Ecology

Seasonality of human–bear conflictOn the Deccan Plateau and Gujarat, most sloth bear attacks occurred in winter, which differs significantly from the seasonality of attacks reported by other studies. Unlike other study areas, people on the Deccan Plateau and in Gujarat are more active in the forest in winter when monsoons and crop harvests have ended. The higher incidence of attacks during monsoons in central India correlates with the increased presence of people farming and protecting crops from cattle depredation, as well as from bears and other wildlife species grazing in nearby forested areas5, 16,17,18. The Kanha–Pench Corridor study was the only one which documented an increase in sloth bear attacks during summer. This increase is concurrent with an increase of people in the forest that collect mahua flower (Madhuca spp) and tendu leaf (Diospyros spp)19. In Sri Lanka, most attacks occurred in the dry season, coincident with the highest levels of human activity in forested areas. People in Sri Lanka enter forests for alternative sources of income as agriculture activity declines during the dry season4.Across all studies, the majority of sloth bear attacks are correlated with the time of year when human activity is greatest in bear habitat. However, the time of year that the peak of human activity occurs in sloth bear habitat varies by region. We conclude that the seasonal activity of bears plays a much smaller role on attack rates than the seasonal activity of humans. Consistent with findings in other studies, human incursion into bear habitat is the primary factor responsible for precipitating conflict21.Time of day influences on human–bear conflictMost studies attributed the time of day that attacks occurred to when most humans were active in the forest4, 17,18,19,20. However, the Deccan plateau differed in that the majority of attacks occurred after dark when fewer people were active in or near the forest. Working in agricultural areas after dark is a more common practice on the Deccan Plateau than for the other study areas due to the availability of electricity and artificial lighting, though even with artificial lighting human activity after dark on the Deccan Plateau is still substantially less than during daytime. While a contributing factor, we do not feel that the increase in nighttime activity on the Deccan Plateau fully explains the significant increase in attacks during that time period as compared to other areas. We suspect that sloth bear activity patterns on the Deccan Plateau, and how bears use their environment, accounts for the shift in attack timing.Sloth bears, though potentially active throughout the day, are predominately crepuscular and nocturnal17, 22,23,24. During daytime, sloth bears seek shelter in naturally occurring caves, crevices between big boulders, the spaces between tree roots, beneath fallen trees, or under bushes1, 25,26,27,28. On the Deccan Plateau, however, sloth bears utilize rocky caves almost exclusively for daytime denning29. A cave reduces chance encounters with people and predators while providing a modicum of security, hence the lower incident rate for areas with naturally occurring caves.Conversely, studies conducted in Sri Lanka, Maharashtra and the Kanha-Pench corridor documented more attacks during daytime when people are more active but sloth bears are less active4, 5, 19. Large areas where sloth bears are located in Sri Lanka do not have caves for resting, though they do have dense vegetation and tree cavities (S. Ratnayeke, personal communication July 28, 2020). The Dnyanganga Wildlife Sanctuary, in the state of Maharastra, is mostly lower plains forest without rocky caves (N. Dharaiya, personal communication June 25, 2020). The Kanha-Pench corridor landscape is largely comprised of sal (Shorea spp) and teak (Tectona spp) forests largely devoid of caves30. The role of caves in minimizing daylight sloth bear attacks may be best exemplified by an attack in Sri Lanka as quoted in Ratnayeke et al.4:
“I was following two of my companions and saw a black form lying at the foot of a clump bushes, about 10 m from me. I called out to my companions. Before I knew it, the impact of the charging bear knocked me off my feet. It happened so fast, I didn’t see the bear coming… just dust, flying leaves, and the screams and roars of the bear.”
Had this bear been in a cave rather than the shade of a bush, it likely would not have felt threatened and reacted defensively. We speculate that during daylight on the Deccan Plateau, sloth bears rest securely within a cave and are not threatened by humans passing nearby. We know that farmers and livestock herders work in relatively close proximity to known den locations without fear of being attacked (S. Shanmugavelu, pers. observation). Clearly, caves afford a level of protection and separation that benefits both bears and humans. Consequently, we suggest this is the most likely explanation as to why there are relatively few attacks on the Deccan Plateau during daytime.Season and sloth bear safety messagingBear attack research and safety messaging often recognizes a seasonal component17,18,19,20, 31 (e.g., more sloth bear attacks occur during the monsoon season than during other seasons). Sloth bears are active year-round, and the rate of attacks is strongly correlated with the level of human activity in the forest. Similarly, in Alaska, Smith and Herrero32 reported that human-brown bear conflicts were strongly seasonal in their occurrence. Additionally, they reported that attacks occurred most often when both people and bears vied for the same resource, such as salmon or ungulates. Farther north, human-polar bear conflict peaks when bears are on land awaiting freeze up in the fall33. Not infrequently, sloth bear safety messaging amounts to little more than general statements such as “when in the forest or in sloth bear country be aware”. In other words, an individual’s odds of being attacked by a sloth bear while in the woods may not significantly vary regardless of season. But, where it has been found to vary by season, this information should be conveyed to the public.Time of day and sloth bear safety messagingSloth bear research and safety messaging often reports and warns of the “most dangerous” time or times of the day to be active in the forest17,18,19,20, 31, 34. Sloth bear attacks, like grizzly bear or American black bear attacks33, can occur anytime, day or night6. However, due to an abundance of naturally occurring caves on the Deccan Plateau, stumbling across a sleeping sloth bear mid-day is much less likely to occur than it is in Sri Lanka or in the Kanha-Pench corridor. Therefore, regional sloth bear safety messaging should acknowledge this significant difference which will promote bear safety.The Corbett Foundation31 and Dharaiya et al.34 do an admirable job of focusing their safety messaging to a specific regional group of people in their respective publications. This type of regional messaging is necessary for optimizing sloth bear safety messaging efficacy. However, there is also value to non-site-specific sloth bear safety messaging. The short film “Living with Sloth Bears”35 intentionally addresses general safety messaging that applies to sloth bears across their entire range. Consequently, in the making of this film, we purposely avoided referring to the timing of attacks, seasons or time of day, or other aspects of human-bear conflict because we were aware of significant differences with respect to these variables between locations.Yet another aspect of bear safety messaging is to keep it simple so that a person, under duress, will remember what to do in the event of a bear encounter Attempting to recall the details of an extended message, especially when being threatened by a bear, can be difficult, if not impossible. Therefore, the trend has been to keep bear messaging as simple as possible and we agree with it. However, teaching people that work in bear habitat the most likely times of day encounters occur can be beneficial. In summary, there is a time and place to provide detailed information that is regionally specific, and other situations in which to keep messaging simple.Sloth bear denning ecology on the Deccan Plateau and its role in human–bear conflictThe Deccan Plateau is known as high quality sloth bear habitat, as evidenced by the relatively high density of bears in this area (S. Shanmugavelu, pers. observation). While there is ample food on the Deccan Plateau, the abundance of caves there sets it apart from other areas within the specie’s range. Sloth bears use only caves or cave-like structures on the Deccan Plateau for resting (Shanmugavelu et al. In Print). Caves provide protection from the elements, such as the heat of the day or severe storms, as well as protection from potential predators. Sloth bears do not have many predators and while a cub or very young bear may be at risk from leopards (Panthera pardus) or wolves (Canis lupes pallipes), the only natural predator of adult sloth bears is the Bengal tiger (Panthera tigris tigris). Tiger scat studies revealed that sloth bears can comprise up to 2% of a their diet36,37,38,39. Tigers no longer occur on the Deccan Plateau, but the abundance of caves in the area undoubtedly historically benefited sloth bears, perhaps facilitating a higher density than would have been otherwise attainable. Presently, however, an increase in human population and habitat loss represents greater threat to the species. More

Hoegh-Guldberg, O. & Bruno, J. F. The impact of climate change on the world’s marine ecosystems. Science 328, 1523–1528 (2010).ADS 
CAS 
PubMed 

Google Scholar 
Rahmstorf, S. & Coumou, D. Increase of extreme events in a warming world. Proc. Natl. Acad. Sci. USA 108, 17905–17909 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Toseland, A. et al. The impact of temperature on marine phytoplankton resource allocation and metabolism. Nat. Clim. Change 3, 979–984 (2013).ADS 
CAS 

Google Scholar 
Doney, S. C. Plankton in a warmer world. Nature 444, 695–696 (2006).ADS 
CAS 
PubMed 

Google Scholar 
Harley, C. D. G. et al. The impacts of climate change in coastal marine systems: Climate change in coastal marine systems. Ecol. Lett. 9, 228–241 (2006).ADS 
PubMed 

Google Scholar 
Vitousek, P. M., Mooney, H. A., Lubchenco, J. & Melillo, J. M. Human domination of Earth’s ecosystems. Science 277, 494–499 (1997).CAS 

Google Scholar 
Zingone, A., Phlips, E. J. & Harrison, P. J. Multiscale variability of twenty-two coastal phytoplankton time series: A global scale comparison. Estuaries Coasts 33, 224–229 (2010).CAS 

Google Scholar 
Cloern, J. E. et al. Human activities and climate variability drive fast-paced change across the world’s estuarine-coastal ecosystems. Glob. Change Biol. 22, 513–529 (2016).ADS 

Google Scholar 
Cloern, J. E. & Jassby, A. D. Patterns and scales of phytoplankton variability in estuarine-coastal ecosystems. Estuaries Coasts 33, 230–241 (2010).CAS 

Google Scholar 
Romagnan, J.-B. et al. Comprehensive model of annual plankton succession based on the whole-plankton time series approach. PLoS ONE 10, e0119219 (2015).PubMed 
PubMed Central 

Google Scholar 
Guadayol, Ò. et al. Responses of coastal osmotrophic planktonic communities to simulated events of turbulence and nutrient load throughout a year. J. Plankton Res. 31, 583–600 (2009).CAS 

Google Scholar 
Totti, C. et al. Phytoplankton communities in the northwestern Adriatic Sea: Interdecadal variability over a 30-years period (1988–2016) and relationships with meteoclimatic drivers. J. Mar. Syst. 193, 137–153 (2019).
Google Scholar 
Zingone, A. et al. Coastal phytoplankton do not rest in winter. Estuaries Coasts 33, 342–361 (2010).CAS 

Google Scholar 
Widdicombe, C. E., Eloire, D., Harbour, D., Harris, R. P. & Somerfield, P. J. Long-term phytoplankton community dynamics in the Western English Channel. J. Plankton Res. 32, 643–655 (2010).
Google Scholar 
Harding, L. W. et al. Variable climatic conditions dominate recent phytoplankton dynamics in Chesapeake Bay. Sci. Rep. 6, 1–16 (2016).
Google Scholar 
Suikkanen, S., Laamanen, M. & Huttunen, M. Long-term changes in summer phytoplankton communities of the open northern Baltic Sea. Estuar. Coast. Shelf Sci. 71, 580–592 (2007).ADS 

Google Scholar 
Wasmund, N., Tuimala, J., Suikkanen, S., Vandepitte, L. & Kraberg, A. Long-term trends in phytoplankton composition in the western and central Baltic Sea. J. Mar. Syst. 87, 145–159 (2011).
Google Scholar 
Cloern, J. E. Turbidity as a control on phytoplankton biomass and productivity in estuaries. Cont. Shelf Res. 7, 1367–1381 (1987).ADS 

Google Scholar 
Barbosa, A. B., Domingues, R. B. & Galvão, H. M. Environmental forcing of phytoplankton in a Mediterranean estuary (Guadiana Estuary, South-western Iberia): A decadal study of anthropogenic and climatic influences. Estuaries Coasts 33, 324–341 (2010).CAS 

Google Scholar 
Barrera-Alba, J. J., Abreu, P. C. & Tenenbaum, D. R. Seasonal and inter-annual variability in phytoplankton over a 22-year period in a tropical coastal region in the southwestern Atlantic Ocean. Cont. Shelf Res. 176, 51–63 (2019).ADS 

Google Scholar 
Brito, A. C. et al. Changes in the phytoplankton composition in a temperate estuarine system (1960 to 2010). Estuaries Coasts 38, 1678–1691 (2015).CAS 

Google Scholar 
Zingone, A. et al. Increasing the quality, comparability and accessibility of phytoplankton species composition time-series data. Estuar. Coast. Shelf Sci. 162, 151–160 (2015).ADS 

Google Scholar 
Smayda, T. J. Phytoplankton species succession. In The Physiological Ecology of Phytoplankton 493–570 (Blackwell Scientific Publications, 1980).
Google Scholar 
Kremer, C. T. & Klausmeier, C. A. Species packing in eco-evolutionary models of seasonally fluctuating environments. Ecol. Lett. 20, 1158–1168 (2017).PubMed 

Google Scholar 
Sakavara, A., Tsirtsis, G., Roelke, D. L., Mancy, R. & Spatharis, S. Lumpy species coexistence arises robustly in fluctuating resource environments. Proc. Natl. Acad. Sci. USA 115, 738–743 (2018).CAS 
PubMed 

Google Scholar 
Wiltshire, K. H. et al. Resilience of North Sea phytoplankton spring bloom dynamics: An analysis of long-term data at Helgoland Roads. Limnol. Oceanogr. 53, 1294–1302 (2008).ADS 

Google Scholar 
Tsakalakis, I., Pahlow, M., Oschlies, A., Blasius, B. & Ryabov, A. B. Diel light cycle as a key factor for modelling phytoplankton biogeography and diversity. Ecol. Model. 384, 241–248 (2018).
Google Scholar 
Platt, T., Fuentes-Yaco, C. & Frank, K. T. Spring algal bloom and larval fish survival. Nature 423, 398–399 (2003).ADS 
CAS 
PubMed 

Google Scholar 
Edwards, M. & Richardson, A. J. Impact of climate change on marine pelagic phenology and trophic mismatch. Nature 430, 881–884 (2004).ADS 
CAS 
PubMed 

Google Scholar 
Vantrepotte, V. & Melin, F. Temporal variability of 10-year global SeaWiFS time-series of phytoplankton chlorophyll a concentration. ICES J. Mar. Sci. 66, 1547–1556 (2009).
Google Scholar 
McQuatters-Gollop, A. et al. From microscope to management: The critical value of plankton taxonomy to marine policy and biodiversity conservation. Mar. Policy 83, 1–10 (2017).
Google Scholar 
Edwards, K. F., Litchman, E. & Klausmeier, C. A. Functional traits explain phytoplankton community structure and seasonal dynamics in a marine ecosystem. Ecol. Lett. 16, 56–63 (2013).PubMed 

Google Scholar 
Wentzky, V. C., Tittel, J., Jäger, C. G., Bruggeman, J. & Rinke, K. Seasonal succession of functional traits in phytoplankton communities and their interaction with trophic state. J. Ecol. 108, 1649–1663 (2020).CAS 

Google Scholar 
Karl, D. M. Oceanic ecosystem time-series programs: Ten lessons learned. Oceanography 23, 104–125 (2010).
Google Scholar 
d’Alcalà, M. R. et al. Seasonal patterns in plankton communities in a pluriannual time series at a coastal Mediterranean site (Gulf of Naples): An attempt to discern recurrences and trends. Sci. Mar. 68, 65–83 (2004).
Google Scholar 
Mazzocchi, M. G., Dubroca, L., García-Comas, C., Capua, I. D. & Ribera d’Alcalà, M. Stability and resilience in coastal copepod assemblages: The case of the Mediterranean long-term ecological research at Station MC (LTER-MC). Prog. Oceanogr. 97–100, 135–151 (2012).ADS 

Google Scholar 
Thioulouse, J., Simier, M. & Chessel, D. Simultaneous analysis of a sequence of paired ecological tables. Ecology 85, 272–283 (2004).
Google Scholar 
Lindeman, R. H., Merenda, P. F. & Gold, R. Z. Introduction to bivariate and multivariate analysis 119 (Scott Foresman Co, 1980).MATH 

Google Scholar 
Longobardi, L. From Data to Knowledge: Integrating Observational Data to Trace Phytoplankton Dynamics in a Changing World (Open Univ, 2021).
Google Scholar 
Pisano, A. et al. New evidence of mediterranean climate change and variability from sea surface temperature observations. Remote Sens. 12, 132 (2020).ADS 

Google Scholar 
Zingone, A. et al. Time series and beyond: multifaceted plankton research at a marine Mediterranean LTER site. Nat. Conserv. 34, 273–310 (2019).
Google Scholar 
Zingone, A., Licandro, P. & Sarno, D. Revising paradigms and myths of phytoplankton ecology using biological time series. In Mediterranean Biological Time Series. CIESM Workshop Monographs 109–114 (2003).Cianelli, D. et al. Disentangling physical and biological drivers of phytoplankton dynamics in a coastal system. Sci. Rep. 7, 1–15 (2017).CAS 

Google Scholar 
Zingone, A., Casotti, R., d’Alcalà, M. R., Scardi, M. & Marino, D. ‘St Martin’s Summer’: The case of an autumn phytoplankton bloom in the Gulf of Naples (Mediterranean Sea). J. Plankton Res. 17, 575–593 (1995).
Google Scholar 
Margalef, R. Life-forms of phytoplankton as survival alternatives in an unstable environment. Oceanol. Acta 1, 493–509 (1978).
Google Scholar 
Sommer, U. et al. Beyond the plankton ecology group (PEG) model: Mechanisms driving plankton succession. Annu. Rev. Ecol. Evol. Syst. 43, 429–448 (2012).
Google Scholar 
Reynolds, C. S. What factors influence the species composition of phytoplankton in lakes of different trophic status? In Phytoplankton and Trophic Gradients (eds Alvarez-Cobelas, M. et al.) 11–26 (Springer, 1998).
Google Scholar 
Zingone, A., Montresor, M. & Marino, D. Summer phytoplankton physiognomy in coastal waters of the Gulf of Naples. Mar. Ecol. 11, 157–172 (1990).ADS 

Google Scholar 
Harding, L. W. et al. Long-term trends of nutrients and phytoplankton in Chesapeake Bay. Estuaries Coasts 39, 664–681 (2016).CAS 

Google Scholar 
Andersen, J. H. et al. Long-term temporal and spatial trends in eutrophication status of the Baltic Sea. Biol. Rev. 92, 135–149 (2017).PubMed 

Google Scholar 
Giner, C. R. et al. Quantifying long-term recurrence in planktonic microbial eukaryotes. Mol. Ecol. https://doi.org/10.1111/mec.14929 (2019).Article 
PubMed 

Google Scholar 
Ward, C. S. et al. Annual community patterns are driven by seasonal switching between closely related marine bacteria. ISME J. 11, 1412–1422 (2017).PubMed 
PubMed Central 

Google Scholar 
Gilbert, J. A. et al. Defining seasonal marine microbial community dynamics. ISME J. 6, 298–308 (2012).CAS 
PubMed 

Google Scholar 
Beaugrand, G. et al. Synchronous marine pelagic regime shifts in the Northern Hemisphere. Philos. Trans. R. Soc. B 370, 20130272 (2015).
Google Scholar 
Conversi, A. et al. The Mediterranean Sea Regime Shift at the End of the 1980s, and intriguing parallelisms with other European Basins. PLoS ONE 5, e10633 (2010).ADS 
PubMed 
PubMed Central 

Google Scholar 
Eilertsen, H., Sandberg, S. & Tøllefsen, H. Photoperiodic control of diatom spore growth; a theory to explain the onset of phytoplankton blooms. Mar. Ecol. Prog. Ser. 116, 303–307 (1995).ADS 

Google Scholar 
Hensen, V. Ueber die Bestimmung des Plankton’s oder des im Meere treibenden Materials an Pflanzen und Thieren (Kiel Publishers, 1887).
Google Scholar 
Andersen, D. M. & Keafer, B. A. An endogenous annual clock in the toxic marine dinoflagellate Gonyaulax tamarensis. Nature 325, 616–617 (1987).ADS 

Google Scholar 
Kremp, A. & Anderson, D. M. Factors regulating germination of resting cysts of the spring bloom dinoflagellate Scrippsiella hangoei from the northern Baltic Sea. J. Plankton Res. 22, 1311–1327 (2000).
Google Scholar 
Aubry, F. B. et al. Plankton communities in the northern Adriatic Sea: Patterns and changes over the last 30 years. Estuar. Coast. Shelf Sci. 115, 125–137 (2012).ADS 

Google Scholar 
Gutiérrez-Rodríguez, A. et al. Growth and grazing rate dynamics of major phytoplankton groups in an oligotrophic coastal site. Estuar. Coast. Shelf Sci. 95, 77–87 (2011).ADS 

Google Scholar 
Brannock, P. M., Ortmann, A. C., Moss, A. G. & Halanych, K. M. Metabarcoding reveals environmental factors influencing spatio-temporal variation in pelagic micro-eukaryotes. Mol. Ecol. 25, 3593–3604 (2016).PubMed 

Google Scholar 
Piredda, R. et al. Diversity and temporal patterns of planktonic protist assemblages at a Mediterranean Long Term Ecological Research site. FEMS Microbiol. Ecol. 93, fiw200 (2017).PubMed 

Google Scholar 
Lambert, S. et al. Rhythmicity of coastal marine picoeukaryotes, bacteria and archaea despite irregular environmental perturbations. ISME J. 13, 388–401 (2019).PubMed 

Google Scholar 
Hiltz, M., Bates, S. S. & Kaczmarska, I. Effect of light: Dark cycles and cell apical length on the sexual reproduction of the pennate diatom Pseudo-nitzschia multiseries (Bacillariophyceae) in culture. Phycologia 39, 59–66 (2000).
Google Scholar 
Mouget, J.-L., Gastineau, R., Davidovich, O., Gaudin, P. & Davidovich, N. A. Light is a key factor in triggering sexual reproduction in the pennate diatom Haslea ostrearia. FEMS Microbiol. Ecol. 69, 194–201 (2009).CAS 
PubMed 

Google Scholar 
Montresor, M., Vitale, L., D’Alelio, D. & Ferrante, M. I. Sex in marine planktonic diatoms: Insights and challenges. Perspect. Phycol. 3, 61–75 (2016).
Google Scholar 
Rost, B., Riebesell, U. & Sültemeyer, D. Carbon acquisition of marine phytoplankton: Effect of photoperiod length. Limnol. Oceanogr. 51, 12–20 (2006).ADS 
CAS 

Google Scholar 
Edwards, K. F. Community trait structure in phytoplankton: Seasonal dynamics from a method for sparse trait data. Ecology 97, 3441–3451 (2016).PubMed 

Google Scholar 
Forrest, J. & Miller-Rushing, A. J. Toward a synthetic understanding of the role of phenology in ecology and evolution. Philos. Trans. R. Soc. B 365, 3101–3112 (2010).
Google Scholar 
Margiotta, F. et al. Do plankton reflect the environmental quality status? The case of a post-industrial Mediterranean Bay. Mar. Environ. Res. 160, 104980 (2020).CAS 
PubMed 

Google Scholar 
Ferrera, I. et al. Assessment of microbial plankton diversity as an ecological indicator in the NW Mediterranean coast. Mar. Pollut. Bull. 160, 111691 (2020).CAS 
PubMed 

Google Scholar 
Cloern, J. E., Jassby, A. D., Thompson, J. K. & Hieb, K. A. A cold phase of the East Pacific triggers new phytoplankton blooms in San Francisco Bay. Proc. Natl. Acad. Sci. USA 104, 18561–18565 (2007).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Scotto di Carlo, B. et al. Uno studio integrato dell’ecosistema pelagico costiero del Golfo di Napoli. Nova Thalass 7, 99–128 (1985).
Google Scholar 
Carrada, G. C., Fresi, E., Marino, D., Modigh, M. & D’Alcalà, M. R. Structural analysis of winter phytoplankton in the Gulf of Naples. J. Plankton Res. 3, 291–314 (1981).CAS 

Google Scholar 
Marino, D., Modigh, M. & Zingone, A. General features of phytoplankton communities and primary production in the Gulf of Naples and adjacent waters. In Marine Phytoplankton and Productivity (Springer, 1984).
Google Scholar 
Hansen, H. P. & Grasshoff, K. Automated chemical analysis. Methods Seawater Anal. 49, 347–395 (1983).
Google Scholar 
Sabia, L. et al. Assessing the quality of biogeochemical coastal data: A step-wise procedure. Mediterr. Mar. Sci. 20, 56–73 (2019).
Google Scholar 
Mann, H. B. Nonparametric tests against trend. Econometrica 13, 245–259 (1945).MathSciNet 
MATH 

Google Scholar 
Kendall, M. G. Kendall Rank Correlation Methods (Griffin, 1975).
Google Scholar 
Jassby, A. D. & Cloern, J. E. wq: Exploring water quality monitoring data. R Package Version 04 5, (2015).Lomb, N. R. Least-squares frequency analysis of unequally spaced data. Astrophys. Space Sci. 39, 447–462 (1976).ADS 

Google Scholar 
Scargle, J. D. Studies in astronomical time series analysis. II-Statistical aspects of spectral analysis of unevenly spaced data. Astrophys. J. 263, 835–853 (1982).ADS 

Google Scholar 
Linnell Nemec, A. F. & Nemec, J. M. A test of significance for periods derived using phase-dispersion-minimization techniques. Astron. J. 90, 2317–2320 (1985).ADS 

Google Scholar 
Fuhrman, J. A., Cram, J. A. & Needham, D. M. Marine microbial community dynamics and their ecological interpretation. Nat. Rev. Microbiol. 13, 133–146 (2015).CAS 
PubMed 

Google Scholar 
Cram, J. A. et al. Seasonal and interannual variability of the marine bacterioplankton community throughout the water column over ten years. ISME J. 9, 563–580 (2015).PubMed 

Google Scholar 
Escoufier, Y. Le traitement des variables vectorielles. Biometrics 29, 751–760 (1973).MathSciNet 

Google Scholar 
Thioulouse, J. et al. Multivariate Analysis of Ecological Data with ade4 (Springer, 2018).
Google Scholar 
Fuhrman, J. A. et al. Annually reoccurring bacterial communities are predictable from ocean conditions. Proc. Natl. Acad. Sci. USA 103, 13104–13109 (2006).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
O’Brien, R. M. A caution regarding rules of thumb for variance inflation factors. Qual. Quant. 41, 673–690 (2007).
Google Scholar 
Grömping, U. Relative importance for linear regression in R: the package relaimpo. J. Stat. Softw. 17, 1–27 (2006).
Google Scholar 
Bi, J. A review of statistical methods for determination of relative importance of correlated predictors and identification of drivers of consumer liking. J. Sens. Stud. 27, 87–101 (2012).
Google Scholar  More

Collias, N.E. & Collias, E.C. Nest Building and Bird Behavior. (Princeton University Press, 1984).Hansell, M.H. Bird nests and construction behaviour. (Cambridge University Press, 2000).Deeming, D.C. & Reynolds, S.J. Nests, eggs and incubation: New ideas about avian reproduction. (Oxford University Press, 2015).Pärssinen, V., Kalb, N., Vallon, M., Anthes, N. & Heubel, K. U. Male and female preferences for nest characteristics under paternal care. Ecol. Evol. 9, 7780–7791 (2019).PubMed 
PubMed Central 

Google Scholar 
Soler, J. J., Morales, J., Cuervo, J. J. & Moreno, J. Conspicuousness of passerine females is associated with the nest-building behaviour of males. Biol. J. Linn. Soc. 126, 824–835 (2019).
Google Scholar 
Tipton, H. C., Dreitz, V. J. & Doherty, P. F. Jr. Occupancy of Mountain Plover and Burrowing Owl in Colorado. J. Wildl. Manage. 72, 1001–1006 (2008).
Google Scholar 
Mukherjee, A., Kumara, H. N. & Bhupathy, S. Golden jackal’s underground shelters: Natal site selection, seasonal burrowing activity and pup rearing by a cathemeral canid. Mammal Res. 63, 325–339 (2018).
Google Scholar 
Berg, M. L., Beintema, N. H., Welbergen, J. A. & Komdeur, J. The functional significance of multiple nest building in the Australian Reed Warbler Acrocephalus australis. Ibis 148, 395–404 (2006).
Google Scholar 
Vergara, P., Gordo, O. & Aguirre, J. I. Nest size, nest building behaviour and breeding success in a species with nest reuse: the white stork Ciconia ciconia. Ann. Zool. Fennici 47, 184–194 (2010).
Google Scholar 
Hansell, M.H. Animal architecture. (Oxford University Press, 2005).Newton, I. Population ecology of raptors. Berkhamsted (T and AD Poyser, 1979).Ontiveros, D., Caro, J. & Pleguezuelos, J. M. Green plant material versus ectoparasites in nests of Bonelli’s Eagle. J. Zool. 274, 99–104 (2008).
Google Scholar 
Soler, J. J., Møller, A. P. & Soler, M. Nest building, sexual selection and parental investment. Evol. Ecol. 12, 427–441 (1998).
Google Scholar 
Moreno, J., Soler, M., Møller, A. P. & Linden, M. The function of stone carrying in the Black Wheatear, Oenanthe leucura. Anim. Behav. 47, 1297–1309 (1994).
Google Scholar 
Soler, J. J., Soler, M., Møller, A. P. & Martínez, J. G. Does the great spotted cuckoo choose magpie hosts according to their parenting ability?. Behav. Ecol. Sociobiol. 36, 201–206 (1995).
Google Scholar 
Soler, J. J., Cuervo, J. J., Møller, A. P. & de Lope, F. Nest building is a sexually selected behaviour in the barn swallow. Anim. Behav. 56, 1435–1442 (1998).CAS 
PubMed 

Google Scholar 
Canal, D., Mulero-Pázmány, M., Negro, J. J. & Sergio, F. Decoration increases the conspicuousness of raptor nests. PLoS ONE 11, e0157440 (2016).PubMed 
PubMed Central 

Google Scholar 
Biddle, L., Goodman, A. M. & Deeming, D. C. Construction patterns of birds’ nests provide insight into nest-building behaviours. PeerJ 5, e3010 (2017).PubMed 
PubMed Central 

Google Scholar 
Akresh, M. E., Ardia, D. R. & King, D. I. Effect of nest characteristics on thermal properties, clutch size, and reproductive performance for an open-cup nesting songbird. Avian Biol. Res. 10, 107–118 (2017).
Google Scholar 
Podofillini, S. et al. Home, dirty home: Effect of old nest material on nest-site selection and breeding performance in a cavity-nesting raptor. Curr. Zool. 64, 693–702 (2018).PubMed 
PubMed Central 

Google Scholar 
Ruiz-Castellano, C., Tomás, G., Ruiz-Rodríguez, M., Martín-Gálvez, D. & Soler, J. J. Nest material shapes eggs bacterial environment. PLoS ONE 11, e0148894 (2016).PubMed 
PubMed Central 

Google Scholar 
Tomás, G. et al. Interacting effects of aromatic plants and female age on nest-dwelling ectoparasites and blood-sucking flies in avian nests. Behav. Proc. 90, 246–253 (2012).
Google Scholar 
Suárez-Rodríguez, M. & García, C. M. An experimental demonstration that house finches add cigarette butts in response to ectoparasites. J. Avian Biol. 48, 1316–1321 (2017).
Google Scholar 
Mennerat, A. et al. Aromatic plants in nests of the blue tit Cyanistes caeruleus protect chicks from bacteria. Oecologia 161, 849–855 (2009).ADS 
PubMed 

Google Scholar 
Sanz, J. J. & García-Navas, V. Nest ornamentation in blue tits: is feather carrying ability a male status signal?. Behav. Ecol. 22, 240–247 (2011).
Google Scholar 
Östlund-Nilsson, S. & Holmlund, M. The artistic three-spined stickleback (Gasterosteus aculeatus). Behav. Ecol. Sociobiol. 53, 214–220 (2003).
Google Scholar 
Quader, S. What makes a good nest? Benefits of nest choice to female Baya Weavers (Ploceus philippinus). Auk 123, 475–486 (2006).
Google Scholar 
Møller, A. P. & Nielsen, J. T. Large increase in nest size linked to climate change: an indicator of life history, senescence and condition. Oecologia 179, 913–921 (2015).ADS 
PubMed 

Google Scholar 
De Neve, L., Soler, J. J., Soler, M. & Pérez-Contreras, T. Nest size predicts the effect of food supplementation to magpie nestlings on their immunocompetence: An experimental test of nest size indicating parental ability. Behav. Ecol. 15, 1031–1036 (2004).
Google Scholar 
Szentirmai, I., Komdeur, J. & Székely, T. What makes a nest-building male successful? Male behavior and female care in penduline tits. Behav. Ecol. 16, 994–1000 (2005).
Google Scholar 
Tomás, G. et al. Nest size and aromatic plants in the nest as sexually selected female traits in blue tits. Behav. Ecol. 24, 926–934 (2013).
Google Scholar 
Jelínek, V., Požgayová, M., Honza, M. & Procházka, P. Nest as an extended phenotype signal of female quality in the great reed warbler. J. Avian Biol. 47, 428–437 (2016).
Google Scholar 
Muth, F. & Healy, S. D. The role of adult experience in nest building in the zebrafinch, Taeniopygia guttata. Anim. Behav. 82, 185–189 (2011).
Google Scholar 
Wysocki, D. et al. Factors affecting nest size in a population of Blackbirds Turdus merula. Bird Study 62, 208–216 (2015).
Google Scholar 
Moreno, J. Avian nests and nest-building as signals. Avian Biol. Res. 5, 238–251 (2012).
Google Scholar 
Bailey, I. E., Morgan, K. V., Bertin, M., Meddle, S. L. & Healy, S. D. Physical cognition: Birds learn the structural efficacy of nest material. Proc. R. Soc. B 281, 20133225 (2014).PubMed 
PubMed Central 

Google Scholar 
Camacho-Alpízar, A., Eckersley, T., Lambert, C. T., Balasubramanian, G. & Guillette, L. M. If it ain’t broke don’t fix it: Breeding success affects nest-building decisions. Behav. Proc. 184, 104336 (2021).
Google Scholar 
Madden, J. R. Bower decorations are good predictors of mating success in the spotted bowerbird. Behav. Ecol. Sociobiol. 53, 269–277 (2003).
Google Scholar 
Mainwaring, M. C., Nagy, J. & Hauber, M. E. Sex-specific contributions to nest building in birds. Behav. Ecol. https://doi.org/10.1093/beheco/arab035 (2021).Article 

Google Scholar 
Witte, K. The differential-allocation hypothesis: Does the evidence support it?. Evolution 49, 1289–1290 (1995).PubMed 

Google Scholar 
Wright, J. & Cuthill, I. Monogamy in the European starling. Behaviour 120, 262–285 (1992).
Google Scholar 
Burley, N. Sexual selection for aesthetic traits in species with biparental care. Am. Nat. 127, 415–445 (1986).ADS 

Google Scholar 
Mainwaring, M. C., Hartley, I. R., Lambrechts, M. M. & Deeming, D. C. The design and function of birds’ nests. Ecol. Evol. 4, 3909–3928 (2014).PubMed 
PubMed Central 

Google Scholar 
Sergio, F. et al. Raptor nest decorations are a reliable threat against conspecifics. Science 331, 327–330 (2011).ADS 
CAS 
PubMed 

Google Scholar 
Heinrich, B. Why does a hawk build with green nesting material?. Northeast. Nat. 20, 209–218 (2013).
Google Scholar 
Mingju, E. et al. Old nest material functions as an informative cue in making nest-site selection decisions in the European Kestrel (Falco tinnunculus). Avian Res. 10, 43 (2019).
Google Scholar 
Martínez-Abraín, A. & Jiménez, J. Stick supply to nests by cliff-nesting raptors as an evolutionary load of past tree-nesting. IEE 12, 22–25. https://doi.org/10.24908/iee.2019.12.3.n (2019).Article 

Google Scholar 
Martínez, J. E. et al. Breeding behaviour and time-activity budgets of Bonelli’s Eagles Aquila fasciata: Marked sexual differences in parental activities. Bird Study 47, 35–44 (2020).
Google Scholar 
Cramp, S. & Simmons, K.E.L. Handbook of the Birds of the western Palearctic. Vol. 2. (Oxford University Press, 1980).Paillisson, J. M. & Chambon, R. Variation in male-built nest volume with nesting-support quality, colony, and egg production in whiskered terns. Ecol. Evol. 11, 15585–15600 (2021).PubMed 
PubMed Central 

Google Scholar 
Álvarez, E. & Barba, E. Nest quality in relation to adult bird condition and its impact on reproduction in Great Tits Parus major. Acta Ornithol. 43, 3–9 (2008).
Google Scholar 
Ferguson-Lees, J. & Christie, D. Raptors of the world. (Christopher Helm, 2001).Ontiveros, D. Águila perdicera – Aquila fasciata. In Enciclopedia Virtual de los Vertebrados Españoles. (eds. Salvador, A. & Morales, M.B.) Museo Nacional de Ciencias Naturales, Madrid; http://www.vertebradosibericos.org/ (accessed 13 September 2021) (2016).Del Hoyo, J., Elliott, A. & Sargatal, J. Handbook of the birds of the world, vol. 2. New world vultures to guineafowl. (Lynx Edicions, 1994).Ontiveros, D., Caro, J. & Pleguezuelos, J. M. Possible functions of alternative nests in raptors: the case of Bonelli’s Eagle. J. Ornithol. 149, 253–259 (2008).
Google Scholar 
Del Moral, J.C. & Molina, B. El águila perdicera en España, población reproductora en 2018 y método de censo. (SEO/BirdLife, 2018).BirdLife International. Aquila fasciata (amended version of 2016 assessment). The IUCN Red List of Threatened Species 2019. https://doi.org/10.2305/IUCN.UK.2019-3.RLTS.T22696076A155464015.en. Downloaded on 26 June 2021 (2019).Balbontín, J. & Ferrer, M. Condition of large brood in Bonelli’s Eagle Hieraaetus fasciatus. Bird Study 52, 37–41 (2005).
Google Scholar 
Martínez, J. E. et al. Copulatory behaviour in the Bonelli’s Eagle (Aquila fasciata): assessing the paternity assurance hypothesis. PLoS ONE 14, e0217175 (2019).PubMed 
PubMed Central 

Google Scholar 
Margalida, A. & Bertran, J. Nest-building behaviour of the Bearded Vulture Gypaetus barbatus. Ardea 88, 259–264 (2000).
Google Scholar 
Krüger, O. Dissecting common buzzard lifespan and lifetime reproductive success: the relative importance of food, competition, weather, habitat and individual attributes. Oecologia 133, 474–482 (2002).ADS 
PubMed 

Google Scholar 
Morrison, T. A., Yoshizaki, J., Nichols, J. D. & Bolger, D. T. Estimating survival in photographic capture–recapture studies: overcoming misidentification error. Methods Ecol. Evol. 2, 454–463 (2011).
Google Scholar 
Jiménez-Franco, M. V., Martínez, J. E., Pagán, I. & Calvo, J. F. Factors determining territory fidelity in a migratory forest raptor, the Booted Eagle Hieraaetus pennatus. J. Ornithol. 154, 311–318 (2013).
Google Scholar 
Sreekar, R. et al. Photographic capture-recapture sampling for assessing populations of the Indian Gliding Lizard Draco dussumieri. PLoS ONE 8, e55935 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Goswami, V. R. et al. Towards a reliable assessment of Asian elephant population parameters: The application of photographic spatial capture–recapture sampling in a priority floodplain ecosystem. Sci. Rep. 9, 8578 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
Méndez, D., Marsden, S. & Lloyd, H. Assessing population size and structure for Andean Condor Vultur gryphus in Bolivia using a photographic ‘capture-recapture’ method. Ibis 161, 867–877 (2019).
Google Scholar 
Zuberogoitia, J., Martínez, J. E. & Zabala, J. Individual recognition of territorial peregrine falcons Falco peregrinus: A key for long-term monitoring programmes. Munibe 61, 117–127 (2013).
Google Scholar 
Gil-Sánchez, J. M., Bautista, J., Godinho, R. & Moleón, M. Detection of individual replacements in a long-lived bird species, the Bonelli’s Eagle (Aquila fasciata), using three noninvasive methods. J. Raptor Res. https://doi.org/10.3356/JRR-20-53 (2021).Article 

Google Scholar 
García, V., Moreno-Opo, R. & Tintó, A. Sex differentiation of Bonelli’s Eagle Aquila fasciata in western Europe using morphometrics and plumage colour patterns. Ardeola 60, 261–277 (2013).
Google Scholar 
Real, J., Mañosa, S. & Codina, J. Post-nestling dependence period in the Bonelli’s Eagle Hieraaetus fasciatus. Ornis Fenn. 75, 129–137 (1998).
Google Scholar 
Mínguez, E., Angulo, E. & Siebering, V. Factors influencing length of the post-fledging period and timing of dispersal in Bonelli’s Eagle (Hieraaetus fasciatus) in southwestern Spain. J. Raptor Res. 35, 228–234 (2001).
Google Scholar 
Gil-Sánchez, J. M., Moleón, M., Otero, M. & Bautista, J. A nine-year study of successful breeding in a Bonelli’s eagle population in southeast Spain: A basis for conservation. Biol. Conserv. 118, 685–694 (2004).
Google Scholar 
Resano-Mayor, J. et al. Multi-scale effects of nestling diet on breeding performance in a terrestrial top predator inferred from stable isotope analysis. PLoS ONE 9, e95320 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Zuberogoitia, J., Martínez, J. E., Larrea, M. & Zabala, M. Parental investment of male Peregrine Falcons during incubation: Influence of experience and weather. J. Ornithol. 159, 275–282 (2018).
Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing. Available at: http://www.R-project.org/ (accessed 20 March 2021) (2021).Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Google Scholar 
Fernández, C. Nest material supplies in the Marsh Harrier Circus aeruginosus: Sexual roles, daily and seasonal activity patterns and rainfall influence. Ardea 80, 281–284 (1992).
Google Scholar 
Margalida, A., González, L. M., Sánchez, R., Oria, J. & Prada, L. Parental behaviour of Spanish Imperial Eagles Aquila adalberti: sexual differences in a moderately dimorphic raptor. Bird Study 54, 112–119 (2007).
Google Scholar 
López-López, P., Perona, A. M., Egea-Casas, O., Morant, J. & Urios, V. Tri-axial accelerometry shows differences in energy expenditure and parental effort throughout the breeding season in long-lived raptors. Curr. Zool. https://doi.org/10.1093/cz/zoab010 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Morant, J., López-López, P. & Zuberogoitia, I. Parental investment asymmetries of a globally endangered scavenger: Unravelling the role of gender, weather conditions and stage of the nesting cycle. Bird Study 66, 329–341 (2019).
Google Scholar 
Margalida, A. & Bertran, J. Breeding biology of the Bearded Vulture Gypaetus barbatus: Minimal sexual differences in parental activities. Ibis 142, 225–234 (2000).
Google Scholar 
Wimberger, P. H. The use of green plant material in bird nests to avoid ectoparasites. Auk 101, 615–618 (1984).
Google Scholar 
Dubiec, A., Gózdz, I. & Mazgagski, T. D. Green plant material in avian nests. Avian Biol. Res. 6, 133–146 (2013).
Google Scholar 
Jagiello, Z. A., Dylewski, L., Winiarska, D., Zolnierowicz, K. M. & Tobolka, M. Factors determining the occurrence of anthropogenic materials in nests of the white stork Ciconia ciconia. Environ. Sci. Pollut. Res. 25, 14726–14733 (2018).
Google Scholar 
Fargallo, J. A., de León, A. & Potti, J. Nest maintenance effort and health status in chinstrap penguins, Pygoscelis antarctica: the functional significance of stone provisioning behaviour. Behav. Ecol. Sociobiol. 50, 141–150 (2001).
Google Scholar  More

Hannam, K. D. et al. Wood ash as a soil amendment in Canadian forests: what are the barriers to utilization?. Can. J. For. Res. 48, 442–450 (2018).
Google Scholar 
Hope, E. S., McKenney, D. W., Allen, D. J. & Pedlar, J. H. A cost analysis of bioenergy-generated ash disposal options in Canada. Can. J. For. Res. https://doi.org/10.1139/cjfr-2016-0524 (2017).Article 

Google Scholar 
Bowd, E. J., Banks, S. C., Strong, C. L. & Lindenmayer, D. B. Long-term impacts of wildfire and logging on forest soils. Nat. Geosci. 12, 113–118 (2019).ADS 
CAS 

Google Scholar 
Adotey, N., Harrell, D. L. & Weatherford, W. P. Characterization and liming effect of wood Ash generated from a biomass-fueled commercial power plant. Commun. Soil Sci. Plan. 49, 38–49 (2018).CAS 

Google Scholar 
Royer-Tardif, S., Whalen, J. & Rivest, D. Can alkaline residuals from the pulp and paper industry neutralize acidity in forest soils without increasing greenhouse gas emissions?. Sci. Total Environ. 663, 537–547 (2019).ADS 
CAS 
PubMed 

Google Scholar 
Reid, C. & Watmough, S. A. Evaluating the effects of liming and wood-ash treatment on forest ecosystems through systematic meta-analysis. Can. J. For. Res. 44, 867–885 (2014).CAS 

Google Scholar 
López, R., Díaz, M. J. & González-Pérez, J. A. Extra CO2 sequestration following reutilization of biomass ash. Sci. Total Environ. 625, 1013–1020 (2018).ADS 
PubMed 

Google Scholar 
Emilson, C. E. et al. Short-term growth response of jack pine and spruce spp. to wood ash amendment across Canada. GCB Bioenergy 12, 158–167 (2020).
Google Scholar 
Azan, S. S. E. et al. Could a residential wood ash recycling programme be part of the solution to calcium decline in lakes and forests in Muskoka (Ontario, Canada)?. FACETS 4, 69–90 (2019).
Google Scholar 
Gorgolewski, A. et al. Responses of eastern red-backed salamander (Plethodon cinereus) abundance 1 year after application of wood ash in a northern hardwood forest. Can. J. For. Res. 46, 402–409 (2016).
Google Scholar 
McTavish, M. J., Gorgolewski, A., Murphy, S. D. & Basiliko, N. Field and laboratory responses of earthworms to use of wood ash as a forest soil amendment. For. Ecol. Manag. 474, 118376 (2020).
Google Scholar 
Mortensen, L. H., Rønn, R. & Vestergård, M. Bioaccumulation of cadmium in soil organisms: with focus on wood ash application. Ecotox. Environ. Safe. 156, 452–462 (2018).CAS 

Google Scholar 
Bélanger, N., Palma Ponce, G. & Brais, S. Contrasted growth response of hybrid larch (Larix × marschlinsii), jack pine (Pinus banksiana) and white spruce (Picea glauca) to wood ash application in northwestern Quebec, Canada. iForest. 14, 155 (2021).
Google Scholar 
Santás-Miguel, V. et al. Use of biomass ash to reduce toxicity affecting soil bacterial community growth due to tetracycline antibiotics. J. Environ. Manage. 269, 110838 (2020).PubMed 

Google Scholar 
Fritze, H. et al. A microcosmos study on the effects of cd-containing wood ash on the coniferous humus fungal community and the cd bioavailability. J Soils Sediments 1, 146–150 (2001).CAS 

Google Scholar 
Coleman, D., Callaham, Jr., M. A. & Crossley, Jr., D. A. Fundamentals of Soil Ecology. (Elsevier, 2018). https://doi.org/10.1016/C2015-0-04083-7.Smenderovac, E. E. et al. Does intensified boreal forest harvesting impact soil microbial community structure and function?. Can. J. For. Res. 47, 916–925 (2017).CAS 

Google Scholar 
Joseph, R. et al. Limited effect of wood ash application on soil quality as indicated by a multisite assessment of soil organic matter attributes. GCB Bioenergy. 00, 1–22. https://doi.org/10.1111/gcbb.12928 (2022).CAS 
Article 

Google Scholar 
Noyce, G. L. et al. Soil microbial responses to wood ash addition and forest fire in managed Ontario forests. Appl. Soil Ecol. 107, 368–380 (2016).
Google Scholar 
Liiri, M., Ilmarinen, K. & Setälä, H. Variable impacts of enchytraeid worms and ectomycorrhizal fungi on plant growth in raw humus soil treated with wood ash. Appl. Soil Ecol. 35, 174–183 (2007).
Google Scholar 
Brais, S., Bélanger, N. & Guillemette, T. Wood ash and N fertilization in the Canadian boreal forest: Soil properties and response of jack pine and black spruce. For. Ecol. Manag. 348, 1–14 (2015).
Google Scholar 
Gömöryová, E., Pichler, V., Tóthová, S. & Gömöry, D. Changes of chemical and biological properties of distinct forest floor layers after wood ash application in a Norway spruce stand. Forests 7, 108 (2016).
Google Scholar 
Hannam, K., Great Lakes Forestry Centre, Canada, Ressources naturelles Canada & Canadian Forest Service. Regulations and guidelines for the use of wood ash as a soil amendment in Canadian forests. (2016).Hannam, K. D. et al. AshNet: Facilitating the use of wood ash as a forest soil amendment in Canada. Forest. Chron. 93, 17–20 (2017).
Google Scholar 
Klavina, D. et al. The ectomycorrhizal community of conifer stands on peat soils 12 years after fertilization with wood ash. Mycorrhiza 26, 153–160 (2016).PubMed 

Google Scholar 
Bang-Andreasen, T. et al. Wood ash induced pH changes strongly affect soil bacterial numbers and community composition. Front. Microbiol. 8, 1400 (2017).PubMed 
PubMed Central 

Google Scholar 
Vestergård, M. et al. The relative importance of the bacterial pathway and soil inorganic nitrogen increase across an extreme wood-ash application gradient. GCB Bioenergy 10, 320–334 (2018).
Google Scholar 
Ekenler, M. & Tabatabai, M. A. β-glucosaminidase activity as an index of nitrogen mineralization in soils. Commun. Soil Sci. Plan. 35, 1081–1094 (2004).CAS 

Google Scholar 
Margalef, O. et al. Global patterns of phosphatase activity in natural soils. Sci Rep 7, 1337 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Vestergaard, G., Schulz, S., Schöler, A. & Schloter, M. Making big data smart—how to use metagenomics to understand soil quality. Biol. Fertil. Soils 53, 479–484 (2017).
Google Scholar 
Emilson, C. et al. Synthesis of current AshNet study designs and methods with recommendations towards a standardized protocol. Information Report GLC-X-22. (2018).Baldwin, K. et al. Vegetation zones of Canada: A biogeoclimatic perspective – Open Government Portal. (2019).Findlay, S. CHAPTER 11: Dissolved organic matter. In: Methods in Stream Ecology (Second Edition) (eds. Hauer, F. R. & Lamberti, G. A.) 239–248 (Academic Press, 2007). https://doi.org/10.1016/B978-012332908-0.50013-9.Saiya-Cork, K. R., Sinsabaugh, R. L. & Zak, D. R. The effects of long term nitrogen deposition on extracellular enzyme activity in an Acer saccharum forest soil. Soil Biol. Biochem. 34, 1309–1315 (2002).CAS 

Google Scholar 
Porter, T. M. & Hajibabaei, M. METAWORKS: A flexible, scalable bioinformatic pipeline for multi-marker biodiversity assessments. bioRxiv 2020.07.14.202960 (2020) https://doi.org/10.1101/2020.07.14.202960.Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Porter, T. M. & Hajibabaei, M. Automated high throughput animal CO1 metabarcode classification. Sci. Rep-UK 8, 4226 (2018).ADS 

Google Scholar 
Kõljalg, U., Abarenkov, K., Nilsson, R. H., Larsson, K. & Taylor, A. F. S. The UNITE Database for molecular identification and for communicating fungal species (2019). https://doi.org/10.3897/BISS.3.37402.Porter, T. M. UNITE ITS Classifier. (2020). https://github.com/terrimporter/UNITE_ITSClassifierLouca, S., Parfrey, L. W. & Doebeli, M. Decoupling function and taxonomy in the global ocean microbiome. Science 353, 1272–1277 (2016).ADS 
CAS 

Google Scholar 
Nguyen, N. H. et al. FUNGuild: An open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol. 20, 241–248 (2016).
Google Scholar 
Hedde, M. et al. BETSI, a complete framework for studying soil invertebrate functional traits. (2012). https://doi.org/10.13140/2.1.1286.6888.McKenney, D. W. et al. Customized spatial climate models for North America. Bull. Am. Meteor. Soc. 92, 1611–1622 (2011).ADS 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2021).Fernandes, A. D., Macklaim, J. M., Linn, T. G., Reid, G. & Gloor, G. B. ANOVA-like differential expression (ALDEx) analysis for mixed population RNA-Seq. PLOS ONE 8, 15 (2013).
Google Scholar 
Wickham, H. et al. Welcome to the {tidyverse}. J. Open Source Softw. 4, 1686 (2019).ADS 

Google Scholar 
Oksanen, J. et al. Vegan: Community ecology package. https://CRAN.R-project.org/package=vegan (2020).Domes, K. A. et al. Short-term changes in spruce foliar nutrients and soil properties in response to wood ash application in the sub-boreal climate zone of British Columbia. Can. J. Soil. Sci. 98, 246–263 (2018).CAS 

Google Scholar 
Pugliese, S. et al. Wood ash as a forest soil amendment: The role of boiler and soil type on soil property response. Can. J. Soil. Sci. 94, 621–634 (2014).CAS 

Google Scholar 
Bang-Andreasen, T. et al. Total RNA sequencing reveals multilevel microbial community changes and functional responses to wood ash application in agricultural and forest soil. FEMS Microbiol. Ecol. 96, fiaa016 (2020).PubMed 
PubMed Central 

Google Scholar 
Haimi, J., Fritze, H. & Moilanen, P. Responses of soil decomposer animals to wood-ash fertilisation and burning in a coniferous forest stand. For. Ecol. Manag. 129, 53–61 (2000).
Google Scholar 
Aronsson, K. A. & Ekelund, N. G. A. Biological effects of wood ash application to forest and aquatic ecosystems. J. Environ. Qual. 33, 1595–1605 (2004).CAS 
PubMed 

Google Scholar 
Omil, B., Piñeiro, V. & Merino, A. Trace elements in soils and plants in temperate forest plantations subjected to single and multiple applications of mixed wood ash. Sci. Total Environ. 381, 157–168 (2007).ADS 
CAS 
PubMed 

Google Scholar 
Taylor, A. F. S. & Finlay, R. D. Effects of liming and ash application on below ground ectomycorrhizal community structure in two Norway spruce forests. WAFO 3, 63–76 (2003).CAS 

Google Scholar 
Wallander, H., Fossum, A., Rosengren, U. & Jones, H. Ectomycorrhizal fungal biomass in roots and uptake of P from apatite by Pinus sylvestris seedlings growing in forest soil with and without wood ash amendment. Mycorrhiza 15, 143–148 (2005).PubMed 

Google Scholar 
Kjøller, R., Cruz-Paredes, C. & Clemmensen, K. E. Ectomycorrhizal fungal responses to forest liming and wood ash addition: Review and meta-analysis. In Soil Biological Communities and Ecosystem Resilience (eds Lukac, M. et al.) 223–252 (Springer International Publishing, Berlin, 2017).
Google Scholar 
Peltoniemi, K., Pyrhönen, M., Laiho, R., Moilanen, M. & Fritze, H. Microbial communities after wood ash fertilization in a boreal drained peatland forest. Eur. J. Soil Biol. 76, 95–102 (2016).CAS 

Google Scholar 
Boisvert-Marsh, L., Great Lakes Forestry Centre, Canada & Resources naturelles Canada. The Island Lake biomass harvest experiment: early results. (2016).Couch, R. L., Luckai, N., Morris, D. & Diochon, A. Short-term effects of wood ash application on soil properties, growth, and foliar nutrition of Picea mariana and Picea glauca seedlings in a plantation trial. Can. J. Soil. Sci. 101, 203–215 (2021).CAS 

Google Scholar 
Perkiömäki, J. & Fritze, H. Cadmium in upland forests after vitality fertilization with wood ash—a summary of soil microbiological studies into the potential risk of cadmium release. Biol Fertil Soils 41, 75–84 (2005).
Google Scholar 
Paredes, C. et al. Bacteria respond stronger than fungi across a steep wood ash-driven pH gradient. Front. For. Glob. Change 4, 781884 (2021).
Google Scholar 
Kļaviņa, D. et al. Fungal communities in roots of scots pine and Norway spruce saplings grown for 10 years on peat soils fertilized with wood ash. Balt. For. 22, 10 (2016).
Google Scholar 
Hansen, M., Bang-Andreasen, T., Sørensen, H. & Ingerslev, M. Micro vertical changes in soil pH and base cations over time after application of wood ash on forest soil. For. Ecol. Manag. 406, 274–280 (2017).
Google Scholar 
Fu, X. et al. Understory vegetation leads to changes in soil acidity and in microbial communities 27years after reforestation. Sci. Total Environ. 502, 280–286 (2015).ADS 
CAS 
PubMed 

Google Scholar 
Pitman, R. M. Wood ash use in forestry – a review of the environmental impacts. Forestry 79, 563–588 (2006).
Google Scholar 
Cruz-Paredes, C., Tájmel, D. & Rousk, J. Can moisture affect temperature dependences of microbial growth and respiration?. Soil Biol. Biochem. 156, 108223 (2021).CAS 

Google Scholar  More

Microcosm preparation and incubationLeaf litters were collected from Lilly-Dickey Woods, a mature eastern US temperate broadleaf forest located in South-Central Indiana (39°14′N, 86°13′W) using litter baskets and surveys for freshly senesced litter as described in Craig et al.52. Of the 19 species collected in Craig et al. (2018), we selected litter from 16 tree species with the goal of maximizing variation in litter chemical traits (Table S1). Litters were air-dried and then homogenized and fragmented such that all litter fragments passed a 4000 µm, but not a 250 µm mesh. Whereas leaf litters had a distinctly C3 δ13C signature of −30.1 ± 1.5 (mean, standard deviation), we used a 13C-rich (δ13C = −12.6 ± 0.4) soil obtained from the A horizon of a 35-yr continuous corn field at the Purdue University Agronomy Center for Research and Education near West Lafayette, Indiana (40°4′N, 86°56′W). The soil is classified as Chalmers silty clay loam (a fine-silty, mixed, superactive, mesic Typic Endoaquoll). Prior to use in the incubation, soils were sieved (2 mm) and remaining recognizable plant residues were thoroughly picked out. Soils were mixed with acid-washed sand—30% by mass—to facilitate litter mixing (see below) and to increase the soil volume for post-incubation processing. The resulting soil had a pH of 6.7 and a C:N ratio of 12.0.We constructed the experimental microcosms by mixing the 16 litter species with the 13C-enriched soil. Each litter treatment was replicated four times in four batches (i.e., 16 microcosms per species, 272 total microcosms including 16 soil-only controls). Two batches (C budget microcosms) were used to monitor CO2 efflux and to track litter-derived C into SOM pools, and two batches were used to quantify microbial biomass dynamics.Incubations were carried out in 50 mL centrifuge tubes modified with an O-ring to prevent leakage and a rubber septum to facilitate headspace sampling. To each microcosm, we added 5 g dry soil, adjusted moisture to 65% water-holding capacity, and pre-incubated for 24 h in the dark at 24 °C. Using a dissecting needle, 300 mg of leaf litter were carefully mixed into treatment microcosms and controls were similarly agitated. This corresponds to an average C addition rate of 27.1 ± 1.1 g C kg−1 dry soil among the 16 species. During incubation, microcosms were loosely capped to retain moisture while allowing gas exchange, and were maintained at 65% water-holding capacity by adding deionized water every week.Carbon budget in microcosmsRespiration was quantified with an infrared gas analyzer (LiCOR 6262, Lincoln, NE, USA) coupled to a sample injection system. Our first measurement was taken about 12 h after litter addition (day 1) and subsequent measurements were taken on days 2, 4, 11, 19, and 30 for both batches and on days 46, 64, 79, 92, 109, 128, 149, and 185 for the second batch. Prior to each measurement, microcosms were capped, flushed with CO2-free air, and incubated for 1–8 h depending on the expected efflux rate. Headspace was sampled with a gas-tight syringe and the CO2-C concentration was converted to a respiration rate (µg CO2-C day−1). Total cumulative CO2-C loss was derived from point measurements by numerical integration (i.e., the trapezoid method). To evaluate soil-derived CO2-C efflux, we measured δ13C in two gas samples per litter type or control on a ThermoFinnigan DELTA Plus XP isotope ratio mass spectrometer (IRMS) with a GasBench interface (Thermo Fisher Scientific, San Jose, CA). Isotopes were measured on days 1, 4, 11, 30, 64, 109, and 185. On each of these days, a two-source mixing model70 was applied to determine the fraction of total CO2-C derived from soil organic matter vs. litter:$$frac{{F}^{l}(t)}{F(t)}=frac{delta Fleft(tright)-,delta {F}^{c}(t)}{delta {C}_{l}-delta {F}^{c}(t)}$$
(1)
where (frac{{F}^{l}(t)}{F(t)}) is the fraction total CO2-C efflux [(F(t))] derived from litter [({F}^{l}(t))] at time (left(tright)), (delta Fleft(tright)) is the δ13C of the CO2 respired by each litter-soil combination, (delta {F}^{c}(t)) is the average δ13C of the CO2 respired by the control soil, and (delta {C}_{l}) is the δ13C of each litter type. These data were used to calculate cumulative soil-derived C efflux via numerical integration and, for each litter type, average soil-derived C efflux was subtracted from total cumulative CO2-C loss to determine cumulative litter-derived CO2-C loss.Carbon budget microcosms were harvested on days 30 and 185 to track litter-derived C into mineral-associated SOC at an early and intermediate stage of decomposition. To do this, we used a size fractionation procedure71,72 modified to minimize the recovery of soluble leaf litter compounds or dissolved organic matter in the mineral-associated SOC fraction. For each sample, we first added 30 mL deionized water, gently shook by hand to suspend all particles, and then centrifuged (2500 rpm) for 10 min. Floating leaf litter was carefully removed, dried for 48 h at 60 °C, and weighed; and the clear supernatant was discarded to remove the dissolved organic matter. The remaining sample was dispersed in 5% (w/v) sodium hexametaphosphate for 20 h on a reciprocal shaker and then washed through a 53 µm sieve. The fraction retained on the sieve was added to the floating leaf litter sample and collectively referred to as particulate SOC, while the fraction that passed through the sieve was considered the mineral-associated SOC. Both fractions were dried, ground, and weighed; and analyzed for C concentrations and δ13C values on an elemental combustion system (Costech ECS 4010, Costech Analytical Technologies, Valencia, CA, USA) as an inlet to an IRMS. As above, litter-derived C in the particulate and mineral-associated SOC was determined as follows:$$frac{{C}_{s}^{l}(t)}{{C}_{s}(t)}=frac{delta {C}_{s}left(tright)-,delta {C}_{c}(t)}{delta {C}_{l}-delta {C}_{c}(t)}$$
(2)
where ({C}_{s}(t)) is the total particulate or mineral-associated SOC content in the sample at time ((t)), ({C}_{s}^{l}(t)) is the litter-derived C in the soil, (delta {C}_{s}left(tright)) is the measured δ13C value for each soil fraction, (delta {C}_{c}left(tright)) is the average δ13C for each fraction in control samples, and (delta {C}_{l}) is the δ13C of each litter type. In a few cases, mineral-associated δ13C was slightly less negative in the treatment than in the control soil. In these cases, litter-derived mineral-associated SOC was considered zero.Total litter-derived SOC at each harvest date was calculated by subtracting the cumulative litter CO2-C from initial added litter C. The difference between this value and the sum of litter-derived particulate and mineral-associated SOC was considered the residual pool which we assume mostly represents water-extractable dissolved organic matter.Microbial biomass dynamics during incubationSample batches were harvested at days 15 and 100 to capture early- and intermediate-term microbial biomass responses to litter treatments. These times were selected to correspond with the middle of early and intermediate C budget microcosm incubations. We quantified microbial biomass as well as MGR, CUE, and MTR using 18O-labeled water73,74 as in Geyer et al.75.Microbial biomass C (MBC) was determined on two ~2 g subsamples using a standard chloroform fumigation extraction76. One subsample was immediately extracted in 0.5 M K2SO4 and one was fumigated for 72 h before extraction. After shaking for 1 h, extracts were gravity filtered through a Whatman No. 40 filter paper, and filtrates were analyzed for total organic C using the method of Bartlett and Ross77 as adapted by Giasson et al.78. The difference between total organic C in the fumigated and unfumigated subsamples was used to calculate MBC (extraction efficiency KEC = 0.45).To determine MGR, CUE, and MTR, we first pre-incubated two 0.5 g soil subsamples (one treatment and one control) for 2 d at 24 °C. Prior to this pre-incubation, samples were allowed to evaporate down to 53 ± 6% (mean, sd) water-holding capacity. After the pre-incubation, water was injected with a 25 µL syringe to bring each sample to 65% water-holding capacity. For one subsample, we used unlabeled deionized water. For the second subsample, enriched 18O-water (98.1 at%; ICON Isotopes) was mixed with unlabeled deionized H2O to achieve approximately 20 at% of 18O in the final soil water. Each sample was placed in a centrifuge tube (modified for gas sampling), flushed with CO2-free air, and incubated for 24 h. Headspace CO2 concentration was then measured, and samples were flash frozen in liquid N2 and stored at −80 °C until DNA extraction.DNA was extracted from each sample using a DNA extraction kit (Qiagen DNeasy PowerSoil Kit, Venlo, Netherlands) following the protocol described in Geyer et al. (2019) which sought to maximize the recovery of DNA. The DNA concentration was determined fluorometrically using a Quant-iT PicoGreen dsDNA Assay Kit (Invitrogen). DNA extracts (80 µL) were dried at 60 °C in silver capsules spiked with 100 µL of salmon sperm DNA (42.5 ng µL−1), to reach the oxygen detection limit, and sent to the UC Davis Stable Isotope Facility for quantification of δ18O and total O content.Microbial growth rate (MGR) was calculated following Geyer et al. (2019). Specifically, atom % of soil DNA O (at% ODNA) was determined using the two-pool mixing model:$${at} % ,{O}_{{DNA}}=,frac{left[left({at} % ,{O}_{{DN}A+{ss}}times {O}_{{DNA}+{ss}}right)-left({at} % ,{O}_{{ss}}times {O}_{{ss}}right)right]}{{O}_{{DNA}}},$$
(3)
where at% is the atom % 18O and ODNA+ss, ODNA, and Oss are the concentration of O in the whole sample, soil DNA, and salmon sperm, respectively. Atom percent excess of soil DNA oxygen (APE Osoil) was calculated as the difference between at% ODNA in the treatment and control samples. Total microbial growth in terms of O (Total O; µg) was estimated as:$${Total},O=frac{{O}_{{soil}}times ,{{APE},O}_{{soil}}}{{at} % ,{soil},{water}}$$
(4)
where at% soil water is the atom % 18O in the soil water. MGR in terms of C (µg C g−1 soil d−1) was calculated by applying conversion mass ratios of oxygen:DNA (0.31) and MBC:DNA for each sample, dividing by the soil mass, and scaling by the incubation time. Assuming uptake rate (Uptake) is equal to the sum of MGR and respiration, CUE and MTR were calculated by the following equations.$${CUE}=,frac{{MGR}}{{Uptake}},$$
(5)
$${MTR}=,frac{{MGR}}{{MBC}}$$
(6)
Data analysis for microcosm experimentLitter decay constants were calculated for each species using litter-derived CO2-C values to estimate litter mass loss over time. After it was determined that a single exponential decay model provided a poor fit, we fit litter decomposition data using the double exponential decay model:$$y=s{e}^{{-k}_{1}t}+(1-s){e}^{{-k}_{2}t}$$
(7)
where s represents the labile or early stage decomposition fraction that decomposes at rate k1, and k2 is the decay constant for the remaining late stage decomposition fraction.To reduce the dimensionality of litter quality and microbial indicators, indices were derived by principal component analysis (PCA; Fig. S1A, B) using the ‘prcomp’ function in R. The first axis of a PCA of decomposition parameters (s, k1, and k2) and litter chemical properties (soluble and AUR contents; AUR-to-N and C-to-N ratios; and the lignocellulose index [LCI]) was taken as a litter quality index. Whereas this index highly correlated with indicators of C quality (AUR, soluble content, and LCI), the second axis of this PCA correlated with C:N and AUR:N and was therefore taken as a second litter quality index representing variation in N concentration. The first axis of a PCA of MGR, CUE, and MTR was taken as a microbial physiological trait index.Bivariate relationships were examined using simple linear regressions on average species values at each harvest (n = 16). To examine relationships between microbial physiological traits and mineral-associated SOC, data from the early-term (day 15) and intermediate-term (day 100) microbial harvest were matched with early-term (day 30) and intermediate-term (day 185) C budget microcosms, respectively. In addition to examining total mineral-associated SOC formation, we also estimated the efficiency of litter C transfer into the mineral-associated SOC pool as the fraction of lost litter C (i.e., litter C lost as CO2, recovered in the mineral-associated SOC fraction, or in the residual pool) retained in the mineral-associated SOC. Path analyses were used to evaluate the hypothesis that microbial physiological traits mediate the effect of litter quality on mineral-associated SOC formation and mineral-associated and particulate SOC decay. We hypothesized that the litter quality index would be positively associated with the microbial physiological trait index (representing faster and more efficient microbial growth) and microbial physiological traits would, in turn, be positively associated with the rate and efficiency of mineral-associated SOC formation. We expected that this mediating pathway would reduce the direct relationship between litter quality and SOC. This analysis was conducted using the LAVAAN package79 to run path analyses for total litter-derived mineral-associated SOC, mineral-associated SOC formation efficiency, and soil-derived mineral-associated and particulate SOC for both early and intermediate stage harvests. All analyses were performed using R version 3.5.2.Field study design and soil samplingWe worked in the Smithsonian’s Forest Global Earth Observatory (ForestGEO) network80 in six mature U.S. temperate forests varying in climate, soil properties, and tree community composition (Fig. 1a): Harvard forest (HF; 42°32′N, 72°11′W) in North-Central Massachusetts, Lilly-Dickey Woods (LDW; 39°14’N, 86°13’W) in South-Central Indiana, the Smithsonian Conservation Biology Institute (SCBI; 38°54′N, 78°9′W) in Northern Virginia, the Smithsonian Environmental Research Center (SERC; 38°53′N, 76°34′W) on the Chesapeake Bay in Maryland, Tyson Research Center (TRC; 38°31′N, 90°33′W) in Eastern Missouri, and Wabikon Lake Forest (WLF; 45°33′N, 88°48′W) in Northern Wisconsin, USA. Land use history across the six sites consisted mostly of timber harvesting which ceased in the early 1900s. Soils are mostly Oxyaquic Dystrudepts at HF, Typic Dystrudepts and Typic Hapludults at LDW, Typic Hapludalfs at SCBI, Typic or Aquic Hapludults at SERC, Typic Hapludalfs and Typic Paleudalfs at TRC, and Typic and Alfic Haplorthods at WLF. Further site details are reported in Table S5.Each site contains a rich assemblage of co-occurring arbuscular mycorrhizal (AM)- and ectomycorrhizal (ECM)-associated trees (Table S6), which we leveraged to generate environmental gradients in factors hypothesized to predict microbial physiological traits within each site. Specifically, the dominance of AM vs. ECM trees within a temperate forest plot has been shown to be a strong predictor of soil pH, C:N, inorganic N availability, and litter quality52,53,54. We established nine 20 × 20 m plots in each of our six sites in Fall 2016 (n = 54) distributed along a gradient of AM- to ECM-associated tree dominance. Plots were selected to avoid obvious confounding environmental factors. Where possible, we established our nine-plot gradient in three blocks (5 cm) at HF, which was removed before coring. Samples were also collected at 5–15 cm depth for soil texture analysis. We sampled from an inner 10 × 10 m square in each plot to avoid edge effects. All samples from the same plot were composited, sieved (2 mm), picked free of roots, subsampled for gravimetric moisture (105 °C), and air-dried, or refrigerated (4 °C) until analysis for microbial physiological variables and N availability.Soil propertiesWe determined several physicochemical properties known to predict mineral-associated SOC. We measured soil pH (8:1 ml 0.01 M CaCl2:g soil) and soil texture using a benchtop pH meter and a standard hydrometer procedure82, respectively. Organic matter content was high in some upper surface soils, so plot-level soil texture was determined from 5 to 15 cm depth samples. We quantified oxalate-extractable Al and Fe pools (Alox and Feox) in all soil samples as an index of poorly crystalline Al- and Fe-oxides83, which is one of the strongest predictors of SOM content in temperate forests84. Briefly, we extracted 0.40 g dried, ground soil in 40 mL 0.2 M NH4-oxalate at pH 3.0 in the dark for 4 h before gravity filtering and refrigerating until analysis (within 2 w) on an atomic-adsorption spectrometer (Aanalyst 800, Perkin Elmer, Waltham, MA, USA), using an acetylene flame and a graphite furnace for the atomization of Fe and Al, respectively.We quantified potential net N mineralization rates as an index of soil N availability. One 5 g subsample per plot was extracted immediately after processing by adding 10 mL 2 M KCl, shaking for 1 h, and filtering through a Whatman No. 1 filter paper after centrifugation at 3000 rpm. A second subsample from each plot was incubated under aerobic conditions at field moisture and 23 °C for 14 d before extraction. Extracts were frozen (−20 °C) until analysis for NH4+-N using the salicylate method and for NO3−-N plus NO2−-N after a cadmium column reduction on a Lachat QuikChem 8000 flow Injection Analyzer (Lachat Instruments, Loveland, CO, USA). Potential net N mineralization rates (mg N g dry soil−1 d−1) were calculated as the difference between pre- and post-incubation inorganic N concentrations.Microbial biomass dynamics in field plotsMicrobial biomass carbon and microbial physiological traits were quantified within 10 days of collection as described above, with four minor differences. First, 30 g soil subsamples were covered with parafilm and pre-incubated for 2 d near the field soil temperature measured at the time of sampling (16.5 °C for WLF and HF, and 21.5 °C for LDW, TRC, SCBI, and SERC). Second, for CO2 analysis, samples were placed in a 61 mL serum vial crimped with a rubber septum. Third, DNA concentrations were determined using a Qubit dsDNA BR Assay Kit (Life Technologies) and a Qubit 3.0 fluorometer (Life Technologies). Fourth, 14.5 g subsamples were used for microbial biomass analysis.Soil organic matter characterization in field plotsMineral-associated SOC was quantified as in the microcosm experiment, but without a pre-fractionation leachate removal step. We additionally measured soil amino sugar concentrations to estimate microbial necromass contributions to SOM. Amino sugars are useful microbial biomarkers because they are found in abundance in microbial cell walls, but are not produced by higher plants and soil animals19. Moreover, amino sugars can provide information on the microbial source of necromass. For example, glucosamine (Glu) is produced mostly by fungi whereas muramic acid (MurA) is produced almost exclusively by bacteria61,85. Amino sugars were extracted, purified, converted to aldononitrile acetates, and quantified with myo-inositol as in Liang et al.86. We used the concentrations of Glu and MurA to estimate total, fungal, and bacterial necromass soil C using the empirical relationships reported in Liang et al.8.$${Bacterial},{necromass},C,=,{MurA},times ,45$$
(8)
$${Fungal},{necromass},C,=,({mmol},{GluN},{-},2,times ,{mmol},{MurA})times ,179.17,times ,9$$
(9)
Leaf litter and fine roots in field plotsIn Fall 2017, we collected leaf litter on two sample dates from four baskets deployed in the inner 10 × 10 m of each plot. Litter was composited by plot, dried (60 °C), sorted by species, weighed, and ground. We performed leaf litter analyses on at least three samples of each species at each site —unless a species was only present in one or two plots— to get a site-specific mean for each species. Some non-dominant species were not included in these analyses because an insufficient amount of material was collected. Fine roots ( 0.5). Feox and Alox were correlated above this threshold and final models were selected to contain only Feox based on AIC. Residuals were screened for normality (Shapiro-Wilk), heteroscedasticity (visual assessment of residual plots), and influential observations (Cook’s D). Based on this, MGR, MTR, and mineral-associated SOC were natural log transformed. For all mixed models, we centered and standardized predictors (i.e., z-transformation) so that the slopes and significance levels of different predictors could be compared to one another on the same axis88. More

Marshall, C. R. Explaining the Cambrian “explosion” of animals. Annu. Rev. Earth Planet. Sci. 34, 355–384 (2016).ADS 

Google Scholar 
Bush, A. M. & Bambach, R. K. Paleoecologic megatrends in marine metazoa. Annu. Rev. Earth Planet. Sci. 39, 241–269 (2011).ADS 
CAS 

Google Scholar 
Smith, M. P. & Harper, D. A. Causes of the Cambrian explosion. Science 341(6152), 1355–1356 (2013).ADS 
PubMed 

Google Scholar 
Daley, A. C., Antcliffe, J. B., Drage, H. B. & Pates, S. Early fossil record of Euarthropoda and the Cambrian explosion. Proc. Natl. Acad. Sci. 115(21), 5323–5331 (2018).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Fu, D. et al. The Qingjiang biota—A Burgess Shale–type fossil Lagerstätte from the early Cambrian of South China. Science 363(6433), 1338–1342 (2019).ADS 
CAS 
PubMed 

Google Scholar 
Nanglu, K., Caron, J. B. & Gaines, R. R. The Burgess Shale paleocommunity with new insights from Marble Canyon, British Columbia. Paleobiology 46(1), 58–81 (2020).
Google Scholar 
Sepkoski, J. J. Jr. The Ordovician radiations: Diversification and extinction shown by global genus-level taxonomic data. In Ordovician Odyssey: Short Papers, 7th International Symposium on the Ordovician System (eds Cooper, J. D. et al.) 393–396 (Pacific Section Society for Sedimentary Geology (SEPM), 1995).
Google Scholar 
Servais, T., Cascales-Miñana, B. & Harper, D. A. The Great Ordovician Biodiversification event (GOBE) is not a single event. Paleontol. Res. 25(4), 315–328 (2021).
Google Scholar 
Harper, D. A., Cascales-Miñana, B., Kroeck, D. M. & Servais, T. The palaeogeographical impact on the biodiversity of marine faunas during the Ordovician radiations. Glob. Planet. Change 207, 103665 (2021).
Google Scholar 
Harper, D. A. et al. The Furongian (late Cambrian) biodiversity gap: Real or apparent?. Palaeoworld 28(1–2), 4–12 (2019).
Google Scholar 
Saleh, F. et al. Taphonomic bias in exceptionally preserved biotas. Earth Planet. Sci. Lett. 529, 115873 (2020).CAS 

Google Scholar 
Saleh, F. et al. A novel tool to untangle the ecology and fossil preservation knot in exceptionally preserved biotas. Earth Planet. Sci. Lett. 569, 117061 (2021).CAS 

Google Scholar 
Vizcaïno, D. & Lefebvre, B. Les échinodermes du Paléozoïqueinférieur de Montagne Noire: Biostratigraphie et paléodiversité. Geobios 32(2), 353–364 (1995).
Google Scholar 
Vizcaïno, D. & Álvaro, J. J. Adequacy of the Early Ordovician trilobite record in the southern Montagne Noire (France): Biases for biodiversity documentation. Earth Environ. Sci. Trans. R. Soc. Edinb. 93(4), 393–401 (2002).
Google Scholar 
Lefebvre, B. et al. Palaeoecological aspects of the diversification of echinoderms in the Lower Ordovician of central Anti-Atlas, Morocco. Palaeogeogr. Palaeoclimatol. Palaeoecol. 460, 97–121 (2016).
Google Scholar 
Lefebvre, B. et al. Exceptionally preserved soft parts in fossils from the Lower Ordovician of Morocco clarify stylophoran affinities within basal deuterostomes. Geobios 52, 27–36 (2019).
Google Scholar 
Martin, E. L. O. et al. Biostratigraphic and palaeoenvironmental controls on the trilobite associations from the Lower Ordovician Fezouata Shale of the central Anti-Atlas, Morocco. Palaeogeogr. Palaeoclimatol. Palaeoecol. 460, 142–154 (2016).
Google Scholar 
Waisfeld, B. G. & Balseiro, D. Decoupling of local and regional dominance in trilobite assemblages from northwestern Argentina: New insights into Cambro-Ordovician ecological changes. Lethaia 49(3), 379–392 (2016).
Google Scholar 
Serra, F., Balseiro, D. & Waisfeld, B. G. Diversity patterns in upper Cambrian to Lower Ordovician trilobite communities of north-western Argentina. Palaeontology 62(4), 677–695 (2019).
Google Scholar 
Serra, F., Balseiro, D., Vaucher, R. & Waisfeld, B. G. Structure of trilobite communities along a delta-marine gradient (lower Ordovician; Northwestern Argentina). Palaios 36(2), 39–52 (2021).ADS 

Google Scholar 
Saleh, F., Lefebvre, B., Hunter, A. W. & Nohejlová, M. Fossil weathering and preparation mimic soft tissues in eocrinoid and somasteroid echinoderms from the Lower Ordovician of Morocco. Microsc. Today 28(1), 24–28 (2020).
Google Scholar 
Saleh, F. et al. Insights into soft-part preservation from the Early Ordovician Fezouata Biota. Earth Sci. Rev. 213, 103464 (2021).
Google Scholar 
Saleh, F. et al. Large trilobites in a stress-free Early Ordovician environment. Geol. Mag. 158(2), 261–270 (2021).ADS 

Google Scholar 
Vilmi, A. et al. Dispersal–niche continuum index: A new quantitative metric for assessing the relative importance of dispersal versus niche processes in community assembly. Ecography 44(3), 370–379 (2021).
Google Scholar 
Hubbell, S. P. A Unified Theory of Biodiversity and Biogeography (Princeton University Press, 2001).
Google Scholar 
Gravel, D., Canham, C. D., Beaudet, M. & Messier, C. Reconciling niche and neutrality: The continuum hypothesis. Ecol. Lett. 9(4), 399–409 (2006).PubMed 

Google Scholar 
Bergström, S. M., Chen, X., Gutiérrez-Marco, J. C. & Dronov, A. The new chronostratigraphic classification of the Ordovician system and its relations to major regional series and stages and to δ13C chemostratigraphy. Lethaia 42, 97–107 (2009).
Google Scholar 
Lefebvre, B. et al. Age calibration of the Lower Ordovician Fezouata Lagerstätte, Morocco. Lethaia 51(2), 296–311 (2018).
Google Scholar 
Servais, T. et al. The onset of the ‘Ordovician Plankton Revolution’in the late Cambrian. Palaeogeogr. Palaeoclimatol. Palaeoecol. 458, 12–28 (2016).
Google Scholar 
Lee, J. H. & Riding, R. Marine oxygenation, lithistid sponges, and the early history of Paleozoic skeletal reefs. Earth Sci. Rev. 181, 98–121 (2018).ADS 
CAS 

Google Scholar 
Servais, T., Danelian, T., Harper, D. A. T. & Munnecke, A. Possible oceanic circulation patterns, surface water currents and upwelling zones in the Early Palaeozoic. GFF 136(1), 229–233 (2014).
Google Scholar 
Rasmussen, C. M. et al. Onset of main Phanerozoic marine radiation sparked by emerging Mid Ordovician icehouse. Sci. Rep. 6(1), 1–9 (2016).
Google Scholar 
Edwards, C. T. Links between early Paleozoic oxygenation and the Great Ordovician Biodiversification Event (GOBE): A review. Palaeoworld 28(1–2), 37–50 (2019).
Google Scholar 
Buatois, L. A. et al. Quantifying ecospace utilization and ecosystem engineering during the early Phanerozoic—The role of bioturbation and bioerosion. Sci. Adv. 6(33), eabb0618 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Mángano, M. G. et al. Were all trilobites fully marine? Trilobite expansion into brackish water during the early Palaeozoic. Proc. R. Soc. B 288(1944), 20202263 (2021).PubMed 
PubMed Central 

Google Scholar 
Park, T. Y. S. et al. Ontogeny of the Furongian (late Cambrian) trilobite Proceratopyge cf. P. Lata Whitehouse from northern Victoria Land, Antarctica, and the evolution of metamorphosis in trilobites. Palaeontology 59(5), 657–670 (2016).
Google Scholar 
Laibl, L. & Fatka, O. Review of early developmental stages of trilobites and agnostids from the Barrandian area (Czech Republic). J. Natl. Mus. (Prague) Nat. Hist. Ser. 186(1), 103–112 (2017).
Google Scholar 
Laibl, L., Cederström, P. & Ahlberg, P. Early post-embryonic development in Ellipsostrenua (Trilobita, Cambrian, Sweden) and the developmental patterns in Ellipsocephaloidea. J. Paleontol. 92(6), 1018–1027 (2018).
Google Scholar 
Laibl, L., Maletz, J. & Olschewski, P. Post-embryonic development of Fritzolenellus suggests the ancestral morphology of the early developmental stages in Trilobita. Pap. Palaeontol. 7(2), 839–859 (2021).
Google Scholar 
Chatterton, B. D. E. & Speyer, S. E. Ontogeny in Treatise on Invertebrate Paleontology. Part O, Arthropoda 1, Trilobita 1, revised. 7–11 (Geological Society of America and University of Kansas Press, Lawrence, 1997).Bignon, A., Waisfeld, B. G., Vaccari, N. E. & Chatterton, B. D. Reassessment of the order Trinucleida (Trilobita). J. Syst. Palaeontol. 18(13), 1061–1077 (2020).
Google Scholar 
Torsvik, T. H. & Cocks, L. R. M. The Palaeozoic palaeogeography of central Gondwana. Geol. Soc. Lond. Spec. Publ. 357(1), 137–166 (2011).ADS 

Google Scholar 
Torsvik, T. H. & Cocks, L. R. M. New global palaeogeographical reconstructions for the early Palaeozoic and their generation. Geol. Soc. Lond. Mem. 38(1), 5–24 (2013).
Google Scholar 
Bahlburg, H., Moya, M. C. & Zeil, W. Geodynamic evolution of the early Palaeozoic continental margin of Gondwana in the Southern Central Andes of Northwestern Argentina and Northern Chile. In Tectonics of the Southern Central Andes. 293–302 (Springer, 1994).McEdward, L. R. & Miner, B. G. Larval and life-cycle patterns in echinoderms. Can. J. Zool. 79(7), 1125–1170 (2001).
Google Scholar 
Lefebvre, B. et al. Palaeobiogeography of Ordovician echinoderms. Geol. Soc. Lond. Mem. 38(1), 173–198 (2013).
Google Scholar 
Signor, P. W. & Vermeij, G. J. The plankton and the benthos: Origins and early history of an evolving relationship. Paleobiology 20, 297–319 (1994).
Google Scholar 
Davis, M. A., Grime, J. P. & Thompson, K. Fluctuating resources in plant communities: A general theory of invasibility. J. Ecol. 88(3), 528–534 (2000).
Google Scholar 
Franeck, F. Perspectives on the Great Ordovician Biodiversification Event-local to global patterns (2020).Pulliam, H. R. Sources, sinks, and population regulation. Am. Nat. 132(5), 652–661 (1988).
Google Scholar 
Kröger, B., Franeck, F. & Rasmussen, C. M. The evolutionary dynamics of the early Palaeozoic marine biodiversity accumulation. Proc. R. Soc. B 286(1909), 20191634 (2019).PubMed 
PubMed Central 

Google Scholar 
Penny, A. & Kröger, B. Impacts of spatial and environmental differentiation on early Palaeozoic marine biodiversity. Nat. Ecol. Evol. 3(12), 1655–1660 (2019).PubMed 

Google Scholar 
Rasmussen, C. M., Kröger, B., Nielsen, M. L. & Colmenar, J. Cascading trend of Early Paleozoic marine radiations paused by Late Ordovician extinctions. Proc. Natl. Acad. Sci. 116(15), 7207–7213 (2019).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Stigall, A. L. The invasion hierarchy: Ecological and evolutionary consequences of invasions in the fossil record. Annu. Rev. Ecol. Evol. Syst. 50, 355–380 (2019).
Google Scholar 
Stigall, A. L., Edwards, C. T., Freeman, R. L. & Rasmussen, C. M. Coordinated biotic and abiotic change during the Great Ordovician Biodiversification Event: Darriwilian assembly of early Paleozoic building blocks. Palaeogeogr. Palaeoclimatol. Palaeoecol. 530, 249–270 (2019).
Google Scholar 
Stigall, A. L. How is biodiversity produced? Examining speciation processes during the GOBE. Lethaia 51(2), 165–172 (2018).
Google Scholar 
Servais, T. & Harper, D. A. T. The great Ordovician biodiversification event (GOBE): Definition, concept and duration. Lethaia 51(2), 151–164 (2018).
Google Scholar 
Trotter, J. A. et al. Did cooling oceans trigger Ordovician biodiversification? Evidence from conodont thermometry. Science 321(5888), 550–554 (2008).ADS 
CAS 
PubMed 

Google Scholar 
Vizcaïno, D., Álvaro, J. J. & Lefebvre, B. The lower Ordovician of the southern Montagne Noire. Ann. Soc. Géol. Nord 8(4), 213–220 (2001).
Google Scholar 
Hsieh, T. C., Ma, K. H. & Chao, A. iNEXT: An R package for rarefaction and extrapolation of species diversity (H ill numbers). Methods Ecol. Evol. 7(12), 1451–1456 (2016).
Google Scholar 
Suchéras-Marx, B., Escarguel, G., Ferreira, J. & Hammer, Ø. Statistical confidence intervals for relative abundances and abundance-based ratios: Simple practical solutions for an old overlooked question. Mar. Micropaleontol. 151, 101751 (2019).ADS 

Google Scholar 
Hammer, Ø., Harper, D. A. T. & Ryan, P. D. PAST-palaeontological statistics, ver. 1.89. Palaeontol. Electron 4(1), 1–9 (2001).
Google Scholar 
Gibert, C. & Escarguel, G. PER-SIMPER—A new tool for inferring community assembly processes from taxon occurrences. Glob. Ecol. Biogeogr. 28(3), 374–385 (2019).
Google Scholar 
Gibert, C. DNCImper: Assembly Process Identification Based on SIMPER Analysis. R package ver. 0.0.1.0000. https://github.com/Corentin-Gibert-Paleontology/DNCImper (2019). More

Foelix, R. F. Biology of Spiders (Oxford University Press, 2011).
Google Scholar 
World Spider Catalog. World Spider Catalog, Version 23.0. Natural History Museum Bern, online at http://wsc.nmbe.ch (2022).Nyffeler, M. & Sunderland, K. D. Composition, abundance and pest control potential of spider communities in agroecosystems: A comparison of European and US studies. Agric. Ecosyst. Environ. 95, 579–612 (2003).
Google Scholar 
Oldrati, V. et al. Peptidomic and transcriptomic profiling of four distinct spider venoms. PLoS ONE 12, e0172966 (2017).PubMed 
PubMed Central 

Google Scholar 
Herzig, V. et al. Animal toxins—Nature’s evolutionary-refined toolkit for basic research and drug discovery. Biochem. Pharmacol. 181, 114096 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Vollrath, F. & Knight, D. P. Liquid crystalline spinning of spider silk. Nature 410, 541–548 (2001).ADS 
CAS 
PubMed 

Google Scholar 
Moradmand, M. & Jäger, P. Taxonomic revision of the huntsman spider genus Eusparassus Simon, 1903 (Araneae: Sparassidae) in Eurasia. J. Nat. Hist. 46, 2439–2496 (2012).
Google Scholar 
Moradmand, M. The stone huntsman spider genus Eusparassus (Araneae: Sparassidae): Systematics and zoogeography with revision of the African and Arabian species. Zootaxa 3675, 1–108 (2013).PubMed 

Google Scholar 
Levy, G. The family of huntsman spiders in Israel with annotations on species of the Middle East (Araneae: Sparassidae). J. Zool. 217, 127–176 (1989).
Google Scholar 
Dunlop, J. A. et al. Computed tomography recovers data from historical amber: An example from huntsman spiders. Naturwissenschaften 98, 519–527 (2011).ADS 
CAS 
PubMed 

Google Scholar 
Moradmand, M., Schönhofer, A. L. & Jäger, P. Molecular phylogeny of the spider family Sparassidae with focus on the genus Eusparassus and notes on the RTA-clade and ‘Laterigradae’. Mol. Phylogenet. Evol. 74, 48–65 (2014).CAS 
PubMed 

Google Scholar 
Hutchinson, G. E. Cold spring harbor symposium on quantitative biology. Concl. Remarks 22, 415–427 (1957).
Google Scholar 
Pearman, P. B., Guisan, A., Broennimann, O. & Randin, C. F. Niche dynamics in space and time. Trends Ecol. Evol. 23, 149–158 (2008).PubMed 

Google Scholar 
Wake, D. B., Hadly, E. A. & Ackerlya, D. D. Biogeography, changing climates, and niche evolution. Proc. Natl. Acad. Sci. U. S. A. 106, 19631–19636 (2009).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Smith, A. B., Godsoe, W., Rodríguez-Sánchez, F., Wang, H. H. & Warren, D. Niche estimation above and below the species level. Trends Ecol. Evol. 34, 260–273 (2019).PubMed 

Google Scholar 
Peñalver-Alcázar, M., Jiménez-Valverde, A. & Aragón, P. Niche differentiation between deeply divergent phylogenetic lineages of an endemic newt: implications for Species Distribution Models. Zoology 144, 125852 (2021).PubMed 

Google Scholar 
Di Pasquale, G. et al. Coastal Pine-Oak Glacial Refugia in the mediterranean basin: A biogeographic approach based on charcoal analysis and spatial modelling. Forests 11, 673 (2020).
Google Scholar 
Du, Z., He, Y., Wang, H., Wang, C. & Duan, Y. Potential geographical distribution and habitat shift of the genus Ammopiptanthus in China under current and future climate change based on the MaxEnt model. J. Arid Environ. 184, 104328 (2021).ADS 

Google Scholar 
Kafash, A. et al. The Gray Toad-headed Agama, Phrynocephalus scutellatus, on the Iranian Plateau: The degree of niche overlap depends on the phylogenetic distance. Zool. Middle East 64, 47–54 (2018).
Google Scholar 
Namyatova, A. A. Climatic niche comparison between closely related trans-Palearctic species of the genus Orthocephalus (Insecta: Heteroptera: Miridae: Orthotylinae). PeerJ 8, e10517 (2020).PubMed 
PubMed Central 

Google Scholar 
Zhang, Z. et al. Lineage-level distribution models lead to more realistic climate change predictions for a threatened crayfish. Divers. Distrib. 27, 684–695 (2021).
Google Scholar 
Mammola, S. & Leroy, B. Applying species distribution models to caves and other subterranean habitats. Ecography (Cop.) 41, 1194–1208 (2018).
Google Scholar 
Mammola, S. et al. Challenges and opportunities of species distribution modelling of terrestrial arthropod predators. Divers. Distrib. 00, 1–19 (2021).
Google Scholar 
Saupe, E. E., Papes, M., Selden, P. A. & Vetter, R. S. Tracking a medically important spider: Climate change, ecological niche modeling, and the brown recluse (Loxosceles reclusa). PLoS ONE 6, 2 (2011).
Google Scholar 
Planas, E., Saupe, E. E., Lima-Ribeiro, M. S., Peterson, A. T. & Ribera, C. Ecological niche and phylogeography elucidate complex biogeographic patterns in Loxosceles rufescens (Araneae, Sicariidae) in the Mediterranean Basin. BMC Evol. Biol. https://doi.org/10.1186/s12862-014-0195-y (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Taucare-Ríos, A., Nentwig, W., Bizama, G. & Bustamante, R. O. Matching global and regional distribution models of the recluse spider Loxosceles rufescens: to what extent do these reflect niche conservatism?. Med. Vet. Entomol. 32, 490–496 (2018).PubMed 

Google Scholar 
Wang, Y., Casajus, N., Buddle, C., Berteaux, D. & Larrivée, M. Predicting the distribution of poorly-documented species, Northern black widow (Latrodectus variolus) and Black purse-web spider (Sphodros Niger), using museum specimens and citizen science data. PLoS ONE 13, e0201094 (2018).PubMed 
PubMed Central 

Google Scholar 
Jiménez-Valverde, A., Decae, A. E. & Arnedo, M. A. Environmental suitability of new reported localities of the funnelweb spider Macrothele calpeiana: An assessment using potential distribution modelling with presence-only techniques. J. Biogeogr. 38, 1213–1223 (2011).
Google Scholar 
Monsimet, J., Devineau, O., Pétillon, J. & Lafage, D. Explicit integration of dispersal-related metrics improves predictions of SDM in predatory arthropods. Sci. Rep. https://doi.org/10.1038/s41598-020-73262-2 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Salgado-Roa, F. C., Gamez, A., Sanchez-Herrera, M., Pardo-Diaz, C. & Salazar, C. Divergence promoted by the northern Andes in the giant fishing spider Ancylometes bogotensis (Araneae: Ctenidae). Biol. J. Linn. Soc. 132, 495–508 (2021).
Google Scholar 
Mammola, S., Goodacre, S. L. & Isaia, M. Climate change may drive cave spiders to extinction. Ecography (Cop.) 41, 233–243 (2018).
Google Scholar 
Ferretti, N. E., Soresi, D. S., González, A. & Arnedo, M. An integrative approach unveils speciation within the threatened spider Calathotarsus simoni (Araneae: Mygalomorphae: Migidae). Syst. Biodivers. 17, 439–457 (2019).
Google Scholar 
Pavlek, M. & Mammola, S. Niche-based processes explaining the distributions of closely related subterranean spiders. J. Biogeogr. 48, 118–133 (2021).
Google Scholar 
Bosso, L. et al. Nature protection areas of Europe are insufficient to preserve the threatened beetle Rosalia alpina (Coleoptera: Cerambycidae): evidence from species distribution models and conservation gap analysis. Ecol. Entomol. 43, 192–203 (2018).
Google Scholar 
Kafash, A. et al. Climate change produces winners and losers: Differential responses of amphibians in mountain forests of the Near East. Glob. Ecol. Conserv. 16, e00471 (2018).
Google Scholar 
Vásquez-Aguilar, A. A., Ornelas, J. F., Rodríguez-Gómez, F. & Cristina MacSwiney, G. Modeling future potential distribution of buff-bellied hummingbird (Amazilia yucatanensis) under climate change: species vs subspecies. Trop. Conserv. Sci. 25, 2 (2021).
Google Scholar 
Rosauer, D. F., Catullo, R. A., VanDerWal, J., Moussalli, A. & Moritz, C. Lineage range estimation method reveals fine-scale endemism linked to pleistocene stability in Australian rainforest herpetofauna. PLoS ONE 10, e0126274 (2015).PubMed 
PubMed Central 

Google Scholar 
Eyres, A., Eronen, J. T., Hagen, O., Böhning-Gaese, K. & Fritz, S. A. Climatic effects on niche evolution in a passerine bird clade depend on paleoclimate reconstruction method. Evolution 75, 1046–1060 (2021).PubMed 

Google Scholar 
Loyola, R. D., Lemes, P., Brum, F. T., Provete, D. B. & Duarte, L. D. S. Clade-specific consequences of climate change to amphibians in Atlantic Forest protected areas. Ecography (Cop.) 37, 65–72 (2014).
Google Scholar 
Muñoz, M. M. & Bodensteiner, B. L. Janzen’s hypothesis meets the bogert effect: Connecting climate variation, thermoregulatory behavior, and rates of physiological evolution. Integr. Org. Biol. 1, 1–12 (2019).
Google Scholar 
Entling, W., Schmidt, M. H., Bacher, S., Brandl, R. & Nentwig, W. Niche properties of Central European spiders: Shading, moisture and the evolution of the habitat niche. Glob. Ecol. Biogeogr. 16, 440–448 (2007).
Google Scholar 
Lafage, D., Maugenest, S., Bouzillé, J. B. & Pétillon, J. Disentangling the influence of local and landscape factors on alpha and beta diversities: opposite response of plants and ground-dwelling arthropods in wet meadows. Ecol. Res. 30, 1025–1035 (2015).
Google Scholar 
Peterson, A. T., Soberón, J. & Sánchez-Cordero, V. Conservatism of ecological niches in evolutionary time. Science 285, 1265–1267 (1999).CAS 
PubMed 

Google Scholar 
Wellenreuther, M., Larson, K. W. & Svensson, E. I. Climatic niche divergence or conservatism? Environmental niches and range limits in ecologically similar damselflies. Ecology 93, 1353–1366 (2012).PubMed 

Google Scholar 
Nosil, P. & Sandoval, C. P. Ecological niche dimensionality and the evolutionary diversification of stick insects. PLoS ONE 3, e1907 (2008).ADS 
PubMed 
PubMed Central 

Google Scholar 
McCormack, J. E., Zellmer, A. J. & Knowles, L. L. Does niche divergence accompany allopatric divergence in Aphelocoma jays as predicted under ecological speciation?: Insights from tests with niche models. Evolution 64, 1231–1244 (2010).PubMed 

Google Scholar 
Goudarzi, F., Hemami, M. R., Malekian, M. & Fakheran-Esfahani, S. Ecological Characterization of the breeding habitat of Luristan newt (Neurergus kaiseri) at local scale. J. Nat. Environ. 72, 113–127 (2019).
Google Scholar 
Chase, J. M. & Leibold, M. Ecological Niches: Linking Classical and Contemporary Approaches (University of Chicago Press, 2003).
Google Scholar 
Bonte, D., Vandenbroecke, N., Lens, L. & Maelfait, J. P. Low propensity for aerial dispersal in specialist spiders from fragmented landscapes. Proc. R. Soc. B Biol. Sci. 270, 1601–1607 (2003).
Google Scholar 
GBIF.org. GBIF Occurrence Download. https://doi.org/10.15468/dl.2tc2ja (2021) doi:https://doi.org/10.15468/dl.2tc2ja.Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
Google Scholar 
Jarvis, A., Reuter, H. I., Nelson, A. & Guevara, E. Hole-Filled SRTM for the Globe Version 4. Available from the CGIAR-CSI SRTM 90m Database. (2008) doi:https ://srtm.csi.cgiar .org.Hijmans, R. J. raster: Geographic Data Analysis and Modeling. R package version 3, 3–7 (2020).
Google Scholar 
Guisan, A., Thuiller, W. & Zimmermann, N. E. Habitat suitability and distribution models: With applications in R. (2017). doi:10.1017/ 9781139028271.Quinn, G. P. & Keough, M. J. Experimental Design and Data Analysis for Biologists (Cambridge University Press, 2002).
Google Scholar 
Naimi, B. Uncertainty Analysis for Species Distribution Models. R package version (2015).Phillips, S. J., Dudík, M. & Schapire, R. E. Maxent software for modeling species niches and distributions (Version 3.4.1). Available from url: http://biodiversityinformatics.amnh.org/open_source/maxent/. Accessed on 2022–2–12.Nǎpǎruş, M. & Kuntner, M. A GIS model predicting potential distributions of a lineage: a test case on hermit spiders (Nephilidae: Nephilengys). PLoS ONE 7, e30047 (2012).ADS 
PubMed 
PubMed Central 

Google Scholar 
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).
Google Scholar 
Merow, C., Smith, M. J. & Silander, J. A. A practical guide to MaxEnt for modeling species’ distributions: What it does, and why inputs and settings matter. Ecography (Cop.) 36, 1058–1069 (2013).
Google Scholar 
Swets, J. A. Measuring the accuracy of diagnostic systems. Science 240, 1285–1293 (1988).ADS 
MathSciNet 
CAS 
PubMed 
MATH 

Google Scholar 
Schoener, T. W. The anolis lizards of Bimini: Resource partitioning in a complex fauna. Ecology 49, 704–726 (1968).
Google Scholar 
Warren, D. L., Glor, R. E. & Turelli, M. Environmental niche equivalency versus conservatism: Quantitative approaches to niche evolution. Evolution 62, 2868–2883 (2008).PubMed 

Google Scholar 
Warren, D. L. et al. ENMTools 1.0: an R package for comparative ecological biogeography. Ecography 44, 504–511 (2021).
Google Scholar 
Liu, C., Berry, P. M., Dawson, T. P. & Pearson, R. G. Selecting thresholds of occurrence in the prediction of species distributions. Ecography (Cop.) 28, 385–393 (2005).
Google Scholar 
Vale, C. G., Tarroso, P. & Brito, J. C. Predicting species distribution at range margins: Testing the effects of study area extent, resolution and threshold selection in the Sahara-Sahel transition zone. Divers. Distrib. 20, 20–33 (2014).
Google Scholar  More

General characteristics of study soilsTable 2 presents the descriptive statistics regarding the soil characteristics. Significant changes were observed in the distribution of sand (110–850 g kg−1), silt (50–530 g kg−1), clay (100–610 g kg−1), and soil textural class (7 texture classes) showing the diversity of natural and human processes involved in the formation and development of these soils28. Almost all soil samples were alkaline (with reaction at a range of 7.4–8.1) and calcareous (with CCE at a range of 5.5–35%). The EC of some soils was  > 4 dS/m (about 7% of the soil samples), indicating the partial salinity of the study soils. The organic carbon and total N contents of the soils were, on average, 2% (0.8–3.1%) and 0.28% (0.05–0.51%), respectively, placing them within the range of the moderate class. Likewise, the mean CEC of the soil, which is an effective indicator of soil fertility and quality, was in the moderate class of 12–25 cmol kg−129. The CEC was found to be highly correlated with clay (r = 0.76 P  Pb (58 mg kg−1)  > Ni (55.4 mg kg−1)  > Cu (38.8 mg kg−1)  > Cd (0.88 mg kg−1). In most soil samples, these ranges are comparable with data reported for other urban soils around the world—e.g. Ref.30 in Poland, Ref.31 in China, and Ref.32 in Greece. The values of Cd, Cu, and Zn were below their acceptable ranges as per the international standards4 in all soil samples. Nonetheless, the Pb and Ni contents were higher than their acceptable ranges in 13.1% and 17.4% of the samples, respectively. Furthermore, the concentrations of the five elements were higher than their background values in all urban soil samples. This difference was considerable for Cd, Pb, and Ni. The heavy metals had CV in the order of Cd (53%)  > Pb (51%)  > Ni (46%)  > Zn (21%)  > Cu (18%). This CV variation implies great variations in Cd, Pb, and Ni, which is linked to anthropogenic activities33. The background values of the metals, estimated by the median absolute deviation method10,14, were 52.3, 18.7, 0.45, 29.1, and 30.8 mg kg−1 for Zn, Cu, Cd, Pb, and Ni, respectively.We compared the concentrations of the heavy metals between urban and non-urban soils and found significant increases in the concentration of the metals in most soil types (Fig. 2). The urban soils had 17–36%, 14–21%, 41–70%, 43–69%, and 13–24% higher Zn, Cu, Cd, Pb, and Ni contents than the non-urban soils. The effluent and waste entry from multiple food processing and storage units, dying plants, metal plating facilities, and plastic production in close proximity of the study area is believed to be the reason for the high concentration of these trace elements. Research in various parts of the world, e.g., Ref.34 in India, Ref.35 in Brazil, and Ref.36 in China, has documented that the facilities have introduced significant quantities of heavy metals to soils. However, traffic and agrochemicals also play a key role in the accumulation of heavy metals in this region10.Figure 2The comparison of the mean values of Zn (a), Cu (b), Cd (c), Pb (d), and Ni (e) between urban and non-urban soils in different soil types. Different letters indicate significant differences in metal content within each soil type at P  Ni  > Cu. These findings are comparable to the results reported by37 and12. The highest EF for all five elements was observed in the Fluvisols soil type, reflecting that this soil type had been exposed to element pollution induced by urban activities to a greater extent than the other soil types. In a study on the pollution potential of four soil types in Central Greece, Ref.38 reported different ranges of element pollution across different soil types.Figure 3The comparison of the mean enrichment factor of Zn (a), Cu (b), Cd (c), Pb (d), and Ni (e) between urban and non-urban soils in different soil types. Different letters indicate significant differences in enrichment factor within each soil type at P  Pb (1.89)  > Ni (1.86)  > Cu (1.73)  > Zn (1.51). Mean PI for non-urban soils followed the order Cd (1.5)  > Zn (1.4)  > Cu (1.33)  > Pb (1.31)  > Ni (1.29). Nearly 7% and 16% of the urban soils showed moderate pollution (MP, PI = 2–3) and high pollution classes (HP, PI  > 3) of PI for Cd and 39% and 4% showed the MP and HP class of PI for Pb, respectively. However, the PI class was low pollution (PI = 1–2) for all soil samples and soil types in the non-urban soils. The results on the pollution index indicate a widespread intensification of soil pollution in urban soils across all studied heavy metals.Table 3 The level and terminology of PI and Ei of the analyzed heavy metals in urban and non-urban soils.Full size tableEcological risk, Ei was similarly found to be significantly higher in the urban soils than in the non-urban soils, even though the concentration of all elements except Cd fell within the low-risk class (Ei ≤ 40) in both urban and non-urban soils (Table 3). The mean Ei for Cd was 58.7 (moderate-risk class) and 39.2 (low-risk class) in the urban and non-urban soils, respectively. This means that urban activities have enhanced the ecological risk class of Cd by one grade. Overall, Cd had the highest EF, PI, and Ei among all heavy metals and in all soil samples, indicating a greater risk potential by Cd than Zn, Cu, Pb, and Ni across the water-soil–plant-human domain. Elevated Cd pollution by anthropogenic activities has been widely reported in the literature10,12,39. Cadmium as a Group 1 carcinogen element40 can accumulate in plant tissue without exhibiting visual symptoms. Therefore, Cd generally transfers from soil to the food chain covertly. Cadmium pollution can also influence soil quality and reduce crop yields and grain quality3.Similar to EF, PI, and Ei, the mean ER was significantly elevated in all urban soil types than the non-urban soils (Fig. 4). Among different soil types, the ER magnitude was in the order of Fluvisols (66.6%)  > Regosols (66.1%)  > Cambisols (59.8%)  > Calcisols (47%). These results indicate that Fluvisols carry a higher ecological risk potential for heavy metal accumulations than other soil types. In the study region, Fluvisols due to higher fertility and productivity are subject to more intense and extensive agronomic operations than other soil types13. Heavy application of agrochemicals (e.g., pesticides, herbicides, insecticides, and chemical fertilizers), accelerate the heavy metal input to the Fluvisols. Widespread application of nitrogen fertilizers and subsequent reduction in average soil pH markedly increases the solubility of certain heavy metals (e.g., Zn, Cu, Cd) which can be another factor increasing the ecological risk of heavy metal contamination in Fluvisols41. In addition, these Fluvisols are located on the margin of open urban wastewater channels, which are sometimes used for irrigation. A combination of mentioned processes can be implicated for higher ER of Fluvisols than that of other soil types as for BF, PI, and Ei.Figure 4The comparison of the mean ecological risk of selected heavy metals between urban and non-urban soils in different soil types. Different letters indicate significant differences in ecological risk within each soil type at P  Cu  > Ni  > Cd  > Pb in the roots, partially differing from that of the grain—Zn  > Cu  > Pb  > Ni  > Cd. Heavy metals concentrations observed in the corn roots and grains are almost comparable with those reported by42 in China and43 in Peru.Table 4 Summary statistical attributes of the concentration of heavy metals in corn root (R) and grain (G) along with their BCF and TF.Full size tableThe accumulation of heavy metals in the edible parts of corn is of higher importance. In the present study, the concentrations of these metals were lower than the acceptable level in the corn grains based on international references44. So, the consumption of corns grown in the regions should not threaten human and animal health in the short term, but caution should be exercised in their long-term consumption because some of these elements, especially Cd and Pb, which have long decomposition half-lives, gradually accumulate in body organs, especially in kidneys and livers45. Besides, the ratio of Zn, Cu, Cd, Pb, and Ni of the corn grain to their acceptable standard concentration, known as the pollution index of crop heavy metals, Ref.12 was lower than 0.7 for most corn samples, indicating the unpolluted risk class.The mean concentrations of Cd, Pb, and Ni were 5, 3.1, and 9.2 times as great in the corn roots as in their grains. This observation exhibits a notable phytoremediatory function of corn roots through restriction of radial translocation of heavy metals to the xylems and eventually into the grains. A similar trend of heavy metal accumulation in different plant organs has been reported in previous observations46,47. Based on Kabata-Pendias4 and Adriano22, plant cells can use the defensive tools of the roots to cope with heavy metals, especially Cd and Pb—highly toxic metals to plant cytosols. Accordingly, plant cells can fix these elements in the root system by such approaches as precipitating on cell walls, storing in vacuoles, and/or chelating by phytochelatins, thereby alleviating their toxic effects and inhibiting their translocation to plant shoots. For Zn, Cu, and Cd metals, a significant correlation was observed between their concentration in corn roots and grains. But, a less significant correlation (P  Cu (0.17)  > Zn (0.12)  > Ni (0.02)  > Pb (0.01). This implies that Cd, and to a smaller extent Cu is taken up by corn roots from the soil more readily, but Pb and Ni are less absorbable. These results are consistent with the reports of48 and46. The greater value of BCF-Cd may be related to a combination of the specific factors e,g., Cd concentration and chemistry, as well as soil characteristics (e.g., soil texture, pH, and calcium carbonate content)4. As was already discussed, the examined soils were characterized by high alkaline (pH = 7.4–8.1) and calcareous properties (CCE = 5.5–35%) with a high concentration of Soluble salts (EC = 0.7–6.6 dS m−1). These characteristics can result in the formation of complex Cd ions, especially CdOH+, CdCl20, CdCl+, CdSO40, and CdHCO3+4,22. These ions are plant-available, resulting in a further increase in Cd BCF. Regarding Ni and Pb, the alkaline and calcareous properties of the soils may have motivated insoluble compounds such as NiHCO3+ and NiCO30 (for Ni) and Pb(OH)2, PbCO3, PbSO4, and PbO (for Pb)4,22. These compounds cannot be uptake by plant roots, which may have resulted in a significant decrease in the BCF of these metals versus the other analyzed elements.Like BCF, the heavy metals had TF of  Pb (0.21)  > Cd (0.2)  > Ni (0.15). This implies that Zn and Cu are translocated from roots to grains readily, about four times as great as the other metals, while Ni, Cd, and Pb are translocated in smaller concentrations.The comparison of BCF and TF of Cd showed that less than 30% of Cd, on average, accumulated in the corn roots were translocated to the grains. This states that Cd is immobilized by various mechanisms before it can find its way into the grains. Some of the important mechanisms include (i) the antagonistic effects of Cd with other equivalent elements, especially Zn, Fe, and Ca, in the vascular system of corn, which reduces its mobility in the corn root-stem-grain system22, (ii) Cd sequestration in active exchange sites on the cell wall in the corn root-stem pathway10, and (iii) the binding of Cd with some specific compounds, e.g., phytochelatins of root vacuoles, which immobilizes it before its translocation to grains4,22. Lin and Aarts52 remarked that Cd mostly tends to be trapped in root vacuoles, which reduces its translocation to the upper parts of the plants. In general, it was found that corn plants have a high potential to absorb and accumulate Cd in their roots and Zn in their grains, which is consistent with previous studies41. For the majority of heavy metals, the values of BCF and TF in different soil types were in the order of Fluvisols  > Regosols  > Cambisols  > Calcisols, indicating that the great variety of soil types for the uptake and translocation of heavy metals in the soil-root-grain of the corn (Fig. 5).Figure 5Effect of soil type on the mean bioconcentration factor (a) and translocation factor (b) of selected heavy metals in urban soils. Different letters indicate significant differences in bioconcentration and translocation factors among soil types for each metal at P  Zn  > Cu  > Pb  > Ni for children, differing from that for adults (Cu  > Cd  > Pb  > Zn  > Ni). The values of HQ was  1 in over 87% of the samples, implying the low non-carcinogenic risk of this metal for corn-consuming children in the study region53. Rapidly developing children’s nervous system are highly sensitive to environmental factors, including heavy metals, so even a relatively low concentration of Cd in children’s blood may irreversibly affect their mental growth and functioning54.The highest HI was observed in children (min = 1.16, max = 2.31, mean = 1.63) followed by women and men which was similar to the found pattern of HQ (Table 7). These data show a moderate non-carcinogenic health risk (1 ≤ HI  More