Transferability of stream benthic macroinvertebrate distribution models to drought-related conditions
Open accessPreprint — published as 10.1016/j.ecoinf.2025.103395 · Paper page
Abstract
Freshwater ecosystems increasingly face pressures from climate change induced extreme events, like droughts, posing significant threats to biodiversity. While Species Distribution Models (SDMs) serve as vital tools for predicting species responses to environmental shifts, their transferability to novel environmental conditions, especially during and after drought remains poorly understood. In this study, we delve into the transferability of SDMs for freshwater macroinvertebrates from drought-free to drought-influenced conditions. We examine how sensitive the transferability is to traits such as tolerance scores according to their distribution along longitudinal gradients, as well as the used modelling method. We constructed and validated SDMs for freshwater macroinvertebrates in a central German catchment under drought-free conditions using four different algorithms (Generalised linear models; GLM, Spatial Stream Networks; SSN, Random Forests; RF, and Maximum Entropy; MaxEnt). We then projected these models to environmental conditions influenced by drought, and obtained their transferability by computing the difference in accuracy when predicting under drought-free and drought-influenced conditions (AUCgap). Our findings reveal a marked reduction in SDM accuracy under drought conditions, illustrating the challenges of accurately predicting species distributions into novel environmental conditions. Our results show a slightly better transferability when using SSN and RF. In addition, we observed that SDM transferability can be influenced by species tolerance, with sensitive and tolerant species presenting higher AUCgap (i.e. lower transferability). Furthermore, we found that when sensitive species were modelled using SSNs, the AUCgap was reduced. Our study underscores the limitations of SDMs in capturing species responses to drought and advocates for integrating ecologically relevant predictors and modelling methods that account for the stream connectivity to allow for robust predictions. These considerations could enhance the ability of SDMs to effectively estimate the impacts of extreme events on freshwater biodiversity.
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Cites
An analysis of variance test for normality (complete samples)
SHAPIRO, S. S., & WILK, M. B. (1965) · Biometrika · doi:10.1093/biomet/52.3-4.591 · Cited by 23,480 · cited 1× · see citing works
“Additionally, Shapiro-Wilk tests (Shapiro & Wilk, 1965) were conducted to assess the normality of the resulting AUCgap (Fig.”
Method(2006) · doi:10.6027/9789289335447-6-en · Cited by 7,543 · cited 1×
“These processes may exhibit spatially autocorrelated patterns that are not adequately captured by standard SDMs (Merow et al., 2014; Barry & Elith, 2006) and which could become more significant in the context of habitat fragmentation resulting from droughts (Robson et al., 2011; Aspin et al.,…”
Allouche, O., Tsoar, A., & Kadmon, R. (2006) · Journal of Applied Ecology · doi:10.1111/j.1365-2664.2006.01214.x · Cited by 5,518 · see citing works
More Intense, More Frequent, and Longer Lasting Heat Waves in the 21st Century
Meehl, G. A., & Tebaldi, C. (2004) · Science · doi:10.1126/science.1098704 · Cited by 4,322 · see citing works
Cross‐validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure
Roberts, D. R., Bahn, V., Ciuti, S., Boyce, M. S., Elith, J., Guillera‐Arroita, G., Hauenstein, S., Lahoz‐Monfort, J. J., Schröder, B., Thuiller, W., et al. (2017) · Ecography · doi:10.1111/ecog.02881 · Cited by 3,689 · see citing works
Selecting pseudo‐absences for species distribution models: how, where and how many?
Barbet-Massin, M., Jiguet, F., Albert, C., & Thuiller, W. (2012) · Methods in Ecology and Evolution · doi:10.1111/j.2041-210x.2011.00172.x · Cited by 2,715 · see citing works
Hernández, P., Graham, C., Master, L., & Albert, D. L. (2006) · Ecography · doi:10.1111/j.0906-7590.2006.04700.x · Cited by 2,557 · see citing works
A Method for Assessing Hydrologic Alteration within Ecosystems
Richter, B., Baumgartner, J. V., Powell, J. L., & Braun, D. P. (1996) · Conservation Biology · doi:10.1046/j.1523-1739.1996.10041163.x · Cited by 2,528 · see citing works
A maximum entropy approach to species distribution modeling
Phillips, S., Dudík, M., & Schapire, R. (2004) · International Conference on Machine Learning · doi:10.1145/1015330.1015412 · Cited by 2,457 · see citing works
Predicting species distributions for conservation decisions
Guisan, A., Tingley, R., Baumgartner, J., Naujokaitis‐Lewis, I., Sutcliffe, P. R., Tulloch, A., Regan, T., Brotóns, L., McDonald-Madden, E., Mantyka-Pringle, C., et al. (2013) · Ecology Letters · doi:10.1111/ele.12189 · Cited by 2,040 · see citing works
Flood risk and climate change: global and regional perspectives
Kundzewicz, Z. W., Kanae, S., Seneviratne, S. I., Handmer, J. W., Nicholls, N., Peduzzi, P., Mechler, R., Bouwer, L. M., Arnell, N. W., Mach, K. J., et al. (2013) · Hydrological Sciences Journal · doi:10.1080/02626667.2013.857411 · Cited by 1,697 · see citing works
Future extreme events in European climate: an exploration of regional climate model projections
Beniston, M., Stephenson, D., Christensen, O., Ferro, C., Frei, C., Goyette, S., Halsnæs, K., Holt, T., Jylhä, K., Koffi, B., et al. (2007) · Climatic Change · doi:10.1007/s10584-006-9226-z · Cited by 1,487 · see citing works
Bending the Curve of Global Freshwater Biodiversity Loss: An Emergency Recovery Plan
Tickner, D., Opperman, J. J., Abell, R., Acreman, M., Arthington, A. H., Bunn, S. E., Cooke, S. J., Dalton, J., Darwall, W., Edwards, G., et al. (2020) · BioScience · doi:10.1093/biosci/biaa002 · Cited by 1,241 · see citing works
Redundancy and the choice of hydrologic indices for characterizing streamflow regimes
Olden, J., & Poff, N. (2003) · River Research and Applications · doi:10.1002/rra.700 · Cited by 1,166 · see citing works
Valavi, R., Guillera-Arroita, G., Lahoz-Monfort, J., & Elith, J. (2021) · Ecological Monographs · doi:10.1002/ecm.1486 · Cited by 798 · see citing works
Outstanding Challenges in the Transferability of Ecological Models.
Yates, K., Bouchet, P., Caley, M., Mengersen, K., Randin, C., Parnell, S., Fielding, A. H., Bamford, A. J., Ban, S., Barbosa, A., et al. (2018) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2018.08.001 · Cited by 675 · see citing works
Owens, H., Campbell, L., Dornak, L., Saupe, E., Barve, N., Soberón, J., Ingenloff, K., Lira-Noriega, A., Hensz, C., Myers, C., et al. (2012) · Ecological Modelling · doi:10.1016/j.ecolmodel.2013.04.011 · Cited by 659 · see citing works
Error and uncertainty in habitat models
Barry, S., & Elith, J. (2006) · Journal of Applied Ecology · doi:10.1111/j.1365-2664.2006.01136.x · Cited by 654 · see citing works
Thuiller, W., Brotóns, L., Araújo, M., & Lavorel, S. (2004) · Ecography · doi:10.1111/j.0906-7590.2004.03673.x · Cited by 653 · cited 1× · see citing works
“In this context, tolerant species may show nonspecific responses to stressors, due to larger withstand thresholds, which SDMs might not fully capture (Thuiller et al., 2004; Guevara et al., 2018).”
BackgroundWhat do we gain from simplicity versus complexity in species distribution models
Merow, C., Smith, M. J., Edwards, T. C., Guisan, A., McMahon, S., Normand, S., Thuiller, W., Wüest, R., Zimmermann, N., & Elith, J. (2014) · Ecography · doi:10.1111/ecog.00845 · Cited by 617 · see citing works
The impacts of drought on freshwater ecosystems: an Australian perspective
Bond, N. R., Lake, P., & Arthington, A. (2008) · Hydrobiologia · doi:10.1007/s10750-008-9326-z · Cited by 614 · see citing works
Drought impacts on the water quality of freshwater systems; review and integration
Mosley, L. (2015) · Earth-Science Reviews · doi:10.1016/j.earscirev.2014.11.010 · Cited by 613 · cited 1× · see citing works
“…droughts can influence abiotic processes, resulting in changes in water quality such as elevated salinity, decreased dissolved oxygen levels, and shifts in nutrient availability in rivers (Mosley, 2015), exceeding critical thresholds within ecological communities (Bailey & van de Pol, 2016).”
Parallels and contrasts in the effects of drought on stream macroinvertebrate assemblages
Boulton, A. (2003) · Freshwater Biology · doi:10.1046/j.1365-2427.2003.01084.x · Cited by 609 · see citing works
Global prevalence of non-perennial rivers and streams
Messager, M., Lehner, B., Cockburn, C., Lamouroux, N., Pella, H., Snelder, T. H., Tockner, K., Trautmann, T., Watt, C., & Datry, T. (2021) · Nature · doi:10.1038/s41586-021-03565-5 · Cited by 606 · see citing works
Schmidt-Kloiber, A., & Hering, D. (2015) · Ecological Indicators · doi:10.1016/j.ecolind.2015.02.007 · Cited by 502 · see citing works
Ensemble modelling of species distribution: the effects of geographical and environmental ranges
Grenouillet, G., Buisson, L., Casajus, N., & Lek, S. (2011) · doi:10.1111/j.1600-0587.2010.06152.x · Cited by 414
Collinearity in ecological niche modeling: Confusions and challenges
Feng, X., Park, D. S., Liang, Y., Pandey, R., & Papeş, M. (2019) · Ecology and Evolution · doi:10.1002/ece3.5555 · Cited by 399 · see citing works
Spatial statistical models that use flow and stream distance
Hoef, J. V. V., Peterson, E., & Theobald, D. (2006) · Environmental and Ecological Statistics · doi:10.1007/s10651-006-0022-8 · Cited by 350
A checklist for maximizing reproducibility of ecological niche models
Feng, X., Park, D. S., Walker, C., Peterson, A. T., Merow, C., & Papeş, M. (2019) · Nature Ecology & Evolution · doi:10.1038/s41559-019-0972-5 · Cited by 323 · see citing works
Climatic vulnerability of the world’s freshwater and marine fishes
Comte, L., & Olden, J. (2017) · Nature Climate Change · doi:10.1038/nclimate3382 · Cited by 320 · see citing works
Increased occurrence of high impact compound events under climate change
Ridder, N., Ukkola, A., Pitman, A. J., & Perkins-Kirkpatrick, S. E. (2022) · npj Climate and Atmospheric Science · doi:10.1038/s41612-021-00224-4 · Cited by 293 · see citing works
The global decline of freshwater megafauna
He, F., Zarfl, C., Bremerich, V., David, J. N. W., Hogan, Z. S., Kalinkat, G., Tockner, K., & Jähnig, S. C. (2019) · Global Change Biology · doi:10.1111/gcb.14753 · Cited by 285 · see citing works
Spatial network structure and amphibian persistence in stochastic environments
Fortuna, M. A., Gómez-Rodríguez, C., & Bascompte, J. (2006) · Proceedings of the Royal Society of London. Biological Sciences · doi:10.1098/rspb.2005.3448 · Cited by 265 · see citing works
Petitpierre, B., Broennimann, O., Kueffer, C., Daehler, C., & Guisan, A. (2017) · Global Ecology and Biogeography · doi:10.1111/geb.12530 · Cited by 255 · see citing works
Mantyka‐Pringle, C., Martin, T. G., Moffatt, D. B., Linke, S., & Rhodes, J. R. (2014) · Journal of Applied Ecology · doi:10.1111/1365-2664.12236 · Cited by 213 · see citing works
Modelling distribution in European stream macroinvertebrates under future climates
Domisch, S., Araújo, M., Bonada, N., Pauls, S., Jähnig, S., & Haase, P. (2013) · Global Change Biology · doi:10.1111/gcb.12107 · Cited by 210 · see citing works
fuzzySim: applying fuzzy logic to binary similarity indices in ecology
Barbosa, A. M. A. (2015) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12372 · Cited by 191 · see citing works
Tackling extremes: challenges for ecological and evolutionary research on extreme climatic events.
Bailey, L. D., & van de Pol, M. (2016) · Journal of Animal Ecology · doi:10.1111/1365-2656.12451 · Cited by 189 · see citing works
Species distribution modelling supports the study of past, present and future biogeographies
Franklin, J. (2023) · Journal of Biogeography · doi:10.1111/jbi.14617 · Cited by 187 · cited 1× · see citing works
“Extrapolating beyond training conditions is necessary when predicting species’ distributions under extreme event conditions (Franklin, 2023), hence, understanding SDM transferability becomes crucial.”
An evaluation of transferability of ecological niche models
Qiao, H., Feng, X., Escobar, L., Peterson, A., Soberón, J., Zhu, G.-P., & Papeş, M. (2018) · Ecography · doi:10.1111/ecog.03986 · Cited by 183 · see citing works
Duque-Lazo, J., van Gils, H., Groen, T., & Navarro-Cerrillo, R. (2016) · Ecological Modelling · doi:10.1016/j.ecolmodel.2015.09.019 · Cited by 171 · see citing works
Guevara, L., Gerstner, B., Kass, J., & Anderson, R. (2018) · Global Change Biology · doi:10.1111/gcb.13992 · Cited by 167 · see citing works
Europe's freshwater biodiversity under climate change: distribution shifts and conservation needs
Markovic, D., Carrizo, S. F., Freyhof, J., Cid, N., Lengyel, S., Scholz, M., Kasperdius, H., & Darwall, W. (2014) · Diversity and Distributions · doi:10.1111/ddi.12232 · Cited by 163 · see citing works
Robson, B., Chester, E., & Austin, C. (2011) · doi:10.1071/mf10062 · Cited by 141
Extreme drought pushes stream invertebrate communities over functional thresholds
Aspin, T. W. H., Khamis, K., Matthews, T. J., Milner, A. M., O’Callaghan, M. J., Trimmer, M., Woodward, G., & Ledger, M. E. (2018) · Global Change Biology · doi:10.1111/gcb.14495 · Cited by 139 · see citing works
Species distribution models have limited spatial transferability for invasive species.
Liu, C., Wolter, C., Xian, W.-W., & Jeschke, J. (2020) · Ecology Letters · doi:10.1111/ele.13577 · Cited by 137 · see citing works
Sarremejane, R., Mykrä, H., Bonada, N., Aroviita, J., & Muotka, T. (2017) · Freshwater Biology · doi:10.1111/fwb.12926 · Cited by 137 · see citing works
Fast and flexible Bayesian species distribution modelling using Gaussian processes
Golding, N., & Purse, B. V. (2016) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12523 · Cited by 130 · see citing works
Konowalik, K., & Nosol, A. (2021) · Scientific Reports · doi:10.1038/s41598-020-80062-1 · Cited by 126
The three Rs of river ecosystem resilience: Resources, recruitment, and refugia
Looy, K. V., Tonkin, J., Floury, M., Leigh, C., Soininen, J., Larsen, S., Heino, J., Poff, N., Delong, M. D., Jähnig, S., et al. (2019) · Rivers Research and Applications: an international journal devoted to river research and management · doi:10.1002/rra.3396 · Cited by 123 · see citing works
Röpke, C. P., Amadio, S., Zuanon, J., Ferreira, E. J. G., Deus, C. P. D., Pires, T. H. S., & Winemiller, K. O. (2017) · Scientific Reports · doi:10.1038/srep40170 · Cited by 122 · cited 1× · see citing works
“Droughts can pose adverse effects on fish migration, due to habitat loss (Röpke et al., 2017), and high flows following droughts can disrupt water quality and lead to increased mortality (King et al., 2012).”
Effects of grain size and niche breadth on species distribution modeling
Connor, T., Hull, V., Viña, A., Shortridge, A. M., Tang, Y., Zhang, J.-D., Wang, F., & Liu, J.-G. (2018) · Ecography · doi:10.1111/ecog.03416 · Cited by 121 · see citing works
Bradter, U., Kunin, W., Altringham, J., Thom, T., & Benton, T. (2013) · Methods in Ecology and Evolution · doi:10.1111/j.2041-210x.2012.00253.x · Cited by 120 · see citing works
Daufresne, M., Bady, P., & Fruget, J. (2007) · Oecologia · doi:10.1007/s00442-006-0655-1 · Cited by 119 · see citing works
Domisch, S., Jähnig, S., Simaika, J., Kuemmerlen, M., & Stoll, S. (2015) · doi:10.1127/fal/2015/0627 · Cited by 112
King, A. J., Tonkin, Z., & Lieshcke, J. (2012) · doi:10.1071/mf11275 · Cited by 108 · cited 1×
“Droughts can pose adverse effects on fish migration, due to habitat loss (Röpke et al., 2017), and high flows following droughts can disrupt water quality and lead to increased mortality (King et al., 2012).”
BackgroundInfluentialVulnerability of European freshwater catchments to climate change
Markovic, D., Carrizo, S. F., Kärcher, O., Walz, A., & David, J. (2017) · Global Change Biology · doi:10.1111/gcb.13657 · Cited by 106 · see citing works
A practical overview of transferability in species distribution modeling
Werkowska, W., Márquez, A. L., Real, R., & Acevedo, P. (2017) · Environmental Reviews · doi:10.1139/er-2016-0045 · Cited by 101 · see citing works
Relationships between lotic macroinvertebrate traits and responses to extreme drought
Chessman, B. (2015) · Freshwater Biology · doi:10.1111/fwb.12466 · Cited by 98 · see citing works
The biota of intermittent rivers and ephemeral streams: aquatic invertebrates
Stubbington, R., Bogan, M., Bonada, N., Boulton, A., Datry, T., Leigh, C., & Vorste, R. V. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-803835-2.00007-3 · Cited by 89 · see citing works
Kuemmerlen, M., Schmalz, B., Guse, B., Cai, Q., Fohrer, N., & Jähnig, S. (2014) · doi:10.1016/j.ecolmodel.2014.01.020 · Cited by 88
Sabater, S., Freixa, A., Jiménez, L., López-Dovál, J., Pace, G., Pascoal, C., Perujo, N., Craven, D., & González-Trujillo, J. D. (2022) · Biological Reviews of The Cambridge Philosophical Society · doi:10.1111/brv.12914 · Cited by 87 · see citing works
Grimmett, L., Whitsed, R., & Horta, A. (2020) · Ecological Modelling · doi:10.1016/j.ecolmodel.2020.109194 · Cited by 81 · see citing works
Radinger, J., Hölker, F., Horký, P., Slavík, O., Dendoncker, N., & Wolter, C. (2015) · Global Change Biology · doi:10.1111/gcb.13183 · Cited by 79 · see citing works
Factors influencing transferability in species distribution models
Rousseau, J. S., & Betts, M. (2022) · Ecography · doi:10.1111/ecog.06060 · Cited by 72 · see citing works
Moderate warming over the past 25 years has already reorganized stream invertebrate communities.
Haase, P., Pilotto, F., Li, F.-Q., Sundermann, A., Lorenz, A., Tonkin, J., & Stoll, S. (2019) · Science of the Total Environment · doi:10.1016/j.scitotenv.2018.12.234 · Cited by 69 · see citing works
Projected effects of Climate‐change‐induced flow alterations on stream macroinvertebrate abundances
Kakouei, K., Kiesel, J., Domisch, S., Irving, K., Jähnig, S. C., & Kail, J. (2018) · Ecology and Evolution · doi:10.1002/ece3.3907 · Cited by 67 · see citing works
Self-citationEffects of agricultural land use on river biota: a meta-analysis
Schürings, C., Feld, C. K., Kail, J., & Hering, D. (2022) · Environmental Sciences Europe · doi:10.1186/s12302-022-00706-z · Cited by 61 · see citing works
Self-citationExtreme events in streams and rivers in arctic and subarctic regions in an uncertain future
Nilsson, C. F., Polvi, L. E., & Lind, L. A. (2015) · Freshwater Biology · doi:10.1111/fwb.12477 · Cited by 56 · see citing works
Polazzo, F., dos Anjos, T. B. O., Arenas‐Sánchez, A., Romo, S., Vighi, M., & Rico, A. (2021) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2021.151052 · Cited by 55 · see citing works
Tessarolo, G., Lobo, J., Rangel, T., & Hortal, J. (2021) · Ecological Indicators · doi:10.1016/j.ecolind.2020.107147 · Cited by 54 · see citing works
Herbst, D., Cooper, S., Medhurst, R., Wiseman, S. W., & Hunsaker, C. (2019) · Freshwater Biology · doi:10.1111/fwb.13270 · Cited by 54 · see citing works
Benkendorf, D. J., Schwartz, S. D., Cutler, D., & Hawkins, C. (2023) · Ecological Modelling · doi:10.1016/j.ecolmodel.2023.110414 · Cited by 52 · see citing works
Charney, N., Record, S., Gerstner, B., Merow, C., Zarnetske, P., & Enquist, B. (2021) · Frontiers in Ecology and Evolution · doi:10.3389/fevo.2021.689295 · Cited by 49 · see citing works
Finding what you don’t know: Testing SDM methods for poorly known species
Radomski, T., Beamer, D. A., Babineau, A. M., Wilson, C., Pechmann, J., & Kozak, K. (2022) · Diversity and Distributions: A journal of biological invasions and biodiversity · doi:10.1111/ddi.13536 · Cited by 44 · see citing works
How well do species distribution models predict occurrences in exotic ranges?
Nguyen, D., Leung, B., & Schrodt, F. (2022) · Global Ecology and Biogeography · doi:10.1111/geb.13482 · Cited by 43 · see citing works
Hysen, L., Nayeri, D., Cushman, S., & Wan, H. (2022) · Ecological Informatics · doi:10.1016/j.ecoinf.2022.101914 · Cited by 42 · see citing works
Wogan, G. O. U. (2016) · PLoS ONE · doi:10.1371/journal.pone.0151024 · Cited by 42 · cited 1× · see citing works
“…as indicated by studies highlighting the dependency of SDM accuracy on species’ ecology, particularly regarding species with broader ranges (Wogan, 2016; Grenouillet et al., 2011; Rousseau & Betts, 2022) and the inability of models to effectively determine their environmental niche.”
Aspin, T. W. H., Hart, K., Khamis, K., Milner, A., O'Callaghan, M., Trimmer, M., Wang, Z.-N., Williams, G. M., Woodward, G., & Ledger, M. (2019) · Freshwater Biology · doi:10.1111/fwb.13259 · Cited by 39 · see citing works
Bertoncin, A. P. D. S., Pinha, G. D., Baumgartner, M., & Mormul, R. (2018) · Hydrobiologia · doi:10.1007/s10750-018-3756-z · Cited by 39 · see citing works
Spatial and temporal changes in daily temperature extremes in China during 1960–2011
Shen, X.-J., Liu, B.-H., Lu, X.-G., & Fan, G. (2017) · Theoretical and Applied Climatology · doi:10.1007/s00704-016-1934-3 · Cited by 35
Fukuda, S., & Baets, B. D. (2016) · Ecological Informatics · doi:10.1016/j.ecoinf.2016.01.005 · Cited by 34
Madsen, P. B., Morabowen, A., Andino, P., Espinosa, R., Cauvy-Fraunié, S., Dangles, O., & Jacobsen, D. (2015) · Ecological Entomology · doi:10.1111/een.12232 · Cited by 34 · see citing works
Navarro, M. Á. P., Sapes, G., Batllori, E., Serra-Diaz, J., Esteve, M. A., & Lloret, F. (2018) · Ecosystems · doi:10.1007/s10021-018-0254-0 · Cited by 32 · see citing works
Huang, J., & Frimpong, E. (2016) · doi:10.1111/fwb.12743 · Cited by 31
Extreme drought effects and recovery patterns in the benthic communities of temperate streams
Calapez, A., Elias, C., Almeida, S., & Feio, M. (2014) · Limnetica · doi:10.23818/limn.33.22 · Cited by 31
Larsen, S., Majone, B., Zulian, P., Stella, E., Bellin, A., Bruno, M. C., & Zolezzi, G. (2021) · Water Resources Research · doi:10.1029/2020wr028496 · Cited by 28 · see citing works
FIDIMO - A free and open source GIS based dispersal model for riverine fish
Radinger, J., Kail, J., & Wolter, C. (2014) · Ecological Informatics · doi:10.1016/j.ecoinf.2013.06.002 · Cited by 28
Current and Future Latitudinal Gradients in Stream Macroinvertebrate Richness Across North America
Shah, D., Domisch, S., Pauls, S., Haase, P., & Jähnig, S. (2014) · Freshwater Science · doi:10.1086/678492 · Cited by 24 · cited 1×
“To date, the distribution of macroinvertebrates has mostly been associated with gradual changes in climatic variables, such as those associated with temperature and precipitation (Domisch et al., 2013; Shah et al., 2014).”
BackgroundElo, M., Jyrkänkallio-Mikkola, J., Ovaskainen, O., Soininen, J., Tolonen, K., & Heino, J. (2021) · Journal of Animal Ecology · doi:10.1111/1365-2656.13453 · Cited by 23
Huang, J., Frimpong, E., & Orth, D. J. (2016) · doi:10.1111/ddi.12430 · Cited by 21
Insights from the past: Invasion trajectory and niche trends of a global freshwater invader
Guareschi, S., Cancellario, T., Oficialdegui, F. J., & Clavero, M. (2023) · Global Change Biology · doi:10.1111/gcb.17059 · Cited by 18 · see citing works
Baldan, D., Kiesel, J., Hauer, C., Jähnig, S., & Hein, T. (2021) · Journal of Applied Ecology · doi:10.1111/1365-2664.13940 · Cited by 18
Self-citationTakolander, A., Forsblom, L., Hellsten, S., Ilmonen, J., Jokinen, A.-P., Kallio, N., Koponen, S., Väkevä, S., & Virtanen, E. A. (2025) · Ecological Modelling · doi:10.1016/j.ecolmodel.2024.110950 · Cited by 17
Collart, F., Broennimann, O., Guisan, A., & Vanderpoorten, A. (2023) · Ecography · doi:10.1111/ecog.06721 · Cited by 17 · see citing works
Hill, M. J., Mathers, K., Little, S., Worrall, T. P., Gunn, J., & Wood, P. J. (2019) · Aquatic Sciences · doi:10.1007/s00027-019-0659-7 · Cited by 17 · cited 1×
“Indeed, the effects of droughts on freshwater macroinvertebrate biodiversity (Hill et al., 2019) have been estimated to be twice as strong as the effects of a gradual warming (Sabater et al., 2023).”
BackgroundNguyen, H. H., Peters, K., Kiesel, J., Welti, E., Gillmann, S. M., Lorenz, A., Jähnig, S., & Haase, P. (2024) · Science of the Total Environment · doi:10.1016/j.scitotenv.2024.172659 · Cited by 16
Self-citationWinkowski, J. J., Olden, J., & Brown, S. (2024) · Transactions of the American Fisheries Society · doi:10.1002/tafs.10454 · Cited by 16
Gies, M., Sondermann, M., Hering, D., & Feld, C. (2015) · doi:10.1127/fal/2014/0600 · Cited by 13
Self-citationFloury, M., Pollock, L. J., Buisson, L., Thuiller, W., Chandesris, A., & Souchon, Y. (2021) · Diversity and Distributions · doi:10.1111/ddi.13411 · Cited by 13 · see citing works
Nguyen, H. H., Kiesel, J., Peters, K., Hering, D., Sinclair, J. S., & Haase, P. (2023) · Landscape Ecology · doi:10.1007/s10980-023-01769-w · Cited by 11
Self-citationGlad, A., & Mallard, F. (2022) · Ecological Informatics · doi:10.1016/j.ecoinf.2022.101646 · Cited by 11
de Albuquerque, F. D., Bateman, H., Ryan, M., & Montgomery, B. J. (2023) · Journal of Biogeography · doi:10.1111/jbi.14733 · Cited by 10
Aldossari, S., Husmeier, D., & Matthiopoulos, J. (2022) · Ecological Informatics · doi:10.1016/j.ecoinf.2022.101803 · Cited by 10
Differing sensitivity of species distribution modelling algorithms to climate data source
Rodríguez-Rey, M., & Jiménez-Valverde, A. (2023) · Ecological Informatics · doi:10.1016/j.ecoinf.2023.102387 · Cited by 9
Ruiz, T., Carrias, J.-F., Bonhomme, C., Farjalla, V. F., Jassey, V., Leflaive, J., Compin, A., Leroy, C., Corbara, B., Srivastava, D., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-12537-2 · Cited by 9 · see citing works
Eger, A., Curtis, J. M. R., Fortin, M., Côté, I., & Guichard, F. (2017) · doi:10.1139/cjfas-2016-0129 · Cited by 9
Assessing multitemporal calibration for species distribution models
Eduardo, A., Liparini, A., Martínez, P. A., Gouveia, S. F., & Riul, P. (2022) · Ecological Informatics · doi:10.1016/j.ecoinf.2022.101787 · Cited by 8
Gies, M., Sondermann, M., Hering, D., & Feld, C. (2015) · Hydrobiologia · doi:10.1007/s10750-015-2280-7 · Cited by 8 · see citing works
Self-citation
Flagged first, then by citations
5 of the 114 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Nukazawa, K., Tanaka, R., & Mineda, H. (2025) · Ecology and Evolution · doi:10.1002/ece3.72411 · Cited by 1 · see citing works
“…of species distribution models for stream macroinvertebrates is often limited under novel environmental conditions such as drought, with only slightly better performance observed for algorithms like spatial stream networks (SSN) and random forests (RF) (e.g., Medina-Madariaga et al. 2025).”
- Authors
- Medina Madariaga, G., Nguyen, H. H., Kiesel, J., Peters, K., Feld, C. K., Jaehnig, S. C., & Cambas, Y. T.
- Affiliations
- University of Duisburg-Essen (DE)
- Christian-Albrechts-Universität zu Kiel (DE)
- ORCID
- Venue
- Ecological Informatics
- Publisher
- openRxiv
- Published
- 2024-06-02
- Volume
- 91
- Pages
- 103395-103395
- ISSN
- 1574-9541 (ISSN-L)
- OpenAlex
- W4413353704
- Cited by
- 1
- References
- 114
- Citations by year
- 2025: 1
- FWCI
- 0.53
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- Ecological Informatics (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- SSRN Electronic Journal (acceptedVersion)
- Universitätsbibliographie, Universität Duisburg-Essen (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Deutsche Forschungsgemeinschaft (426547801)
- Topics
- Physical Sciences › Environmental Science › Ecology › Freshwater macroinvertebrate diversity and ecology
- Physical Sciences › Environmental Science › Ecological Modeling › Species Distribution and Climate Change
- Physical Sciences › Environmental Science › Nature and Landscape Conservation › Fish Ecology and Management Studies
- Keywords
- species distribution modeling, model transferability, drought, stream macroinvertebrates, species tolerance, stream connectivity, random forest, maximum entropy, generalized linear models, climate change, freshwater biodiversity
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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