Philippa Kaur

Corlett, R. T. The Anthropocene concept in ecology and conservation. Trends Ecol. Evol. 30, 36–41 (2015).PubMed 

Google Scholar 
IPBES. Summary for Policymakers of the Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES Secretariat, 2019).
Google Scholar 
Vitousek, P. M. Human domination of Earth’s ecosystems. Science 277, 494–499 (1997).CAS 

Google Scholar 
Wong, B. B. M. & Candolin, U. Behavioral responses to changing environments. Behav. Ecol. 26, 665–673 (2015).
Google Scholar 
Hale, R. & Swearer, S. E. Ecological traps: Current evidence and future directions. Proc. R. Soc. B Biol. Sci. 283, 1–8 (2016).
Google Scholar 
Charman, T. G., Sears, J., Green, R. E. & Bourke, A. F. G. Conservation genetics, foraging distance and nest density of the scarce Great Yellow Bumblebee (Bombus distinguendus). Mol. Ecol. 19, 2661–2674 (2010).PubMed 

Google Scholar 
Violle, C. et al. Let the concept of trait be functional!. Oikos 116, 882–892 (2007).
Google Scholar 
Husemann, M., Zachos, F. E., Paxton, R. J. & Habel, J. C. Effective population size in ecology and evolution. Heredity 117, 191–192 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wagner, D. L. Insect declines in the Anthropocene. Annu. Rev. Entomol. 65, 457–480 (2020).CAS 
PubMed 

Google Scholar 
Goulson, D., Nicholls, E., Botías, C. & Rotheray, E. L. Bee declines driven by combined stress from parasites, pesticides, and lack of flowers. Science 347, 1255957 (2015).PubMed 

Google Scholar 
Thogmartin, W. E. et al. Monarch butterfly population decline in North America: Identifying the threatening processes. R. Soc. Open Sci. 4, 170760 (2017).ADS 
PubMed 
PubMed Central 

Google Scholar 
Cameron, S. A. et al. Patterns of widespread decline in North American bumble bees. Proc. Natl. Acad. Sci. U.S.A. 108, 662–667 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Burkle, L. A., Marlin, J. C. & Knight, T. M. Plant-pollinator interactions over 120 years: Loss of species, co-occurrence, and function. Science 340, 1611–1615 (2013).ADS 

Google Scholar 
Grixti, J. C., Wong, L. T., Cameron, S. A. & Favret, C. Decline of bumble bees (Bombus) in the North American Midwest. Biol. Conserv. 142, 75–84 (2009).
Google Scholar 
Goulson, D. Bumblebees: Behaviour, Ecology, and Conservation (Oxford University Press, Oxford, 2010).
Google Scholar 
Colla, S. R., Gadallah, F., Richardson, L., Wagner, D. & Gall, L. Assessing declines of North American bumble bees (Bombus spp.) using museum specimens. Biodivers. Conserv. 21, 3585–3595 (2012).
Google Scholar 
Hatfield, R. et al. IUCN assessments of North American Bombus spp. http://www.xerces.org/ (2015).Arbetman, M. P., Gleiser, G., Morales, C. L., Williams, P. & Aizen, M. A. Global decline of bumblebees is phylogenetically structured and inversely related to species range size and pathogen incidence. Proc. R. Soc. B Biol. Sci. 284, 20170204 (2017).
Google Scholar 
Bommarco, R. et al. Dispersal capacity and diet breadth modify the response of wild bees to habitat loss. Proc. R. Soc. B Biol. Sci. 277, 2075–2082 (2010).
Google Scholar 
Hall, D. M. et al. The city as a refuge for insect pollinators. Conserv. Biol. 31, 24–29 (2017).PubMed 

Google Scholar 
Banaszak-Cibicka, W. & Żmihorski, M. Wild bees along an urban gradient: Winners and losers. J. Insect Conserv. 16, 331–343 (2012).
Google Scholar 
Wilson, C. J. & Jamieson, M. A. The effects of urbanization on bee communities depends on floral resource availability and bee functional traits. PLoS One 14, e0225852 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Thompson, M.J., Capilla-Lasheras, P.C., Dominoni, D.M., Réale, D. & Charmantier, A. Phenotypic variation in urban environments: mechanisms and implications. Trends Ecol. Evol. 37, 171–182 (2022).CAS 
PubMed 

Google Scholar 
Peat, J., Tucker, J. & Goulson, D. Does intraspecific size variation in bumblebees allow colonies to efficiently exploit different flowers?. Ecol. Entomol. 30, 176–181 (2005).
Google Scholar 
Greenleaf, S. S., Williams, N. M., Winfree, R. & Kremen, C. Bee foraging ranges and their relationship to body size. Oecologia 153, 589–596 (2007).ADS 
PubMed 

Google Scholar 
Spaethe, J. & Weidenmüller, A. Size variation and foraging rate in bumblebees (Bombus terrestris). Insectes Soc. 49, 142–146 (2002).
Google Scholar 
Couvillon, M. J. & Dornhaus, A. Small worker bumble bees (Bombus impatiens) are hardier against starvation than their larger sisters. Insectes Soc. 57, 193–197 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Pendrel, B. A. & Plowright, R. C. Larval feeding by adult bumble bee workers (Hymenoptera: Apidae). Behav. Ecol. Sociobiol. 8, 71–76 (1981).
Google Scholar 
Sutcliffe, G. H. & Plowright, R. C. The effects of food supply on adult size in the bumble bee Bombus terricola Kirby (Hymenoptera: Apidae). Can. Entomol. 120, 1051–1058 (1988).
Google Scholar 
Couvillon, M. J. & Dornhaus, A. Location, location, location: Larvae position inside the nest is correlated with adult body size in worker bumble-bees (Bombus impatiens). Proc. R. Soc. B Biol. Sci. 276, 2411–2418 (2009).
Google Scholar 
Bartomeus, I. et al. Historical changes in northeastern US bee pollinators related to shared ecological traits. Proc. Natl. Acad. Sci. U.S.A. 110, 4656–4660 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Austin, M. W. & Dunlap, A. S. Intraspecific variation in worker body size makes North American bumble bees (Bombus spp.) less susceptible to decline. Am. Nat. 194, 381–394 (2019).PubMed 

Google Scholar 
Watters, J. V., Lema, S. C. & Nevitt, G. A. Phenotype management: A new approach to habitat restoration. Biol. Conserv. 112, 435–445 (2003).
Google Scholar 
Haddaway, N. R., Mortimer, R. J. G., Christmas, M., Grahame, J. W. & Dunn, A. M. Morphological diversity and phenotypic plasticity in the threatened British white-clawed crayfish (Austropotamobius pallipes). Aquat. Conserv. Mar. Freshw. Ecosyst. 22, 220–231 (2012).
Google Scholar 
Lema, S. C. & Nevitt, G. A. Testing an ecophysiological mechanism of morphological plasticity in pupfish and its relevance to conservation efforts for endangered Devils Hole pupfish. J. Exp. Biol. 209, 3499–3509 (2006).PubMed 

Google Scholar 
Crispo, E. Modifying effects of phenotypic plasticity on interactions among natural selection, adaptation and gene flow. J. Evol. Biol. 21, 1460–1469 (2008).CAS 
PubMed 

Google Scholar 
Fraser, D. J. & Bernatchez, L. Adaptive evolutionary conservation: Towards a unified concept for defining conservation units. Mol. Ecol. 10, 2741–2752 (2001).CAS 
PubMed 

Google Scholar 
Nicotra, A. B. et al. Plant phenotypic plasticity in a changing climate. Trends Plant Sci. 15, 684–692 (2010).CAS 
PubMed 

Google Scholar 
Spielman, D., Brook, B. W. & Frankham, R. Most species are not driven to extinction before genetic factors impact them. Proc. Natl. Acad. Sci. U.S.A. 101, 15261–15264 (2004).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Woodard, S. H. et al. Molecular tools and bumble bees: Revealing hidden details of ecology and evolution in a model system. Mol. Ecol. 24, 2916–2936 (2015).MathSciNet 
PubMed 

Google Scholar 
Lozier, J. D., Strange, J. P., Stewart, I. J. & Cameron, S. A. Patterns of range-wide genetic variation in six North American bumble bee (Apidae: Bombus) species. Mol. Ecol. 20, 4870–4888 (2011).PubMed 

Google Scholar 
Williams, B. L., Brawn, J. D. & Paige, K. N. Landscape scale genetic effects of habitat fragmentation on a high gene flow species: Speyeria idalia (Nymphalidae). Mol. Ecol. 12, 11–20 (2003).CAS 
PubMed 

Google Scholar 
IUCN. The IUCN Red List of Threatened Species. https://www.iucnredlist.org. Accessed 18 Dec 2019 (2019).MacPhail, V. J., Richardson, L. L. & Colla, S. R. Incorporating citizen science, museum specimens, and field work into the assessment of extinction risk of the American Bumble bee (Bombus pensylvanicus De Geer 1773) in Canada. J. Insect Conserv. 23, 597–611 (2019).
Google Scholar 
Camilo, G. R., Muñiz, P. A., Arduser, M. S. & Spevak, E. M. A checklist of the bees (Hymenoptera: Apoidea) of St. Louis, Missouri, USA. J. Kansas Entomol. Soc. 90, 175–188 (2018).
Google Scholar 
United States Census Bureau. Land Area and Persons Per Square Mile. https://www.census.gov/quickfacts/fact/note/US/LND110210. Accessed 26 March 2020 (2010).United States Census Bureau. City and Town Population Totals: 2010–2018. https://www.census.gov/data/tables/time-series/demo/popest/2010s-total-cities-and-towns.html. Accessed 26 March 2020 (2020).Thompson, K. & Jones, A. Human population density and prediction of local plant extinction in Britain. Conserv. Biol. 13, 185–189 (1999).
Google Scholar 
Fontana, C. S., Burger, M. I. & Magnusson, W. E. Bird diversity in a subtropical South-American City: Effects of noise levels, arborisation and human population density. Urban Ecosyst. 14, 341–360 (2011).
Google Scholar 
Lepais, O. et al. Estimation of bumblebee queen dispersal distances using sibship reconstruction method. Mol. Ecol. 19, 819–831 (2010).CAS 
PubMed 

Google Scholar 
Holehouse, K. A., Hammond, R. L. & Bourke, A. F. G. Non-lethal sampling of DNA from bumble bees for conservation genetics. Insectes Soc. 50, 277–285 (2003).
Google Scholar 
Williams, P. H., Thorp, R., Richardson, L. & Colla, S. R. Bumble Bees of North America (Princeton University Press, 2014).
Google Scholar 
Cane, J. H. Estimation of bee size using intertegular span (Apoidea). J. Kansas Entomol. Soc. 60, 145–147 (1987).
Google Scholar 
Walsh, P. S., Metzger, D. A. & Higuchi, R. Chelex 100 as a medium for simple extraction of DNA for PCR-based typing from forensic material. Biotechniques 10, 506–513 (1991).CAS 
PubMed 

Google Scholar 
Estoup, A., Scholl, A., Pouvreau, A. & Solignac, M. Monoandry and polyandry in bumble bees (Hymenoptera; Bombinae) as evidenced by highly variable microsatellites. Mol. Ecol. 4, 89–94 (1995).CAS 
PubMed 

Google Scholar 
Estoup, A., Solignac, M., Cornuet, J. M., Goudet, J. & Scholl, A. Genetic differentiation of continental and island populations of Bombus terrestris (Hymenoptera: Apidae) in Europe. Mol. Ecol. 5, 19–31 (1996).CAS 
PubMed 

Google Scholar 
Funk, C. R., Schmid-Hempel, R. & Schmid-Hempel, P. Microsatellite loci for Bombus spp. Mol. Ecol. Notes 6, 83–86 (2006).CAS 

Google Scholar 
Stolle, E. et al. Novel microsatellite DNA loci for Bombus terrestris (Linnaeus, 1758). Mol. Ecol. Resour. 9, 1345–1352 (2009).CAS 
PubMed 

Google Scholar 
Kearse, M. et al. Geneious Basic: An integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28, 1647–1649 (2012).PubMed 
PubMed Central 

Google Scholar 
Chapman, R. E. & Bourke, A. F. G. The influence of sociality on the conservation biology of social insects. Ecol. Lett. 4, 650–662 (2001).
Google Scholar 
Geib, J. C., Strange, J. P. & Galen, A. Bumble bee nest abundance, foraging distance, and host-plant reproduction: Implications for management and conservation. Ecol. Appl. 25, 768–778 (2015).PubMed 

Google Scholar 
Chakraborty, R., Andrade, M. D. E., Daiger, S. P. & Budowle, B. Apparent heterozygote deficiencies observed in DNA typing data and their implications in forensic applications. Ann. Hum. Genet. 56, 45–57 (1992).CAS 
PubMed 
MATH 

Google Scholar 
Gruber, B. & Adamack, A. T. PopGenReport: Simplifying basic population genetic analyses in R. Methods Ecol. Evol. 5, 384–387 (2014).
Google Scholar 
Wang, J. Sibship reconstruction from genetic data with typing errors. Genetics 166, 1963–1979 (2004).PubMed 
PubMed Central 

Google Scholar 
Crozier, R. H. Genetics of sociality. In Social Insects Vol. I (ed. Hermann, H. R.) 223–286 (Academic Press, 1979).
Google Scholar 
Rousset, F. genepop’007: A complete re-implementation of the genepop software for Windows and Linux. Mol. Ecol. Resour. 8, 103–106 (2008).PubMed 

Google Scholar 
Leberg, P. L. Estimating allelic richness: Effects of sample size and bottlenecks. Mol. Ecol. 11, 2445–2449 (2002).CAS 
PubMed 

Google Scholar 
Goudet, J. hierfstat, a package for r to compute and test hierarchical F-statistics. Mol. Ecol. Notes 5, 184–186 (2005).
Google Scholar 
Weir, B. S. & Cockerham, C. C. Estimating F-statistics for the analysis of population structure. Evolution 38, 1358–1370 (1984).CAS 

Google Scholar 
Ryman, N. & Palm, S. POWSIM: A computer program for assessing statistical power when testing for genetic differentiation. Mol. Ecol. Notes 6, 600–602 (2006).
Google Scholar 
Zayed, A. & Packer, L. High levels of diploid male production in a primitively eusocial bee (Hymenoptera: Halictidae). Heredity 87, 631–636 (2001).CAS 
PubMed 

Google Scholar 
Darvill, B., Ellis, J. S., Lye, G. C. & Goulson, D. Population structure and inbreeding in a rare and declining bumblebee, Bombus muscorum (Hymenoptera: Apidae). Mol. Ecol. 15, 601–611 (2006).CAS 
PubMed 

Google Scholar 
Hale, M. L., Burg, T. M. & Steeves, T. E. Sampling for microsatellite-based population genetic studies: 25 to 30 Individuals per population is enough to accurately estimate allele frequencies. PLoS One 7, e45170 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lenth, R. V. Least-squares means: The R package lsmeans. J. Stat. Softw. 69, 1–33 (2016).
Google Scholar 
Fitzpatrick, S. W. et al. Gene flow constrains and facilitates genetically based divergence in quantitative traits. Copeia 105, 462–474 (2017).
Google Scholar 
Price, T. D., Qvarnström, A. & Irwin, D. E. The role of phenotypic plasticity in driving genetic evolution. Proc. R. Soc. B Biol. Sci. 270, 1433–1440 (2003).
Google Scholar 
Liu, B.-J., Zhang, B.-D., Xue, D.-X., Gao, T.-X. & Liu, J.-X. Population structure and adaptive divergence in a high gene flow marine fish: The small yellow croaker (Larimichthys polyactis). PLoS One 11, e0154020 (2016).PubMed 
PubMed Central 

Google Scholar 
Vaudo, A. D., Tooker, J. F., Grozinger, C. M. & Patch, H. M. Bee nutrition and floral resource restoration. Curr. Opin. Insect Sci. 10, 133–141 (2015).PubMed 

Google Scholar 
Woodard, S. H. & Jha, S. Wild bee nutritional ecology: Predicting pollinator population dynamics, movement, and services from floral resources. Curr. Opin. Insect Sci. 21, 83–90 (2017).PubMed 

Google Scholar 
Keller, L. F. & Waller, D. M. Inbreeding effects in wild populations. Trends Ecol. Evol. 17, 230–241 (2002).
Google Scholar 
Sivakoff, F. S. & Gardiner, M. M. Soil lead contamination decreases bee visit duration at sunflowers. Urban Ecosyst. 20, 1221–1228 (2017).
Google Scholar 
Whitehorn, P. R., Norville, G., Gilburn, A. & Goulson, D. Larval exposure to neonicotinoid imidacloprid impacts adult size in the farmland butterfly Pieris brassicae. PeerJ 6, e4772 (2018).PubMed 
PubMed Central 

Google Scholar 
Merckx, T., Kaiser, A. & Van Dyck, H. Increased body size along urbanization gradients at both community and intraspecific level in macro-moths. Glob. Change Biol. 24, 3837–3848 (2018).ADS 

Google Scholar 
Oliveira, M. O., Brito, T. F., Campbell, A. J. & Contrera, F. A. L. Body size and corbiculae area variation of the stingless bee Melipona fasciculata Smith, 1854 (Apidae, Meliponini) under different levels of habitat quality in the eastern Amazon. Entomol. Gen. 39, 45–52 (2019).
Google Scholar 
Warzecha, D., Diekötter, T., Wolters, V. & Jauker, F. Intraspecific body size increases with habitat fragmentation in wild bee pollinators. Landsc. Ecol. 31, 1449–1455 (2016).
Google Scholar 
Theodorou, P., Baltz, L. M., Paxton, R. J. & Soro, A. Urbanization is associated with shifts in bumblebee body size, with cascading effects on pollination. Evol. Appl. 14, 53–68 (2021).PubMed 

Google Scholar 
Strange, J. P. & Tripodi, A. D. Characterizing bumble bee (Bombus) communities in the United States and assessing a conservation monitoring method. Ecol. Evol. 9, 1061–1069 (2019).PubMed 
PubMed Central 

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

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

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

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

Du, E. et al. Global patterns of terrestrial nitrogen and phosphorus limitation. Nat. Geosci. 13, 221–226 (2020).ADS 
CAS 

Google Scholar 
Schulze, E. Ueber einige stickstoffhaltige Bestandtheile der Keimlinge von Vicia sativa. Z. Phys. Chem. 17, 193–216 (1893).
Google Scholar 
Wishart, D. S. et al. HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res. 46, D608–D617 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kato, T., Yamagata, M. & Tsukahara, S. Guanidine compounds in fruit trees and their seasonal variations in citrus (Citrus unshiu Marc.). J. Jpn. Soc. Hortic. Sci. 55, 169–173 (1986).CAS 

Google Scholar 
Gund, P. Guanidine, trimethylenemethane, and “Y-delocalization.” Can acyclic compounds have “aromatic” stability? J. Chem. Educ. 49, 100 (1972).CAS 

Google Scholar 
Güthner, T., Mertschenk, B. & Schulz, B. In Ullmann’s Fine Chemicals vol. 2, 657–672 (Wiley-VCH, 2014).Strecker, A. Untersuchungen über die chemischen Beziehungen zwischen Guanin, Xanthin, Theobromin, Caffeïn und Kreatinin. Justus Liebigs Ann. Chem. 118, 151–177 (1861).
Google Scholar 
Iwanoff, N. N. & Awetissowa, A. N. The fermentative conversion of guanidine in urea. Biochem. Z. 231, 67–78 (1931).
Google Scholar 
Lenkeit, F., Eckert, I., Hartig, J. S. & Weinberg, Z. Discovery and characterization of a fourth class of guanidine riboswitches. Nucleic Acids Res. 48, 12889–12899 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Salvail, H., Balaji, A., Yu, D., Roth, A. & Breaker, R. R. Biochemical validation of a fourth guanidine riboswitch class in bacteria. Biochemistry 59, 4654–4662 (2020).CAS 
PubMed 

Google Scholar 
Nelson, J. W., Atilho, R. M., Sherlock, M. E., Stockbridge, R. B. & Breaker, R. R. Metabolism of free guanidine in bacteria is regulated by a widespread riboswitch class. Mol. Cell 65, 220–230 (2017).CAS 
PubMed 

Google Scholar 
Sherlock, M. E. & Breaker, R. R. Biochemical validation of a third guanidine riboswitch class in bacteria. Biochemistry 56, 359–363 (2016).
Google Scholar 
Sherlock, M. E., Malkowski, S. N. & Breaker, R. R. Biochemical validation of a second guanidine riboswitch class in bacteria. Biochemistry 56, 352–358 (2016).
Google Scholar 
Kermani, A. A., Macdonald, C. B., Gundepudi, R. & Stockbridge, R. B. Guanidinium export is the primal function of SMR family transporters. Proc. Natl Acad. Sci. USA 115, 3060–3065 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Sinn, M., Hauth, F., Lenkeit, F., Weinberg, Z. & Hartig, J. S. Widespread bacterial utilization of guanidine as nitrogen source. Mol. Microbiol. 116, 200–210 (2021).CAS 
PubMed 

Google Scholar 
Schneider, N. O. et al. Solving the conundrum: widespread proteins annotated for urea metabolism in bacteria are carboxyguanidine deiminases mediating nitrogen assimilation from guanidine. Biochemistry 59, 3258–3270 (2020).CAS 
PubMed 

Google Scholar 
Zhao, J., Zhu, L., Fan, C., Wu, Y. & Xiang, S. Structure and function of urea amidolyase. Biosci. Rep. 38, BSR20171617 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Mobley, H. L., Island, M. D. & Hausinger, R. P. Molecular biology of microbial ureases. Microbiol. Rev. 59, 451–480 (1995).CAS 
PubMed 
PubMed Central 

Google Scholar 
Mazzei, L., Musiani, F. & Ciurli, S. The structure-based reaction mechanism of urease, a nickel dependent enzyme: tale of a long debate. J. Biol. Inorg. Chem. 25, 829–845 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Uribe, E. et al. Functional analysis of the Mn2+ requirement in the catalysis of ureohydrolases arginase and agmatinase – a historical perspective. J. Inorg. Biochem. 202, 110812 (2020).CAS 
PubMed 

Google Scholar 
Perozich, J., Hempel, J. & Morris, S. M. Jr Roles of conserved residues in the arginase family. Biochim. Biophys. Acta 1382, 23–37 (1998).CAS 
PubMed 

Google Scholar 
Sekowska, A., Danchin, A. & Risler, J. L. Phylogeny of related functions: the case of polyamine biosynthetic enzymes. Microbiology 146, 1815–1828 (2000).CAS 
PubMed 

Google Scholar 
Sekula, B. The neighboring subunit is engaged to stabilize the substrate in the active site of plant arginases. Front. Plant Sci. 11, 987 (2020).PubMed 
PubMed Central 

Google Scholar 
Quintero, M. J., Muro-Pastor, A. M., Herrero, A. & Flores, E. Arginine catabolism in the cyanobacterium Synechocystis sp. strain PCC 6803 involves the urea cycle and arginase pathway. J. Bacteriol. 182, 1008–1015 (2000).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lacasse, M. J., Summers, K. L., Khorasani-Motlagh, M., George, G. N. & Zamble, D. B. Bimodal nickel-binding site on Escherichia coli [NiFe]-hydrogenase metallochaperone HypA. Inorg. Chem. 58, 13604–13618 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Hoffmann, D., Gutekunst, K., Klissenbauer, M., Schulz-Friedrich, R. & Appel, J. Mutagenesis of hydrogenase accessory genes of Synechocystis sp. PCC 6803. FEBS J. 273, 4516–4527 (2006).CAS 
PubMed 

Google Scholar 
Dowling, D. P., Di Costanzo, L., Gennadios, H. A. & Christianson, D. W. Evolution of the arginase fold and functional diversity. Cell. Mol. Life Sci. 65, 2039–2055 (2008).CAS 
PubMed 
PubMed Central 

Google Scholar 
Dutta, A., Mazumder, M., Alam, M., Gourinath, S. & Sau, A. K. Metal-induced change in catalytic loop positioning in Helicobacter pylori arginase alters catalytic function. Biochem. J. 476, 3595–3614 (2019).CAS 
PubMed 

Google Scholar 
Di Costanzo, L. et al. Crystal structure of human arginase I at 1.29-Å resolution and exploration of inhibition in the immune response. Proc. Natl Acad. Sci. USA 102, 13058–13063 (2005).ADS 
PubMed 
PubMed Central 

Google Scholar 
Suzek, B. E. et al. UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches. Bioinformatics 31, 926–932 (2014).PubMed 
PubMed Central 

Google Scholar 
Alfano, M. & Cavazza, C. Structure, function, and biosynthesis of nickel-dependent enzymes. Protein Sci. 29, 1071–1089 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wang, B. et al. A guanidine-degrading enzyme controls genomic stability of ethylene-producing cyanobacteria. Nat. Commun. 12, 5150 (2021).ADS 
PubMed 
PubMed Central 

Google Scholar 
McGee, D. J. et al. Purification and characterization of Helicobacter pylori arginase, RocF: unique features among the arginase superfamily. Eur. J. Biochem. 271, 1952–1962 (2004).CAS 
PubMed 

Google Scholar 
Arakawa, N., Igarashi, M., Kazuoka, T., Oikawa, T. & Soda, K. d-Arginase of Arthrobacter sp. KUJ 8602: characterization and its identity with Zn2+-guanidinobutyrase. J. Biochem. 133, 33–42 (2003).CAS 
PubMed 

Google Scholar 
Saragadam, T., Kumar, S. & Punekar, N. S. Characterization of 4-guanidinobutyrase from Aspergillus niger. Microbiology 165, 396–410 (2019).CAS 
PubMed 

Google Scholar 
Viator, R. J., Rest, R. F., Hildebrandt, E. & McGee, D. J. Characterization of Bacillus anthracis arginase: effects of pH, temperature, and cell viability on metal preference. BMC Biochem. 9, 15 (2008).PubMed 
PubMed Central 

Google Scholar 
D’Antonio, E. L., Hai, Y. & Christianson, D. W. Structure and function of non-native metal clusters in human arginase I. Biochemistry 51, 8399–8409 (2012).PubMed 

Google Scholar 
Andresen, E., Peiter, E. & Küpper, H. Trace metal metabolism in plants. J. Exp. Bot. 69, 909–954 (2018).CAS 
PubMed 

Google Scholar 
Eisenhut, M. Manganese homeostasis in cyanobacteria. Plants 9, 18 (2019).PubMed Central 

Google Scholar 
Burnat, M. & Flores, E. Inactivation of agmatinase expressed in vegetative cells alters arginine catabolism and prevents diazotrophic growth in the heterocyst-forming cyanobacterium Anabaena. MicrobiologyOpen 3, 777–792 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Callahan, B. P., Yuan, Y. & Wolfenden, R. The burden borne by urease. J. Am. Chem. Soc. 127, 10828–10829 (2005).CAS 
PubMed 

Google Scholar 
Lewis, C. A. Jr & Wolfenden, R. The nonenzymatic decomposition of guanidines and amidines. J. Am. Chem. Soc. 136, 130–136 (2014).CAS 
PubMed 

Google Scholar 
Grobben, Y. et al. Structural insights into human Arginase-1 pH dependence and its inhibition by the small molecule inhibitor CB-1158. J. Struct. Biol. X 4, 100014 (2020).CAS 
PubMed 

Google Scholar 
Mills, L. A., McCormick, A. J. & Lea-Smith, D. J. Current knowledge and recent advances in understanding metabolism of the model cyanobacterium Synechocystis sp. PCC 6803. Biosci. Rep. 40, BSR20193325 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Giner-Lamia, J. et al. Identification of the direct regulon of NtcA during early acclimation to nitrogen starvation in the cyanobacterium Synechocystis sp PCC 6803. Nucleic Acids Res. 45, 11800–11820 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Martinez, S. & Hausinger, R. P. Biochemical and spectroscopic characterization of the non-heme Fe(II)- and 2-oxoglutarate-dependent ethylene-forming enzyme from Pseudomonas syringae pv. phaseolicola PK2. Biochemistry 55, 5989–5999 (2016).CAS 
PubMed 

Google Scholar 
Copeland, R. A. et al. An iron(IV)-oxo intermediate initiating l-arginine oxidation but not ethylene production by the 2-oxoglutarate-dependent oxygenase, ethylene-forming enzyme. J. Am. Chem. Soc. 143, 2293–2303 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Rippka, R., Deruelles, J., Waterbury, J. B., Herdman, M. & Stanier, R. Y. Generic assignments, strain histories and properties of pure cultures of cyanobacteria. Microbiology 111, 1–61 (1979).
Google Scholar 
Geyer, J. W. & Dabich, D. Rapid method for determination of arginase activity in tissue homogenates. Anal. Biochem. 39, 412–417 (1971).CAS 
PubMed 

Google Scholar 
van Anken, H. C. & Schiphorst, M. E. A kinetic determination of ammonia in plasma. Clin. Chim. Acta 56, 151–157 (1974).PubMed 

Google Scholar 
Kabsch, W. XDS. Acta Crystallogr. D 66, 125–132 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
McCoy, A. J. et al. Phaser crystallographic software. J. Appl. Crystallogr. 40, 658–674 (2007).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lamzin, V. S. P. A., Wilson, K. S. In International Tables for Crystallography Vol. F (eds Arnold, E. et al.) 525–528 (Kluwer, 2012).Emsley, P., Lohkamp, B., Scott, W. G. & Cowtan, K. Features and development of Coot. Acta Crystallogr. D 66, 486–501 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Adams, P. D. et al. The Phenix software for automated determination of macromolecular structures. Methods 55, 94–106 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Williams, C. J. et al. MolProbity: more and better reference data for improved all-atom structure validation. Protein Sci. 27, 293–315 (2018).CAS 
PubMed 

Google Scholar 
Wang, J., Wang, W., Kollman, P. A. & Case, D. A. Automatic atom type and bond type perception in molecular mechanical calculations. J. Mol. Graph. Model. 25, 247–260 (2006).ADS 
PubMed 

Google Scholar 
Maier, J. A. et al. ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB. J. Chem. Theory Comput. 11, 3696–3713 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wang, J., Wolf, R. M., Caldwell, J. W., Kollman, P. A. & Case, D. A. Development and testing of a general amber force field. J. Comput. Chem. 25, 1157–1174 (2004).CAS 
PubMed 

Google Scholar 
Trott, O. & Olson, A. J. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 31, 455–461 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Ashkenazy, H. et al. ConSurf 2016: an improved methodology to estimate and visualize evolutionary conservation in macromolecules. Nucleic Acids Res. 44, W344–W350 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lemoine, F. et al. Renewing Felsenstein’s phylogenetic bootstrap in the era of big data. Nature 556, 452–456 (2018).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lemoine, F. et al. NGPhylogeny.fr: new generation phylogenetic services for non-specialists. Nucleic Acids Res. 47, W260–W265 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v4: recent updates and new developments. Nucleic Acids Res. 47, W256–W259 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Crooks, G. E., Hon, G., Chandonia, J. M. & Brenner, S. E. WebLogo: a sequence logo generator. Genome Res. 14, 1188–1190 (2004).CAS 
PubMed 
PubMed Central 

Google Scholar  More

Beck, M. W. et al. The role of near shore ecosystems as fish and shellfish nurseries. Issues Ecol. 11, 1–12 (2003).
Google Scholar 
De la Torre-Castro, M., Di Carlo, G. & Jiddawi, N. S. Seagrass importance for a small-scale fishery in the tropics: The need for seascape management. Mar. Poll. Bull. 83, 398–407 (2014).
Google Scholar 
Sheaves, M., Baker, R., Nagelkerken, I. & Connolly, R. M. True value of estuarine and coastal nurseries for fish: incorporating complexity and dynamics. Estuar. Coasts 38, 401–414 (2014).
Google Scholar 
Nordlund, L. M., Unsworth, R. K. F., Gullström, M. & Cullen-Unsworth, L. C. Global significance of seagrass fishery activity. Fish. Fish. 19, 399–412 (2018).
Google Scholar 
Kimirei, I. A., Nagelkerken, I., Griffioen, B., Wagner, C. & Mgaya, Y. D. Ontogenetic habitat use by mangrove/seagrass-associated coral reef fishes shows flexibility in time and space. Estuar. Coast. Shelf Sci. 92, 47–58 (2011).ADS 

Google Scholar 
Unsworth, R. K. F. et al. Structuring of Indo-Pacific fish assemblages along the mangrove-seagrass continuum. Aquat. Biol. 5, 85–95 (2009).
Google Scholar 
Cocheret De La Morinière, E., Pollux, B. J. A., Nagelkerken, I. & van Der Velde, G. Post-settlement life cycle migration patterns and habitat preference of coral reef fish that use seagrass and mangrove habitats as nurseries. Estuar. Coast. Shelf Sci. 55, 309–321 (2002).Berkström, C., Lindborg, R., Thyresson, M. & Gullström, M. Assessing connectivity in a tropical embayment: fish migrations and seascape ecology. Biol. Conserv. 166, 43–53 (2013).
Google Scholar 
Saenger, P., Gartside, D. & Funge-Smith, S. A review of mangrove and seagrass ecosystems and their linkage to fisheries and fisheries management. FAO Regional Office for Asia and the Pacific, Bangkok, Thailand, 74 (RAP Publication, 2013).King, A. J. Density and distribution of potential prey for larval fish in the main channel of a floodplain river: pelagic versus epibenthic meiofauna. River Res. Appl. 20, 883–897 (2004).
Google Scholar 
Carassou, L., Ponton, D., Mellin, C. & Galzin, R. Predicting the structure of larval fish assemblages by a hierarchical classification of meteorological and water column forcing factors. Coral Reefs 27, 867–880 (2008).ADS 

Google Scholar 
Pinho Costa, A. C., Martins Garcia, T., Pereira Paiva, B., Ximenes Neto, A. R. & de Oliveira Soares, M. Seagrass and rhodolith beds are important seascapes for the development of fish eggs and larvae in tropical coastal areas. Mar. Environ. Res. 161, 105064 (2020).Muzaki, F. K., Giffari, A. & Saptarini, D. Community structure of fish larvae in mangroves with different root types in Labuhan coastal area, Sepulu–Madura. AIP Conf. Proc. 1854, 020025 (2017).Isari, S. et al. Exploring the larval fish community of the central Red Sea with an integrated morphological and molecular approach. PLoS ONE, 12, e0182503 (2017).Levin, P. S. Fine-scale temporal variation in recruitment of a temperate demersal fish: the importance of settlement versus post-settlement loss. Oecologia 97, 124–133 (1994).ADS 
CAS 
PubMed 

Google Scholar 
Mwaluma, J. M., Boaz Kaunda-Arara, B., Rasowo, J., Osore, M. K. & Vidar Øresland V. Seasonality in fish larval assemblage structure within marine reef National Parks in coastal Kenya. Environ. Biol. Fish. 90, 393–404 (2011).Reglero, P., Tittensor, D. P., Álvarez-Berastegui, D., Aparicio-González, A. & Worm, B. Worldwide distributions of tuna larvae: revisiting hypotheses on environmental requirements for spawning habitats. Mar. Ecol. Prog. Ser. 501, 207–224 (2014).ADS 

Google Scholar 
Leis, J. M. Ontogeny of behaviour in larvae of marine demersal fishes. Ichthyol. Res. 57, 325–342 (2010).
Google Scholar 
Tzeng, W. N. & Wang, Y. T. Hydrography and distribution dynamics of larval and juvenile fishes in the coastal waters of the Tanshui River estuary, Taiwan, with reference to estuarine larval transport. Mar. Biol. 116, 205–217 (1993).
Google Scholar 
Leis, J. M., Sweatman, H. P. A. & Reader, S. E. What the pelagic stages of coral reef fishes are doing out in blue water: Daytime field observations of larval behavioural capabilities. Mar. Freshw. Res. 47, 401–411 (1996).
Google Scholar 
Leis, J. M. & Carson-Ewart, B. M. Complex behaviour by coral-reef fish larvae in open-water and near-reef pelagic environments. Environ. Biol. Fish. 53, 259–266 (1998).
Google Scholar 
Leis, J. M. Are larvae of demersal fishes plankton or nekton?. Adv. Mar. Biol. 51, 57–141 (2006).PubMed 

Google Scholar 
Faillettaz, R., Paris, C. B. & Irisson, J. O. Larval fish swimming behavior alters dispersal patterns from marine protected areas in the North-Western Mediterranean Sea. Front. Mar. Sci. 5, 1–12 (2018).ADS 

Google Scholar 
Azeiteiro, U. M., Bacelar-Nicolau, L., Resende, P., Gonçalves, F. & Pereira, M. J. Larval fish distribution in shallow coastal waters off North Western Iberia (NE Atlantic). Estuar. Coast. Shelf Sci. 69, 554–566 (2006).ADS 

Google Scholar 
Irisson, J. O. & Lecchini, D. In situ observation of settlement behaviour in larvae of coral reef fishes at night. J. Fish Biol. 72, 2707–2713 (2008).
Google Scholar 
Teixeira Bonecker, F., de Castro, M. S. & Teixeira Bonecker, A. C. Larval fish assemblage in a tropical estuary in relation to tidal cycles, day/night and seasonal variations. Pan-Am. J. Aquat. Sci. 4, 239–246 (2009).Strydom, N. A. Patterns in larval fish diversity, abundance, and distribution in temperate South African estuaries. Estuar. Coasts 38, 268–284 (2014).
Google Scholar 
Lana, P. C. & Bernardino, A. F. (Eds). Brazilian estuaries: a benthic perspective. Brazilian Marine Biodiversity series. 212 (Springer, Cham, 2018).Donahue, M. J., Karnauskas, M., Toews, C. & Paris, C. B. Location isn’t everything: Timing of spawning aggregations optimizes larval replenishment. PLoS ONE 10, 1–15 (2015).
Google Scholar 
Reynalte-Tataje, D. A., Zaniboni-Filho, E., Bialetzki, A. & Agostinho, A. A. Temporal variability of fish larvae assemblages: influence of natural and anthropogenic disturbances. Neotrop. Ichthyol. 10, 837–846 (2012).
Google Scholar 
Somarakis, S., Tsoukali, S., Giannoulaki, M., Schismenou, E. & Nikolioudakis, N. Spawning stock, egg production and larval survival in relation to small pelagic fish recruitment. Mar. Ecol. Prog. Ser. 2018, 113–136 (2018).
Google Scholar 
Sampey, A., Meekan, M. G., Carleton, J. H., McKinnon, A. D. & McCormick, M. I. Temporal patterns in distributions of tropical fish larvae on the North West Shelf of Australia. Mar. Freshw. Res. 55, 473–487 (2004).
Google Scholar 
Rezagholinejad, S., Arshad, A., Nurul Amin, S. M. & Ehteshami, F. The influence of environmental parameters on fish larval distribution and abundance in the mangrove estuarine area of Marudu bay, Sabah, Malaysia. J. Surv. Fish. Sci. 2, 67–78 (2016).Shuai, F. et al. Temporal patterns of larval fish occurrence in a large subtropical river. PLoS ONE 11, e0156556 (2016).Nordlund, L. M. et al. Intertidal zone management in the Western Indian Ocean: assessing current status and future possibilities using expert opinions. Ambio 43, 1006–1019 (2014).PubMed 

Google Scholar 
De Oliveira, E. C. & Ferreira, E. J. G. Spawning areas, dispersion and microhabitats of fish larvae in the Anavilhanas Ecological Station, rio Negro, Amazonas State Brazil. Neotrop. Ichthyol. 6, 559–566 (2008).
Google Scholar 
Caley, M. J. et al. Recruitment and the local dynamics of open marine populations. Ann. Rev. Ecol. Syst. 27, 477–500 (1996).
Google Scholar 
Crochelet, E. et al. Validation of a fish larvae dispersal model with otolith data in the Western Indian Ocean and implications for marine spatial planning in data-poor regions. Ocean Coast Manag. 86, 13–21 (2013).
Google Scholar 
Gilroy, J. J. & Edwards, D. P. Source-sink dynamics: a neglected problem for landscape-scale biodiversity conservation in the tropics. Curr. Landsc. Ecol. Rep. 2, 51–60 (2017).
Google Scholar 
Little, M. C., Reay, P. J. & Grove, S. J. Distribution gradients of ichthyoplankton in an East African mangrove creek. Estuar. Coast. Shelf Sci. 26, 669–677 (1988).ADS 

Google Scholar 
Hedberg, P., Rybak, F. F., Gullström, M., Jiddawi, N. S. & Winder, M. Fish larvae distribution among different habitats in coastal East Africa. J. Fish Biol. 94, 29–39 (2019).CAS 
PubMed 

Google Scholar 
Heylen, B. C. & Nachtsheim, D. A. Bio-telemetry as an essential tool in movement ecology and marine conservation. In: Jungblut, S., Liebich, V. & Bode, M. (Eds), YOUMARES 8–Oceans Across Boundaries: Learning From Each Other. 83–107 (Springer, 2018).Parrish, J. Fish communities of interacting shallow-water habitats in tropical oceanic regions. Mar. Ecol. Prog. Ser. 58, 143–160 (1989).ADS 

Google Scholar 
McMahon, K. W., Berumen, M. L. & Thorrold, S. R. Linking habitat mosaics and connectivity in a coral reef seascape. Proc. Natl. Acad. Sci. USA 109, 15372–15376 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Carlson, R. R. et al. Synergistic benefits of conserving land-sea ecosystems. Glob. Ecol. Conserv. 28, e01684 (2021).Mwaluma, J. M. et al. Assemblage structure and distribution of fish larvae on the North Kenya Banks during the Southeast Monsoon season. Ocean Coast. Manag. 212, 105800 (2021).Joyeux, J. C. The abundance of fish larvae in estuaries: Within-tide variability at inlet and immigration. Estuaries 22, 889–904 (1999).
Google Scholar 
Able, K. W., Valenti, J. L. & Grothues, T. M. Fish larval supply to and within a lagoonal estuary: Multiple sources for Barnegat Bay New Jersey. Environ. Biol. Fish. 100, 663–683 (2017).
Google Scholar 
McClanahan, T. R. Seasonality in East Africa’s coastal waters. Mar. Ecol. Prog. Ser. 44, 191–199 (1988).ADS 

Google Scholar 
Aceves-Medina, G. et al. Distribution and abundance of the ichthyoplankton assemblages and its relationships with the geostrophic flow along the southern region of the California current. Lat. Am. J. Aquat. Res. 46, 104–119 (2018).
Google Scholar 
Gray, C. A. & Miskiewicz, A. G. Larval fish assemblages in south-east Australian coastal waters: Seasonal and spatial structure. Estuar. Coast. Shelf Sci. 50, 549–570 (2000).ADS 

Google Scholar 
Jiménez, M. P., Sánchez-Leal, R. F., González, C., García-Isarch, E. & García, A. Oceanographic scenario and fish larval distribution off Guinea-Bissau (north-west Africa). J. Mar. Biolog. Assoc. UK 95, 435–452.Mwaluma, J. M., Kaunda-Arara, B. & Rasowo, J. Diel and lunar variations in larval supply to Malindi Marine Park, Kenya. West Ind. Ocean J. Mar. Sci. 13, 57–67 (2014).
Google Scholar 
Stephens, J. S. Jr., Jordan, G. A., Morris, P. A., Singer, M. M. & McGowen, G. E. Can we relate larval fish abundance to recruitment or population stability? A preliminary analysis of recruitment to a temperate rocky reef. CalCOFI Rep. 27, 65–83 (1986).
Google Scholar 
Green, B. C., Smith, D. J., Grey, J. & Underwood, G. J. C. High site fidelity and low site connectivity in temperate salt marsh fish populations: A stable isotope approach. Oecologia 168, 245–255 (2012).ADS 
PubMed 

Google Scholar 
Green, J. M. & Wroblewski, J. S. Movement patterns of Atlantic cod in Gilbert Bay, Labrador: Evidence for bay residency and spawning site fidelity. J. Mar. Biolog. Assoc. UK 80, 1077–1085 (2000).
Google Scholar 
Grüss, A., Kaplan, D. M. & Hart, D. R. Relative impacts of adult movement, larval dispersal and harvester movement on the effectiveness of reserve networks. PLoS ONE 6, e19960 (2011).Luiz, O. J. et al. Adult and larval traits as determinants of geographic range size among tropical reef fishes. Proc. Natl. Acad. Sci. USA 110, 16498–16502 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Macpherson, E. & Raventos, N. Relationship between pelagic larval duration and geographic distribution of Mediterranean littoral fishes. Mar. Ecol. Prog. Ser. 327, 257–265 (2006).ADS 

Google Scholar 
Green, A. L. et al. Larval dispersal and movement patterns of coral reef fishes, and implications for marine reserve network design. Biol. Rev. 90, 1215–1247 (2015).PubMed 

Google Scholar 
Taylor, M. D., Laffan, S. D., Fielder, D. S. & Suthers, I. M. Key habitat and home range of mulloway Argyrosomus japonicus in a south-east Australian estuary: Finding the estuarine niche to optimise stocking. Mar. Ecol. Prog. Ser. 328, 237–247 (2006).ADS 

Google Scholar 
Manson, F. J., Loneragan, N. R., Skilleter, G. A. & Phinn, S. R. An evaluation of the evidence for linkages between mangroves and fisheries: A synthesis of the literature and identification of research directions. Oceanogr. Mar. Biol. 43, 483–513 (2005).
Google Scholar 
Pattrick, P. & Strydom, N. A. Composition, abundance, distribution and seasonality of larval fishes in the shallow nearshore of the proposed Greater Addo Marine Reserve, Algoa Bay South Africa. Estuar. Coast. Shelf Sci. 79, 251–262 (2008).ADS 

Google Scholar 
Sato, N., Asahida, T., Terashima, H., Hurbungs, M. D. & Ida, H. Species composition and dynamics of larval and juvenile fishes in the surf zone of Mauritius. Environ. Biol. Fish. 81, 229–238 (2008).
Google Scholar 
Jaonalison, H., Mahafina, J. & Ponton, D. Fish post-larvae assemblages at two contrasted coral reef habitats in southwest Madagascar. Reg. Stud. Mar. Sci 6, 62–74 (2016).
Google Scholar 
Azmir, I. A., Esa, Y., Amin, S. M. N., Yasin, I. S. & Yusof, F. ZMd. Identification of larval fish in mangrove areas of Peninsular Malaysia using morphology and DNA barcoding methods. J. Appl. Ichthyol. 33, 998–1006 (2017).CAS 

Google Scholar 
Macedo-Soares, L. C. P., Freire, A. S. & Muelbert, J. H. Small-scale spatial and temporal variability of larval fish assemblages at an isolated oceanic island. Mar. Ecol. Prog. Ser. 444, 207–222 (2012).ADS 

Google Scholar 
Monteleone, D. M. Seasonality and abundance of ichthyoplankton in great South Bay, New York. Estuaries 15, 230–238 (1992).
Google Scholar 
Ara, R., Arshad, A., Amin, S. M. & Mazlan, A. G. Temporal and spatial distribution of fish larvae in different ecological habitats. Asian J. Anim. Vet. Adv. 8, 53–62 (2013).
Google Scholar 
Abu El-Regal, M. Abundance and diversity of coral reef fish larvae at Hurghada, Egyptian Red Sea. Egypt. J. Aquat. Biol. Fish. 12, 17–33 (2008).
Google Scholar 
Bialetzki, A., Nakatani, K., Sanches, P. V., Baumgartner, G. & Gomes, L. C. Larval fish assemblage in the Baía River (Mato Grosso do Sul State, Brazil): temporal and spatial patterns. Environ. Biol. Fish. 73, 37–47 (2005).
Google Scholar 
Dudley, B., Tolimieri, N. & Montgomery, J. Swimming ability of the larvae of some reef fishes from New Zealand waters. Mar. Freshw. Res. 51, 783–787. https://doi.org/10.1071/MF00062 (2000).Article 

Google Scholar 
Hare, J. A. et al. Biophysical mechanisms of larval fish ingress into Chesapeake Bay. Mar. Ecol. Prog. Ser. 303, 295–310 (2005).ADS 

Google Scholar 
Watt-pringle, P. & Strydom, N. A. Habitat use by larval fishes in a temperate South African surf zone. Estuar. Coast. Shelf Sci. 58, 765–774 (2003).ADS 

Google Scholar 
Picapedra, P. H. S., Sanches, P. V. & Lansac-Tôha, F. A. Effects of light-dark cycle on the spatial distribution and feeding activity of fish larvae of two co-occurring species (Pisces: Hypophthalmidae and Sciaenidae) in a neotropical floodplain lake. Braz. J. Biol. 78, 763–772 (2018).CAS 
PubMed 

Google Scholar 
Cederlöf, U., Rydberg, L., Mgendi, M. & Mwaipopo, O. Tidal exchange in a warm tropical lagoon: Chwaka Bay, Zanzibar. Ambio 24, 458–464 (1995).
Google Scholar 
Gullström, M. et al. Assessment of changes in the seagrass-dominated submerged vegetation of tropical Chwaka Bay (Zanzibar) using satellite remote sensing. Estuar. Coast. Shelf Sci. 67, 399–408 (2006).ADS 

Google Scholar 
Gullström, M. et al. Seagrass meadows of Chwaka Bay: ecological, social and management aspects. In: de la Torre-Castro, M., Lyimo, T. J. (Eds) People, nature and research: past, present and future of Chwaka Bay, Zanzibar. ISBN: 978-9987-9559-1-6, Zanzibar Town: 89–109 (WIOMSA, 2012a)Gullström, M. et al. Connectivity and nursery function of shallow-water habitats in Chwaka Bay. In: de la Torre-Castro, M., Lyimo, T. J. (Eds) People, nature and research: past, present and future of Chwaka Bay, Zanzibar. ISBN: 978-9987-9559-1-6, Zanzibar Town: 175–192 (WIOMSA, 2012b)Rehren, J., Wolff, M. & Jiddawi, N. Holistic assessment of Chwaka Bay’s multi-gear fishery—using a trophic modeling approach. J. Mar. Syst. 180, 265–278 (2018).
Google Scholar 
Torell, E., Mmochi, A. & Palmigiano, K. Menai Bay Convernance Baseline. Coastal Resources Center, 1–18 (University of Rhode Island, 2006).Torell, E., Shalli, M., Francis, J., Kalangahe, B. & Munubi, R. Tanzania biodiversity threats assessment: Biodiversity threats and management opportunities for Fumba, Bagamoyo, and Mkuranga. 1–47 (University of Rhode Island, Narragansett, 2007).Jeyaseelan, M. J. P. Manual of fish eggs and larvae from Asian mangrove waters.193 (Paris: UNESCO Publishing, 1998).Mwaluma, J. M., Kaunda-Arara, B. & Strydom, N. A. A guide to commonly occurring larval stages of fishes in Kenyan Coastal Waters. WIOMSA Book Series No. 15. xvi + 73 (WIOMSA, 2014).Leis, J. M. & Carson-Ewart, B. M. (Eds.). The larvae of Indo-Pacific coastal fishes: an identification guide to marine fish larvae (Fauna Malesiana Handbooks 2), 804 (Brill, Leiden, 2000).Strickland, J. D. H. & Parsons, T. R. A practical handbook of seawater analysis, 2nd edn. Vol. 167. 21–26 (Bull. Fish. Res. Bd. Canada, 1972).Clarke, K. R. & Warwick, R. M. Change in Marine Communities: An Approach to Statistical Analysis and Interpretation (PRIMER-E). Plymouth Marine Laboratory, (Plymouth, UK, 2001). More

NATURE PODCAST
09 March 2022

The AI that deciphers ancient Greek graffiti

An artificial intelligence that restores illegible inscriptions, and the project that’s reintroducing lost species in Argentina.

Nick Petrić Howe

&

Benjamin Thompson

Nick Petrić Howe

View author publications

You can also search for this author in PubMed
 Google Scholar

Benjamin Thompson

View author publications

You can also search for this author in PubMed
 Google Scholar

Twitter

Facebook

Email

Subscribe
Subscribe

iTunes
Google Podcast
acast
RSS

Listen to the latest from the world of science, with Nick Petrić Howe and Benjamin Thompson.

Your browser does not support the audio element.

Download MP3

In this episode:00:46 The AI helping historians read ancient textsResearchers have developed an artificial intelligence that can restore and date ancient Greek inscriptions. They hope that it will help historians by speeding up the process of reconstructing damaged texts. Research article: Assael et al.News and Views: AI minds the gap and fills in missing Greek inscriptionsVideo: The AI historian: A new tool to decipher ancient textsIthaca platform08:53 Research HighlightsPollinators prefer nectar with a pinch of salt, and measurements of a megacomet’s mighty size.Research Highlight: Even six-legged diners can’t resist sweet-and-salty snacksResearch Highlight: Huge comet is biggest of its kind11:10 Rewilding ArgentinaThis week Nature publishes a Comment article from a group who aim to reverse biodiversity loss by reintroducing species to areas where they are extinct. We speak to one of the Comment’s authors about the project and their hopes that it might kick start ecosystem restoration.Comment: Rewilding Argentina: lessons for the 2030 biodiversity targets21:02 Briefing ChatWe discuss some highlights from the Nature Briefing. This time, giant bacteria that can be seen with the naked eye, and how record-breaking rainfall has caused major floods in Australia.Science: Largest bacterium ever discovered has an unexpectedly complex cellNew Scientist: Record flooding in Australia driven by La Niña and climate changeThe Conversation: The east coast rain seems endless. Where on Earth is all the water coming from?Subscribe to Nature Briefing, an unmissable daily round-up of science news, opinion and analysis free in your inbox every weekday.Never miss an episode: Subscribe to the Nature Podcast on Apple Podcasts, Google Podcasts, Spotify or your favourite podcast app. Head here for the Nature Podcast RSS feed.

doi: https://doi.org/10.1038/d41586-022-00701-7

Related Articles

Read the paper: Restoring and attributing ancient texts using deep neural networks

Rewilding Argentina: lessons for the 2030 biodiversity targets

Subjects

Machine learning

History

Ecology

Latest on:

Machine learning

AI minds the gap and fills in missing Greek inscriptions
News & Views 09 MAR 22

The evolution, evolvability and engineering of gene regulatory DNA
Article 09 MAR 22

Gran Turismo champion, reimagined urine — the week in infographics
News 15 FEB 22

History

AI minds the gap and fills in missing Greek inscriptions
News & Views 09 MAR 22

Police rely on radiocarbon dating to identify forged paintings
News 09 MAR 22

Restoring and attributing ancient texts using deep neural networks
Article 09 MAR 22

Ecology

Discovery of a Ni2+-dependent guanidine hydrolase in bacteria
Article 09 MAR 22

Rewilding Argentina: lessons for the 2030 biodiversity targets
Comment 07 MAR 22

How itchy vicuñas remade a vast wilderness
Research Highlight 04 MAR 22

Jobs

Principal Investigator / Faculty

San Diego Biomedical Research Institute (SDBRI)
Multiple locations

Research Associate / Light Microscopy Imaging Specialist (m/f/x)

Technische Universität Dresden (TU Dresden)
01069 Dresden, Germany

Bioinformatician (m/f/div)

Max Planck Institute for Plant Breeding Research (MPIPZ)
Cologne, Germany

Research Coordinator / Institute Manager (div/f/m)

Max Planck Institute for Astrophysics (MPA)
Garching near Munich, Germany More