web. Proc. Natl. Acad. Sci. U S A. 118, e2022892118. https://doi.org/10.1073/pnas.2022892118 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Cameron, V. & Hargreaves, A. L. Spatial distribution and conservation hotspots of mammals in Canada. Facets 5, 692–703. https://doi.org/10.1139/facets-2020-0018 (2020).Article 

Google Scholar 
Ceballos, G. & Ehrlich, P. R. Global mammal distributions, biodiversity hotspots, and conservation. PNAS 103, 19374–19379. https://doi.org/10.1073/pnas.0609334103 (2016).ADS 
Article 

Google Scholar 
Anielski, M. & Wilson, S. Counting Canada’s natural capital: Assessing the real value of Canada’s boreal ecosystems. Ottawa, On: Canadian Boreal Initiative and Pembina Institute counting-canadas-natural-capital (2009).Kumaraswamy, S. & Udyakumar, M. Biodiversity banking: A strategic conservation mechanism. Biodiver. Conserv. 20, 1155–1165. https://doi.org/10.1007/s10531-011-0020-5 (2011).Article 

Google Scholar 
Garnett, S. T. et al. A spatial overview of the global importance of Indigenous lands for conservation. Nat. Sustain. 1, 369–374. https://doi.org/10.1038/s41893-018-0100-6 (2018).Article 

Google Scholar 
Godden, L. & Cowell, S. Conservation planning and Indigenous governance in Australia’s Indigenous Protected Areas. Restor. Ecol. 24, 692–697. https://doi.org/10.1111/rec.12394 (2016).Article 

Google Scholar 
Greg Brown, B. & Fagerholm, N. Empirical PPGIS/PGIS mapping of ecosystem services: A review and evaluation. Ecol. Ser. 13, 119–133. https://doi.org/10.1016/j.ecoser.2014.10.007 (2021).Article 

Google Scholar 
Martin, A. E., Neave, E., Kirby, P., Drever, C. R. & Johnson, C. A. Multi-objective optimization can balance trade-offs among boreal caribou, biodiversity, and climate change objectives when conservation hotspots do not overlap. Sci. Rep. 12, 11895. https://doi.org/10.1038/s41598-022-15274-8 (2022).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
COSEWIC. Canadian Wildlife Species at Risk. Committee on the Status of Endangered Wildlife in Canada (2018).Alberta Environment and Parks and Alberta Conservation Association. Status of the Arctic Grayling (Thymallus arcticus) in Alberta: Update 2015. Alberta Environment and Parks. Alberta Wildlife Status Report No. 57 (Update 2015). Edmonton, AB. 96 pp. (2015).Environment and Climate Change Canada (ECCC). 2016. Range map extents, species at risk, Canada. Government of Canada. Open Government Dataset. https://open.canada.ca/data/en/dataset/d00f8e8c-40c4-435a-b790-980339ce3121.Magurran, A. E. Measuring Biological Diversity 256 (Blackwell Publishing, 2004).
Google Scholar 
Caissy, P., Klemet-N’Guessan, S., Jackiw, R., Eckert, C. G. & Hargreaves, A. L. High conservation priority of range-edge plant populations not matched by habitat protection or research effort. Biol. Conserv. 249, 108732 (2020).Article 

Google Scholar 
Gaston, K. J. Rarity 201 (Chapman & Hall, 1994).Book 

Google Scholar 
Stralberg, D. Velocity-based macrorefugia for North American ecoregions. Zenodo. https://doi.org/10.5281/zenodo.2579337 (2019).Fuss, S. et al. Betting on negative emissions. Nat. Clim. Change 4, 850–853. https://doi.org/10.1038/nclimate2392 (2014).ADS 
CAS 
Article 

Google Scholar 
Chen, I., Hill, J. K., Ohlemüller, R. D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science 333, 1024–1026. https://doi.org/10.1126/science.1206432 (2011).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Woodall, C. W. et al. An indicator of tree migration in forests of the eastern United States. For. Ecol. Manag. 257, 1434–1444 (2009).Article 

Google Scholar 
Iverson, L. R., Schwartz, M. W. & Prasad, A. M. How fast and far might tree species migrate in the eastern United States due to climate change? Glob. Ecol. Biogeogr. 13, 209–219 (2004).Article 

Google Scholar 
McLachlan, J. S., Hellmann, J. J. & Schwartz, M. W. A framework for debate of assisted migration in an era of climate change. Conserv. Biol. 21, 297–302 (2007).Article 

Google Scholar 
Sittaro, F., Paquette, A., Messier, C. & Nock, C. A. Tree range expansion in eastern North America fails to keep pace with climate warming at northern range limits. Glob. Change Biol. 23, 3292–3301. https://doi.org/10.1111/gcb.13622 (2017).ADS 
Article 

Google Scholar 
Ping, C. L. et al. Carbon stores and biogeochemical properties of soils under black spruce forest, Alaska. Soil Sci. Soc. Am. J. 74, 969–978. https://doi.org/10.2136/sssaj2009.0152 (2010).ADS 
CAS 
Article 

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

Google Scholar 
Chung, N. C., Miasojedow, B., Startek, M. & Gambin, A. Jaccard/Tanimoto similarity test and estimation methods for biological presence-absence data. BMC Bioinform. 29, 644. https://doi.org/10.1186/s12859-019-3118-5 (2019).Article 

Google Scholar 
Chung, N. C., Miasojedow, B., Startek, M. & Gambin A. Jaccard: Test Similarity Between Binary Data using Jaccard/Tanimoto Coefficients. R package version 0.1.0. https://CRAN.R-project.org/package=jaccard (2018). More

Bardgett, R. D. & van der Putten, W. H. Belowground biodiversity and ecosystem functioning. Nature 515, 505–511 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Guerra, C. A. et al. Tracking, targeting, and conserving soil biodiversity. Science 371, 239–241 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Wall, D. H. et al. (eds) Soil Ecology and Ecosystem Services (Oxford University Press, 2012).Jansson, J. K. & Hofmockel, K. S. Soil microbiomes and climate change. Nat. Rev. Microbiol. 18, 35–46 (2020).CAS 
PubMed 
Article 

Google Scholar 
de Vries, F. T. et al. Soil food web properties explain ecosystem services across European land use systems. Proc. Natl Acad. Sci. USA 110, 14296–14301 (2013).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Adhikari, K. & Hartemink, A. E. Linking soils to ecosystem services—a global review. Geoderma 262, 101–111 (2016).ADS 
CAS 
Article 

Google Scholar 
Pereira, P., Bogunovic, I., Muñoz-Rojas, M. & Brevik, E. C. Soil ecosystem services, sustainability, valuation and management. Curr. Opin. Environ. Sci. Health 5, 7–13 (2018).Article 

Google Scholar 
Wall, D. H., Nielsen, U. N. & Six, J. Soil biodiversity and human health. Nature 528, 69–76 (2015).Delgado-Baquerizo, M. et al. The proportion of soil-borne pathogens increases with warming at the global scale. Nat. Clim. Chang. 10, 550–554 (2020).ADS 
Article 

Google Scholar 
Rillig, M. C. et al. The role of multiple global change factors in driving soil functions and microbial biodiversity. Science 366, 886–890 (2019).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Guerra, C. A. et al. Global vulnerability of soil ecosystems to erosion. Landsc. Ecol. 35, 823–842 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Geisen, S., Wall, D. H. & van der Putten, W. H. Challenges and opportunities for soil biodiversity in the Anthropocene. Curr. Biol. 29, R1036–R1044 (2019).CAS 
PubMed 
Article 

Google Scholar 
Jung, M. et al. Areas of global importance for conserving terrestrial biodiversity, carbon and water. Nat. Ecol. Evol. 5, 1499–1509 (2021).PubMed 
Article 

Google Scholar 
Xu, H. et al. Ensuring effective implementation of the post-2020 global biodiversity targets. Nat. Ecol. Evol. 5, 411–418 (2021).PubMed 
Article 

Google Scholar 
Díaz, S. et al. (eds). Summary for Policymakers of the Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES, 2019); https://zenodo.org/record/3553579#.YyhIsXbMK70Phillips, H. R. P. et al. Global distribution of earthworm diversity. Science 366, 480–485 (2019).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
van den Hoogen, J. et al. Soil nematode abundance and functional group composition at a global scale. Nature 572, 194–198 (2019).ADS 
PubMed 
Article 

Google Scholar 
Delgado-baquerizo, M. et al. A global atlas of the dominant bacteria found in soil. Science 325, 320–325 (2018).ADS 
Article 

Google Scholar 
Tedersoo, L. et al. Global diversity and geography of soil fungi. Science 346, 1256688 (2014).PubMed 
Article 

Google Scholar 
Xu, X., Thornton, P. E. & Post, W. M. A global analysis of soil microbial biomass carbon, nitrogen and phosphorus in terrestrial ecosystems: global soil microbial biomass C, N and P. Glob. Ecol. Biogeogr. 22, 737–749 (2013).Article 

Google Scholar 
Djukic, I. et al. Early stage litter decomposition across biomes. Sci. Total Environ. 628–629, 1369–1394 (2018).Guerra, C. A. et al. Global projections of the soil microbiome in the Anthropocene. Glob. Ecol. Biogeogr. 30, 987–999 (2021).PubMed 
Article 

Google Scholar 
Cameron, E. K. et al. Global mismatches in aboveground and belowground biodiversity. Conserv. Biol. 33, 1187–1192 (2019).PubMed 
Article 

Google Scholar 
El Moujahid, L. et al. Effect of plant diversity on the diversity of soil organic compounds. PLoS One 12, e0170494 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Guerra, C. A. et al. Blind spots in global soil biodiversity and ecosystem function research. Nat. Commun. 11, 3870 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Fierer, N. & Jackson, R. B. The diversity and biogeography of soil bacterial communities. Proc. Natl Acad. Sci. USA 103, 626–631 (2006).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tedersoo, L. et al. Regional-scale in-depth analysis of soil fungal diversity reveals strong pH and plant species effects in Northern Europe. Front. Microbiol. 11, 1953 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Popp, A. et al. Land-use futures in the shared socio-economic pathways. Glob. Environ. Change 42, 331–345 (2017).Article 

Google Scholar 
Dornelas, M. et al. Assemblage time series reveal biodiversity change but not systematic loss. Science 344, 296–299 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Egoh, B., Reyers, B., Rouget, M., Bode, M. & Richardson, D. M. Spatial congruence between biodiversity and ecosystem services in South Africa. Biol. Conserv. 142, 553–562 (2009).Article 

Google Scholar 
Jürgens, N. et al. The BIOTA Biodiversity Observatories in Africa—a standardized framework for large-scale environmental monitoring. Environ. Monit. Assess. 184, 655–678 (2012).PubMed 
Article 

Google Scholar 
Wyborn, C. & Evans, M. C. Conservation needs to break free from global priority mapping. Nat. Ecol. Evol. 5, 1322–1324 (2021).PubMed 
Article 

Google Scholar 
Hautier, Y. et al. Local loss and spatial homogenization of plant diversity reduce ecosystem multifunctionality. Nat. Ecol. Evol. 2, 50–56 (2018).PubMed 
Article 

Google Scholar 
Zhou, Z., Wang, C. & Luo, Y. Meta-analysis of the impacts of global change factors on soil microbial diversity and functionality. Nat. Commun. 11, 3072 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Eisenhauer, N., Schulz, W., Scheu, S. & Jousset, A. Niche dimensionality links biodiversity and invasibility of microbial communities. Funct. Ecol. 27, 282–288 (2013).Article 

Google Scholar 
Wagg, C., Bender, S. F., Widmer, F. & van der Heijden, M. G. A. Soil biodiversity and soil community composition determine ecosystem multifunctionality. Proc. Natl Acad. Sci. USA 111, 5266–5270 (2014).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Haines-Young, R. H. & Potschin, M. B. in Ecosystems Ecology: A New Synthesis (eds Raffaelli, D. G. & Frid, C. L. J.) Ch. 6 (2012).Smith, L. C. et al. Large‐scale drivers of relationships between soil microbial properties and organic carbon across Europe. Glob. Ecol. Biogeogr. 30, 2070–2083 (2021).Article 

Google Scholar 
Keesstra, S. et al. The superior effect of nature based solutions in land management for enhancing ecosystem services. Sci. Total Environ. 610-611, 997–1009 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Le Provost, G. et al. Contrasting responses of above- and belowground diversity to multiple components of land-use intensity. Nat. Commun. 12, 3918 (2021).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tanneberger, F. et al. The power of nature‐based solutions: how peatlands can help us to achieve key EU sustainability objectives. Adv. Sustain. Syst. 5, 2000146 (2021).CAS 
Article 

Google Scholar 
Johnston, A. et al. Observed and predicted effects of climate change on species abundance in protected areas. Nat. Clim. Chang. 3, 1055–1061 (2013).ADS 
Article 

Google Scholar 
Hannah, L. et al. Protected area needs in a changing climate. Front. Ecol. Environ. 5, 131–138 (2007).Article 

Google Scholar 
Gallardo, B. et al. Protected areas offer refuge from invasive species spreading under climate change. Glob. Chang. Biol. 23, 5331–5343 (2017).ADS 
PubMed 
Article 

Google Scholar 
O’Neill, B. C. et al. The roads ahead: narratives for shared socioeconomic pathways describing world futures in the 21st century. Glob. Environ. Change 42, 169–180 (2017).Article 

Google Scholar 
Fedele, G., Donatti, C. I., Bornacelly, I. & Hole, D. G. Nature-dependent people: mapping human direct use of nature for basic needs across the tropics. Glob. Environ. Change 71, 102368 (2021).Visconti, P. et al. Protected area targets post-2020. Science 364, 239–241 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Allan, J. R. et al. The minimum land area requiring conservation attention to safeguard biodiversity. Science 376, 1094–1101 (2022).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Maestre, F. T. et al. Plant species richness and ecosystem multifunctionality in global drylands. Science 335, 214–218 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Delgado-Baquerizo, M. et al. Changes in belowground biodiversity during ecosystem development. Proc. Natl Acad. Sci. USA. 116, 6891–6896 (2019).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mace, G. M. Whose conservation? Science 345, 1558–1560 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Amaral-Zettler, L. A., McCliment, E. A., Ducklow, H. W. & Huse, S. M. A method for studying protistan diversity using massively parallel sequencing of V9 hypervariable regions of small-subunit ribosomal RNA genes. PLoS One 4, e6372 (2009).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Stoeck, T. et al. Multiple marker parallel tag environmental DNA sequencing reveals a highly complex eukaryotic community in marine anoxic water. Mol. Ecol. 19, 21–31 (2010).CAS 
PubMed 
Article 

Google Scholar 
Ramirez, K. S. et al. Biogeographic patterns in below-ground diversity in New York City’s Central Park are similar to those observed globally. Proc. Biol. Sci. 281, 20141988 (2014).PubMed 
PubMed Central 

Google Scholar 
Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).CAS 
PubMed 
Article 

Google Scholar 
Edgar, R. C. & Flyvbjerg, H. Error filtering, pair assembly and error correction for next-generation sequencing reads. Bioinformatics 31, 3476–3482 (2015).CAS 
PubMed 
Article 

Google Scholar 
Edgar, R. C. UNOISE2: improved error-correction for Illumina 16S and ITS amplicon sequencing. Preprint at bioRxiv https://doi.org/10.1101/081257 (2016).Tedersoo, L. et al. Towards understanding diversity, endemicity and global change vulnerability of soil fungi. Preprint at bioRxiv https://doi.org/10.1101/2022.03.17.484796 (2022).Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).CAS 
PubMed 
Article 

Google Scholar 
Delgado-Baquerizo, M. et al. Global homogenization of the structure and function in the soil microbiome of urban greenspaces. Sci. Adv. 7, eabg5809 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Phillips, H. R. P., Heintz-Buschart, A. & Eisenhauer, N. Putting soil invertebrate diversity on the map. Mol. Ecol. 29, 655–657 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Xiong, W. et al. A global overview of the trophic structure within microbiomes across ecosystems. Environ. Int. 151, 106438 (2021).PubMed 
Article 

Google Scholar 
Drummond, A. J. et al. Evaluating a multigene environmental DNA approach for biodiversity assessment. Gigascience 4, 46 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Oliverio, A. M., Gan, H., Wickings, K. & Fierer, N. A DNA metabarcoding approach to characterize soil arthropod communities. Soil Biol. Biochem. 125, 37–43 (2018).CAS 
Article 

Google Scholar 
Horton, D. J., Kershner, M. W. & Blackwood, C. B. Suitability of PCR primers for characterizing invertebrate communities from soil and leaf litter targeting metazoan 18S ribosomal or cytochrome oxidase I (COI) genes. Eur. J. Soil Biol. 80, 43–48 (2017).CAS 
Article 

Google Scholar 
Delgado-Baquerizo, M. et al. Multiple elements of soil biodiversity drive ecosystem functions across biomes. Nat. Ecol. Evol. 4, 210–220 (2020).PubMed 
Article 

Google Scholar 
Carter, M. R. & Gregorich, E. G. (eds) Soil Sampling and Methods of Analysis (CRC Press, 2007).Sparks, D. L. et al. (eds) Methods of Soil Analysis, Part 3: Chemical Methods (Wiley, 2020).Nguyen, N. H. et al. FUNGuild: an open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol. 20, 241–248 (2016).Article 

Google Scholar 
Bell, C. W. et al. High-throughput fluorometric measurement of potential soil extracellular enzyme activities. J. Vis. Exp. 81, e50961 (2013).Wang, L. et al. Diversifying livestock promotes multidiversity and multifunctionality in managed grasslands. Proc. Natl Acad. Sci. USA. 116, 6187–6192 (2019).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Durán, J., Delgado-Baquerizo, M., Rodríguez, A., Covelo, F. & Gallardo, A. Ionic exchange membranes (IEMs): a good indicator of soil inorganic N production. Soil Biol. Biochem. 57, 964–968 (2013).Article 

Google Scholar 
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).MATH 
Article 

Google Scholar 
Friedman, J. H. Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001).MathSciNet 
MATH 
Article 

Google Scholar 
Sharma, N. XGBoost. The Extreme Gradient Boosting for Mining Applications (GRIN Verlag, 2018).Chen, T. & Guestrin, C. XGBoost: a scalable tree boosting system. In Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 785–794 (Association for Computing Machinery, 2016).Wilson. ParBayesianOptimization: Parallel Bayesian Optimization of Hyperparameters. R version 1 https://CRAN.R-project.org/package=ParBayesianOptimization (2021).Hastie, T., Friedman, J. & Tibshirani, R. The Elements of Statistical Learning (Springer, 2001).Jackson, D. A. & Chen, Y. Robust principal component analysis and outlier detection with ecological data. Environmetrics 15, 129–139 (2004).Article 

Google Scholar 
Breiman, L. Bagging predictors. Mach. Learn. 24, 123–140 (1996).MATH 
Article 

Google Scholar 
Breiman, L., Friedman, J., Stone, C. J. & Olshen, R. A. Classification and Regression Trees (Routledge, 1984).Ord, J. K. & Getis, A. Local spatial autocorrelation statistics: distributional issues and an application. Geogr. Anal. 27, 286–306 (2010).Article 

Google Scholar 
Getis, A. & Ord, J. K. The analysis of spatial association by use of distance statistics. Geogr. Anal. 24, 189–206 (2010).Article 

Google Scholar 
Prasannakumar, V., Vijith, H., Charutha, R. & Geetha, N. Spatio-temporal clustering of road accidents: GIS based analysis and assessment. Procedia Soc. Behav. Sci. 21, 317–325 (2011).Article 

Google Scholar 
Lin, G. Comparing spatial clustering tests based on rare to common spatial events. Comput. Environ. Urban Syst. 28, 691–699 (2004).Article 

Google Scholar 
Araújo, M. B. et al. Standards for distribution models in biodiversity assessments. Sci. Adv. 5, eaat4858 (2019).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rousseeuw, P. J. & van Zomeren, B. C. Unmasking multivariate outliers and leverage points. J. Am. Stat. Assoc. 85, 633–639 (1990).Article 

Google Scholar 
Hempel, S., Frieler, K., Warszawski, L., Schewe, J. & Piontek, F. A trend-preserving bias correction—the ISI-MIP approach. Earth Syst. Dyn. 4, 219–236 (2013).ADS 
Article 

Google Scholar 
Lawrence, D. M. et al. The Land Use Model Intercomparison Project (LUMIP) contribution to CMIP6: rationale and experimental design. Geosci. Model Dev. 9, 2973–2998 (2016).ADS 
Article 

Google Scholar 
Kim, H. et al. A protocol for an intercomparison of biodiversity and ecosystem services models using harmonized land-use and climate scenarios. Geosci. Model Dev. 11, 4537–4562 (2018).Dufresne, J.-L. et al. Climate change projections using the IPSL-CM5 Earth System Model: from CMIP3 to CMIP5. Clim. Dyn. 40, 2123–2165 (2013).Article 

Google Scholar 
Hurtt, G. C. et al. Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. Clim. Change 109, 117 (2011).ADS 
Article 

Google Scholar 
Hurtt, G. C. et al. Harmonization of global land use change and management for the period 850–2100 (LUH2) for CMIP6. Geosci. Model Dev. 13, 5425–5464 (2020).ADS 
CAS 
Article 

Google Scholar 
Riahi, K. et al. The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Glob. Environ. Change 42, 153–168 (2017).Article 

Google Scholar 
O’Neill, B. C. et al. A new scenario framework for climate change research: the concept of shared socioeconomic pathways. Clim. Change 122, 387–400 (2014).ADS 
Article 

Google Scholar 
Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Powers, R. P. & Jetz, W. Global habitat loss and extinction risk of terrestrial vertebrates under future land-use-change scenarios. Nat. Clim. Chang. 9, 323–329 (2019).ADS 
Article 

Google Scholar  More

Johnson, N. et al. (eds) Global Soil Biodiversity Atlas (EU, 2016).
Google Scholar 
FAO et al. State of Knowledge of Soil Biodiversity — Status, Challenges and Potentialities (FAO, 2020).
Google Scholar 
Cameron, E. K. et al. Nature Ecol. Evol. 2, 1042–1043 (2018).PubMed 
Article 

Google Scholar 
van den Hoogen, J. et al. Nature 572, 194–198 (2019).PubMed 
Article 

Google Scholar 
Phillips, H. R. P. et al. Science 366, 480–485 (2019).PubMed 
Article 

Google Scholar 
Guerra, C. A. et al. Nature https://doi.org/10.1038/s41586-022-05292-x (2022).Article 

Google Scholar 
Moore, J. C. & de Ruiter, P. C. Energetic Food Webs: An Analysis of Real and Model Ecosystems (Oxford Univ. Press, 2012).
Google Scholar 
Wolters V. et al. Bioscience 50, 1089–1098 (2000).Article 

Google Scholar 
Schimel, J. P. & Schaeffer, S. M. Front. Microbiol. 3, 348 (2012).PubMed 
Article 

Google Scholar 
IPCC. In Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change Impacts, Adaptation, and Vulnerability: Summary for Policymakers (eds Shukla, P. R. et al.) 50 (Cambridge Univ. Press, 2022).
Google Scholar 
Chenu, C. et al. Soil Till. Res. 188, 41–52 (2019).Article 

Google Scholar 
Liang, C., Schimel, J. P. & Jastrow, J. D. Nature Microbiol. 2, 17105 (2017).PubMed 
Article 

Google Scholar 
Hannula, S. E. & Morriën, E. Geoderma 413, 115767 (2022).Article 

Google Scholar  More

Keith, D. A. et al. Nature https://doi.org/10.1038/s41586-022-05318-4 (2022).Article 

Google Scholar 
Domesday Book, or, The Great Survey of England of William the Conqueror A.D. MLXXXVI (Ordnance Survey Office, 1862).McMahon, G. et al. Environ. Manage. 28, 293–316 (2001).PubMed 
Article 

Google Scholar 
Spalding, M. D. et al. BioScience 57, 573–583 (2007).Article 

Google Scholar 
Holdridge, L. R. Science 105, 367–368 (1947).PubMed 
Article 

Google Scholar 
Köppen, W. in Handbuch der Klimatologie (eds Köppen, W. & Geiger, G. C.) 1–44 (Gebrüder Borntraeger, 1936).
Google Scholar 
Whittaker, R. H. Communities and Ecosystems (Macmillan, 1975).
Google Scholar 
Keddy, P. A. Trends Ecol. Evol. 9, 231–234 (1994).PubMed 
Article 

Google Scholar 
United Nations. Convention on Biological Diversity (UN, 1992).
Google Scholar 
MacArthur, R. H. Geographical Ecology: Patterns in the Distribution of Species (Princeton Univ. Press, 1972).
Google Scholar 
Schoener, T. W. in Community Ecology (eds Diamond, J. D. & Case, T. ) 467–479 (Harper & Row, 1986).
Google Scholar 
Winemiller, K. O., Fitzgerald, D. B., Bower, L. M. & Pianka, E. R. Ecol. Lett. 18, 737–751 (2015).PubMed 
Article 

Google Scholar  More