in

Refocusing multiple stressor research around the targets and scales of ecological impacts

  • 1.

    Maxwell, S. L., Fuller, R. A., Brooks, T. M. & Watson, J. E. Biodiversity: the ravages of guns, nets and bulldozers. Nature 536, 143–145 (2016).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 2.

    Threats Classification Scheme (Version 3.2) (International Union for Conservation of Nature and Natural Resources, 2020); https://www.iucnredlist.org/resources/threat-classification-scheme

  • 3.

    Living Planet Report 2018: Aiming Higher (World Wildlife Fund, 2018).

  • 4.

    Halpern, B. S. et al. A global map of human impact on marine ecosystems. Science 319, 948–952 (2008).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 5.

    Halpern, B. S. & Fujita, R. Assumptions, challenges, and future directions in cumulative impact analysis. Ecosphere 4, art131 (2013).

    Article 

    Google Scholar 

  • 6.

    Brook, B. W., Sodhi, N. S. & Bradshaw, C. J. A. Synergies among extinction drivers under global change. Trends Ecol. Evol. 23, 453–460 (2008).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 7.

    Orr, J. A. et al. Towards a unified study of multiple stressors: divisions and common goals across research disciplines. Proc. R. Soc. B Biol. Sci. 287, 20200421 (2020).

    Article 

    Google Scholar 

  • 8.

    Piggott, J. J., Townsend, C. R. & Matthaei, C. D. Reconceptualizing synergism and antagonism among multiple stressors. Ecol. Evol. 5, 1538–1547 (2015).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 9.

    Crain, C. M., Kroeker, K. & Halpern, B. S. Interactive and cumulative effects of multiple human stressors in marine systems. Ecol. Lett. 11, 1304–1315 (2008).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 10.

    Burgess, B. J., Purves, D., Mace, G. & Murrell, D. J. Ecological theory predicts ecosystem stressor interactions in freshwater ecosystems, but highlights the strengths and weaknesses of the additive null model. Preprint at bioRxiv https://doi.org/10.1101/2020.08.10.243972 (2020).

  • 11.

    Didham, R. K., Tylianakis, J. M., Gemmell, N. J., Rand, T. A. & Ewers, R. M. Interactive effects of habitat modification and species invasion on native species decline. Trends Ecol. Evol. 22, 489–496 (2007).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 12.

    Donohue, I. et al. Navigating the complexity of ecological stability. Ecol. Lett. 19, 1172–1185 (2016).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 13.

    Galic, N., Sullivan, L. L., Grimm, V. & Forbes, V. E. When things don’t add up: quantifying impacts of multiple stressors from individual metabolism to ecosystem processing. Ecol. Lett. 21, 568–577 (2018).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 14.

    Kéfi, S. et al. Advancing our understanding of ecological stability. Ecol. Lett. 22, 1349–1356 (2019).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 15.

    Tylianakis, J. M., Didham, R. K., Bascompte, J. & Wardle, D. A. Global change and species interactions in terrestrial ecosystems. Ecol. Lett. 11, 1351–1363 (2008).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 16.

    Ashauer, R. & Jager, T. Physiological modes of action across species and toxicants: the key to predictive ecotoxicology. Environ. Sci. Process Impacts 20, 48–57 (2018).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 17.

    Caswell, H. in Ecotoxicology. A Hierarchical Treatment (eds Newman, M. C. & Jagoe, C. H) 255–292 (CRC Press, 1996).

  • 18.

    Judd, A., Backhaus, T. & Goodsir, F. An effective set of principles for practical implementation of marine cumulative effects assessment. Environ. Sci. Policy 54, 254–262 (2015).

    Article 

    Google Scholar 

  • 19.

    Schafer, R. B. & Piggott, J. J. Advancing understanding and prediction in multiple stressor research through a mechanistic basis for null models. Glob. Change Biol. 24, 1817–1826 (2018).

    Article 

    Google Scholar 

  • 20.

    Boyd, P. W. & Brown, C. J. Modes of interactions between environmental drivers and marine biota. Front. Mar. Sci. 2, 9 (2015).

    Google Scholar 

  • 21.

    Beyer, J. et al. Environmental risk assessment of combined effects in aquatic ecotoxicology: a discussion paper. Mar. Environ. Res. 96, 81–91 (2014).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 22.

    Côté, I. M., Darling, E. S. & Brown, C. J. Interactions among ecosystem stressors and their importance in conservation. Proc. R. Soc. B Biol. Sci. 283, 20152592 (2016).

    Article 

    Google Scholar 

  • 23.

    Kroeker, K. J., Kordas, R. L. & Harley, C. D. Embracing interactions in ocean acidification research: confronting multiple stressor scenarios and context dependence. Biol. Lett. https://doi.org/10.1098/rsbl.2016.0802 (2017).

  • 24.

    De Laender, F. Community- and ecosystem-level effects of multiple environmental change drivers: beyond null model testing. Glob. Change Biol. 24, 5021–5030 (2018).

    Article 

    Google Scholar 

  • 25.

    Goussen, B., Price, O. R., Rendal, C. & Ashauer, R. Integrated presentation of ecological risk from multiple stressors. Sci. Rep. 6, 36004 (2016).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 26.

    Liess, M., Foit, K., Knillmann, S., Schafer, R. B. & Liess, H. D. Predicting the synergy of multiple stress effects. Sci. Rep. 6, 32965 (2016).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 27.

    Van den Brink, P. J. et al. Towards a general framework for the assessment of interactive effects of multiple stressors on aquatic ecosystems: results from the Making Aquatic Ecosystems Great Again (MAEGA) workshop. Sci. Total Environ. 684, 722–726 (2019).

    PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 

  • 28.

    Kooijman, S. A. L. M. Dynamic Energy Budgets in Biological Systems: Applications to Ecotoxicology (Cambridge Univ. Press, 1993).

  • 29.

    Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).

    Article 

    Google Scholar 

  • 30.

    Jeschke, J. M., Kopp, M. & Tollrian, R. Consumer-food systems: why type I functional responses are exclusive to filter feeders. Biol. Rev. 79, 337–349 (2004).

    PubMed 
    Article 

    Google Scholar 

  • 31.

    Bolker, B., Holyoak, M., Krivan, V., Rowe, L. & Schmitz, O. Connecting theoretical and empirical studies of trait-mediated interactions. Ecology 84, 1101–1114 (2003).

    Article 

    Google Scholar 

  • 32.

    Schmitz, O. J., Krivan, V. & Ovadia, O. Trophic cascades: the primacy of trait-mediated indirect interactions. Ecol. Lett. 7, 153–163 (2004).

    Article 

    Google Scholar 

  • 33.

    Abrams, P. A., Menge, B. A., Mittelbach, G. G., Spiller, D. A. & Yodzis, P. in Food Webs: Integration of Patterns and Dynamics (eds G. A. Polis & K. O. Winemiller) 371–395 (Chapman & Hall, 1996).

  • 34.

    Thompson, P. L., MacLennan, M. M. & Vinebrooke, R. D. Species interactions cause non‐additive effects of multiple environmental stressors on communities. Ecosphere 9, e02518 (2018).

    Article 

    Google Scholar 

  • 35.

    Loreau, M. Linking biodiversity and ecosystems: towards a unifying ecological theory. Philos. Trans. R. Soc. B Biol. Sci. 365, 49–60 (2010).

    Article 

    Google Scholar 

  • 36.

    Gonzalez, A. et al. Scaling-up biodiversity-ecosystem functioning research. Ecol. Lett. 23, 757–776 (2020).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 37.

    Adler, P. B. et al. Productivity is a poor predictor of plant species richness. Science 333, 1750–1753 (2011).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 38.

    Ives, A. R. & Carpenter, S. R. Stability and diversity of ecosystems. Science 317, 58–62 (2007).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 39.

    Newman, E. A. Disturbance ecology in the Anthropocene. Front. Ecol. Evol. 7, 147 (2019).

    Article 

    Google Scholar 

  • 40.

    Ohlmann, M. et al. Diversity indices for ecological networks: a unifying framework using Hill numbers. Ecol. Lett. 22, 737–747 (2019).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 41.

    Ohlmann, M. et al. Mapping the imprint of biotic interactions on β‐diversity. Ecol. Lett. 21, 1660–1669 (2018).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 42.

    Brun, P. et al. The productivity–biodiversity relationship varies across diversity dimensions. Nat. Commun. 10, 5691 (2019).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 43.

    Pellissier, L. et al. Comparing species interaction networks along environmental gradients. Biol. Rev. 93, 785–800 (2018).

    PubMed 
    Article 

    Google Scholar 

  • 44.

    Bracewell, S. et al. Qualifying the effects of single and multiple stressors on the food web structure of Dutch drainage ditches using a literature review and conceptual models. Sci. Total Environ. 684, 727–740 (2019).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • 45.

    Kohler, H. R. & Triebskorn, R. Wildlife ecotoxicology of pesticides: can we track effects to the population level and beyond? Science 341, 759–765 (2013).

    PubMed 
    Article 
    CAS 

    Google Scholar 

  • 46.

    Kooijman, S. A. L. M. Dynamic Energy and Mass Budgets in Biological Systems (Cambridge Univ. Press, 2000).

  • 47.

    Stearns, S. C. The Evolution of Life Histories (Oxford Univ. Press, 1992).

  • 48.

    Jackson, M. C., Pawar, S. & Woodward, G. The temporal dynamics of multiple stressor effects: from individuals to ecosystems. Trends Ecol. Evol. https://doi.org/10.1016/j.tree.2021.01.005 (2021).

  • 49.

    Billick, I. & Case, T. J. Higher order interactions in ecological communities: what are they and how can they be detected? Ecology 75, 1529–1543 (1994).

    Article 

    Google Scholar 

  • 50.

    Grilli, J., Barabás, G., Michalska-Smith, M. J. & Allesina, S. Higher-order interactions stabilize dynamics in competitive network models. Nature 548, 210–213 (2017).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 51.

    Gill, R. J., Ramos-Rodriguez, O. & Raine, N. E. Combined pesticide exposure severely affects individual- and colony-level traits in bees. Nature 491, 105–108 (2012).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 52.

    Crespi, E. J., Williams, T. D., Jessop, T. S. & Delehanty, B. Life history and the ecology of stress: how do glucocorticoid hormones influence life‐history variation in animals? Funct. Ecol. 27, 93–106 (2013).

    Article 

    Google Scholar 

  • 53.

    Matthiopoulos, J., Moss, R. & Lambin, X. The kin-facilitation hypothesis for red grouse population cycles: territory sharing between relatives. Ecol. Modell. 127, 53–63 (2000).

    Article 

    Google Scholar 

  • 54.

    Moss, R., Watson, A. & Parr, R. Experimental prevention of a population cycle in red grouse. Ecology 77, 1512–1530 (1996).

    Article 

    Google Scholar 

  • 55.

    Kaiser-Bunbury, C. N. et al. Ecosystem restoration strengthens pollination network resilience and function. Nature 542, 223–227 (2017).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 56.

    Lever, J. J., van Nes, E. H., Scheffer, M. & Bascompte, J. The sudden collapse of pollinator communities. Ecol. Lett. 17, 350–359 (2014).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 57.

    Schmitz, O. J. Press perturbations and the predictability ofecological interactions in a food web. Ecology 78, 55–69 (1997).

    Google Scholar 

  • 58.

    Ernest, S. K. M. et al. Thermodynamic and metabolic effects on the scaling of production and population energy use. Ecol. Lett. 6, 990–995 (2003).

    Article 

    Google Scholar 

  • 59.

    Price, P. B. & Sowers, T. Temperature dependence of metabolic rates for microbial growth, maintenance, and survival. Proc. Natl Acad. Sci. USA 101, 4631–4636 (2004).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 60.

    Apple, J. K., Del Giorgio, P. A. & Kemp, W. M. Temperature regulation of bacterial production, respiration, and growth efficiency in a temperate salt-marsh estuary. Aquat. Microb. Ecol. 43, 243–254 (2006).

    Article 

    Google Scholar 

  • 61.

    Pawar, S., Dell, A. I., Savage, V. M. & Knies, J. L. Real versus artificial variation in the thermal sensitivity of biological traits. Am. Nat. 187, E41–E52 (2016).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 62.

    Dell, A. I., Pawar, S. & Savage, V. M. Systematic variation in the temperature dependence of physiological and ecological traits. Proc. Natl Acad. Sci. USA 108, 10591–10596 (2011).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 63.

    Yee, E. & Murray, S. Effects of temperature on activity, food consumption rates, and gut passage times of seaweed-eating Tegula species (Trochidae) from California. Mar. Biol. 145, 895–903 (2004).

    Article 

    Google Scholar 

  • 64.

    Savage, V. M., Gillooly, J. F., Brown, J. H., West, G. B. & Charnov, E. L. Effects of body size and temperature on population growth. Am. Nat. 163, E429–E441 (2004).

    Article 

    Google Scholar 

  • 65.

    Vasseur, D. A. et al. Increased temperature variation poses a greater risk to species than climate warming. Proc. R. Soc. B Biol. Sci. https://doi.org/10.1098/rspb.2013.2612 (2014).

  • 66.

    Vasseur, D. A. & McCann, K. S. A mechanistic approach for modeling temperature-dependent consumer-resource dynamics. Am. Nat. 166, 184–198 (2005).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 67.

    Gilbert, B. et al. A bioenergetic framework for the temperature dependence of trophic interactions. Ecol. Lett. 17, 902–914 (2014).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 68.

    Binzer, A., Guill, C., Brose, U. & Rall, B. C. The dynamics of food chains under climate change and nutrient enrichment. Philos. Trans. R. Soc. B Biol. Sci. 367, 2935–2944 (2012).

    Article 

    Google Scholar 

  • 69.

    Binzer, A., Guill, C., Rall, B. C. & Brose, U. Interactive effects of warming, eutrophication and size structure: impacts on biodiversity and food-web structure. Glob. Change Biol. 22, 220–227 (2016).

    Article 

    Google Scholar 

  • 70.

    Sentis, A., Binzer, A. & Boukal, D. S. Temperature-size responses alter food chain persistence across environmental gradients. Ecol. Lett. 20, 852–862 (2017).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 71.

    Robinson, S. I., McLaughlin, Ó. B., Marteinsdóttir, B. & O’Gorman, E. J. Soil temperature effects on the structure and diversity of plant and invertebrate communities in a natural warming experiment. J. Anim. Ecol. 87, 634–646 (2018).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 72.

    McKee, D. et al. Response of freshwater microcosm communities to nutrients, fish, and elevated temperature during winter and summer. Limnol. Oceanogr. 48, 707–722 (2003).

    Article 

    Google Scholar 

  • 73.

    McKee, D. et al. Macro-zooplankter responses to simulated climate warming in experimental freshwater microcosms. Freshw. Biol. 47, 1557–1570 (2002).

    Article 

    Google Scholar 

  • 74.

    Allen, A., Gillooly, J. & Brown, J. Linking the global carbon cycle to individual metabolism. Funct. Ecol. 19, 202–213 (2005).

    Article 

    Google Scholar 

  • 75.

    Anderson, K. J., Allen, A. P., Gillooly, J. F. & Brown, J. H. Temperature‐dependence of biomass accumulation rates during secondary succession. Ecol. Lett. 9, 673–682 (2006).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 76.

    Clarke, A. & Fraser, K. Why does metabolism scale with temperature? Funct. Ecol. 18, 243–251 (2004).

    Article 

    Google Scholar 

  • 77.

    Sokolova, I. M. & Lannig, G. Interactive effects of metal pollution and temperature on metabolism in aquatic ectotherms: implications of global climate change. Clim. Res. 37, 181–201 (2008).

    Article 

    Google Scholar 

  • 78.

    Petchey, O. L., Brose, U. & Rall, B. C. Predicting the effects of temperature on food web connectance. Philos. Trans. R. Soc. B Biol. Sci. 365, 2081–2091 (2010).

    Article 

    Google Scholar 

  • 79.

    Bellard, C., Bertelsmeier, C., Leadley, P., Thuiller, W. & Courchamp, F. Impacts of climate change on the future of biodiversity. Ecol. Lett. 15, 365–377 (2012).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 80.

    Relyea, R. A. The impact of insecticides and herbicides on the biodiversity and productivity of aquatic communities. Ecol. Appl. 15, 618–627 (2005).

    Article 

    Google Scholar 

  • 81.

    Beketov, M. A., Kefford, B. J., Schäfer, R. B. & Liess, M. Pesticides reduce regional biodiversity of stream invertebrates. Proc. Natl Acad. Sci. USA 110, 11039–11043 (2013).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 82.

    Clements, W. H. & Rohr, J. R. Community responses to contaminants: using basic ecological principles to predict ecotoxicological effects. Environ. Toxicol. Chem. 28, 1789–1800 (2009).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 83.

    Case, T. J. An Illustrated Guide to Theoretical Ecology (Oxford Univ. Press, 2000).

  • 84.

    Jeschke, J. M., Kopp, M. & Tollrian, R. Predator functional responses: discriminating between handling and digesting prey. Ecol. Monogr. 72, 95–112 (2002).

    Article 

    Google Scholar 

  • 85.

    Jeschke, J. M. & Tollrian, R. Density-dependent effects of prey defences. Oecologia 123, 391–396 (2000).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 86.

    Jorgensen, C., Ernande, B. & Fiksen, O. Size-selective fishing gear and life history evolution in the Northeast Arctic cod. Evol. Appl. 2, 356–370 (2009).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 87.

    Kuparinen, A., Kuikka, S. & Merila, J. Estimating fisheries-induced selection: traditional gear selectivity research meets fisheries-induced evolution. Evol. Appl. 2, 234–243 (2009).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 88.

    Benítez-López, A. et al. The impact of hunting on tropical mammal and bird populations. Science 356, 180–183 (2017).

    PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 

  • 89.

    Day, T., Abrams, P. A. & Chase, J. M. The role of size-specific predation in the evolution and diversification of prey life histories. Evolution 56, 877–887 (2002).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 90.

    Heino, M., Pauli, B. D. & Dieckmann, U. Fisheries-induced evolution. Annu. Rev. Ecol. Evol. Syst. 46, 461–480 (2015).

    Article 

    Google Scholar 

  • 91.

    Galloway, J. N. et al. The nitrogen cascade. Bioscience 53, 341–356 (2003).

    Article 

    Google Scholar 

  • 92.

    Beman, J. M., Arrigo, K. R. & Matson, P. A. Agricultural runoff fuels large phytoplankton blooms in vulnerable areas of the ocean. Nature 434, 211–214 (2005).

    Article 
    CAS 

    Google Scholar 

  • 93.

    Birk, S. et al. Impacts of multiple stressors on freshwater biota across spatial scales and ecosystems. Nat. Ecol. Evol. 4, 1060–1068 (2020).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 94.

    Rosenzweig, M. L. Paradox of enrichment: destabilization of exploitation ecosystems in ecological time. Science 171, 385–387 (1971).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • 95.

    Oksanen, L., Fretwell, S. D., Arruda, J. & Niemela, P. Exploitation ecosystems in gradients of primary productivity. Am. Nat. 118, 240–261 (1981).

    Article 

    Google Scholar 

  • 96.

    Lotze, H. K. et al. Depletion, degradation, and recovery potential of estuaries and coastal seas. Science 312, 1806–1809 (2006).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • 97.

    Doney, S. C. The growing human footprint on coastal and open-ocean biogeochemistry. Science 328, 1512–1516 (2010).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 98.

    Diaz, R. J. & Rosenberg, R. Spreading dead zones and consequences for marine ecosystems. Science 321, 926–929 (2008).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • 99.

    Duchet, C. et al. Pesticide‐mediated trophic cascade and an ecological trap for mosquitoes. Ecosphere 9, e02179 (2018).

    Article 

    Google Scholar 

  • 100.

    Halstead, N. T. et al. Community ecology theory predicts the effects of agrochemical mixtures on aquatic biodiversity and ecosystem properties. Ecol. Lett. 17, 932–941 (2014).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 101.

    Ferger, S. W. et al. Synergistic effects of climate and land use on avian beta‐diversity. Divers. Distrib. 23, 1246–1255 (2017).

    Article 

    Google Scholar 

  • 102.

    Maris, V. et al. Prediction in ecology: promises, obstacles and clarifications. Oikos 127, 171–183 (2018).

    Article 

    Google Scholar 

  • 103.

    Palmer, M. A. et al. Ecological science and sustainability for the 21st century. Front. Ecol. Environ. 3, 4–11 (2005).

    Article 

    Google Scholar 

  • 104.

    Folt, C. L., Chen, C. Y., Moore, M. V. & Burnaford, J. Synergism and antagonism among multiple stressors. Limnol. Oceanogr. 44, 864–877 (1999).

    Article 

    Google Scholar 

  • 105.

    Grimm, V. & Berger, U. Structural realism, emergence, and predictions in next-generation ecological modelling: synthesis from a special issue. Ecol. Modell. 326, 177–187 (2016).

    Article 

    Google Scholar 

  • 106.

    Geary, W. L. et al. A guide to ecosystem models and their environmental applications. Nat. Ecol. Evol. 4, 1459–1471 (2020).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 107.

    Rosenblatt, A. E., Smith-Ramesh, L. M. & Schmitz, O. J. Interactive effects of multiple climate change variables on food web dynamics: Modeling the effects of changing temperature, CO2, and water availability on a tri-trophic food web. Food Webs https://doi.org/10.1016/j.fooweb.2016.10.002 (2017).

  • 108.

    Bartley, T. J. et al. Food web rewiring in a changing world. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-018-0772-3 (2019).

  • 109.

    CaraDonna, P. J. et al. Interaction rewiring and the rapid turnover of plant–pollinator networks. Ecol. Lett. 20, 385–394 (2017).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 110.

    Gilljam, D., Curtsdotter, A. & Ebenman, B. Adaptive rewiring aggravates the effects of species loss in ecosystems. Nat. Commun. 6, 8412 (2015).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 111.

    Staniczenko, P. P. A., Lewis, O. T., Jones, N. S. & Reed-Tsochas, F. Structural dynamics and robustness of food webs. Ecol. Lett. 13, 891–899 (2010).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 112.

    Thierry, A. et al. Adaptive foraging and the rewiring of size-structured food webs following extinctions. Basic Appl. Ecol. 12, 562–570 (2011).

    Article 

    Google Scholar 

  • 113.

    Petchey, O. L., Beckerman, A. P., Riede, J. O. & Warren, P. H. Size, foraging, and food web structure. Proc. Natl Acad. Sci. USA 105, 4191–4196 (2008).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 114.

    Beckerman, A. P., Petchey, O. L. & Warren, P. H. Foraging biology predicts food web complexity. Proc. Natl Acad. Sci. USA 103, 13745–13749 (2006).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 115.

    O’Gorman, E. J. et al. A simple model predicts how warming simplifies wild food webs. Nat. Clim. Change 9, 611–616 (2019).

    Article 

    Google Scholar 

  • 116.

    Williams, R. J., Brose, U. & Martinez, N. D. in From Energetics to Ecosystems: The Dynamics and Structure of Ecological Systems (eds Rooney, N. et al.) 37–51 (Springer, 2007).

  • 117.

    Blanchard, J. L. et al. How does abundance scale with body size in coupled size‐structured food webs? J. Anim. Ecol. 78, 270–280 (2009).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 118.

    Blanchard, J. L., Heneghan, R. F., Everett, J. D., Trebilco, R. & Richardson, A. J. From bacteria to whales: using functional size spectra to model marine ecosystems. Trends Ecol. Evol. 32, 174–186 (2017).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 119.

    Kerr, S. R. & Dickie, L. M. The Biomass Spectrum: A Predator–Prey Theory of Aquatic Production (Columbia Univ. Press, 2001).

  • 120.

    Adams, M. P. et al. Informing management decisions for ecological networks, using dynamic models calibrated to noisy time-series data. Ecol. Lett. 23, 607–619 (2020).

    PubMed 
    Article 

    Google Scholar 

  • 121.

    Bode, M. et al. Revealing beliefs: using ensemble ecosystem modelling to extrapolate expert beliefs to novel ecological scenarios. Methods Ecol. Evol. 8, 1012–1021 (2017).

    Article 

    Google Scholar 

  • 122.

    McGowan, C. P., Runge, M. C. & Larson, M. A. Incorporating parametric uncertainty into population viability analysis models. Biol. Conserv. 144, 1400–1408 (2011).

    Article 

    Google Scholar 

  • 123.

    Delmas, E., Brose, U., Gravel, D., Stouffer, D. B. & Poisot, T. Simulations of biomass dynamics in community food webs. Methods Ecol. Evol. 8, 881–886 (2017).

    Article 

    Google Scholar 

  • 124.

    Scott, F., Blanchard, J. L. & Andersen, K. H. mizer: an R package for multispecies, trait-based and community size spectrum ecological modelling. Methods Ecol. Evol. 5, 1121–1125 (2014).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 125.

    Nakagawa, S. & Cuthill, I. C. Effect size, confidence interval and statistical significance: a practical guide for biologists. Biol. Rev. 82, 591–605 (2007).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 126.

    Tabi, A., Petchey, O. L. & Pennekamp, F. Warming reduces the effects of enrichment on stability and functioning across levels of organisation in an aquatic microbial ecosystem. Ecol. Lett. 22, 1061–1071 (2019).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 127.

    O’Brien, A. L., Dafforn, K. A., Chariton, A. A., Johnston, E. L. & Mayer-Pinto, M. After decades of stressor research in urban estuarine ecosystems the focus is still on single stressors: a systematic literature review and meta-analysis. Sci. Total Environ. 684, 753–764 (2019).

    PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 

  • 128.

    Hampton, S. E. et al. Quantifying effects of abiotic and biotic drivers on community dynamics with multivariate autoregressive (MAR) models. Ecology 94, 2663–2669 (2013).

    PubMed 
    Article 

    Google Scholar 

  • 129.

    Ives, A. R., Dennis, B., Cottingham, K. L. & Carpenter, S. R. Estimating community stability and ecological interactions from time-series data. Ecol. Monogr. 73, 301–330 (2003).

    Article 

    Google Scholar 

  • 130.

    Geary, W. L., Nimmo, D. G., Doherty, T. S., Ritchie, E. G. & Tulloch, A. I. T. Threat webs: reframing the co‐occurrence and interactions of threats to biodiversity. J. Appl. Ecol. 56, 1992–1997 (2019).

    Google Scholar 

  • 131.

    Gillooly, J. F., Brown, J. H., West, G. B., Savage, V. M. & Charnov, E. L. Effects of size and temperature on metabolic rate. Science 293, 2248–2251 (2001).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 132.

    Rall, B. C. et al. Universal temperature and body-mass scaling of feeding rates. Philos. Trans. R. Soc. Lond. B Biol. Sci. 367, 2923–2934 (2012).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 133.

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

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 134.

    Brennan, G. L., Colegrave, N. & Collins, S. Evolutionary consequences of multidriver environmental change in an aquatic primary producer. Proc. Natl Acad. Sci. USA 114, 9930–9935 (2017).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 135.

    De Valpine, P. & Hastings, A. Fitting population models incorporating process noise and observation error. Ecol. Monogr. 72, 57–76 (2002).

    Article 

    Google Scholar 

  • 136.

    Ellner, S. P., Seifu, Y. & Smith, R. H. Fitting population dynamic models to time‐series data by gradient matching. Ecology 83, 2256–2270 (2002).

    Article 

    Google Scholar 

  • 137.

    Blanchard, J. L. A rewired food web. Nature 527, 173–174 (2015).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 138.

    Law, R., Plank, M. J., James, A. & Blanchard, J. L. Size‐spectra dynamics from stochastic predation and growth of individuals. Ecology 90, 802–811 (2009).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 139.

    Hampton, S. E., Scheuerell, M. D. & Schindler, D. E. Coalescence in the Lake Washington story: interaction strengths in a planktonic food web. Limnol. Oceanogr. 51, 2042–2051 (2006).

    Article 

    Google Scholar 

  • 140.

    Ives, A. R. Predicting the response of populations to environmental change. Ecology 76, 926–941 (1995).

    Article 

    Google Scholar 


  • Source: Ecology - nature.com

    Institute Professor Paula Hammond named to White House science council

    Mycorrhizal types influence island biogeography of plants