References

Aiello-Lammens, M. E., Boria, R. A., Radosavljevic, A., Vilela, B., & Anderson, R. P. (2015). spThin: an R package for spatial thinning of species occurrence records for use in ecological niche models.Ecography , 38, 541–545.
Araújo, M. B., Thuiller, W., & Pearson, R. G. (2006). Climate warming and the decline of amphibians and reptiles in Europe. Journal of Biogeography , 33, 1712–1728.
Assis, J., Fragkopoulou, E., Frade, D., Neiva, J., Oliveira, A., Abecasis, D., … & Serrão, E. A. (2020). A fine-tuned global distribution dataset of marine forests. Scientific Data , 7, 119.
Assis, J., Serrão, E. A., C. Coelho, N., Tempera, F., Valero, M., & Alberto, F. (2018a). Past climate changes and strong oceanographic barriers structured low ‐ latitude genetic relics for the golden kelpLaminaria ochroleuca . Journal of Biogeography , 45, 2326–2336.
Assis, J., Tyberghein, L., Bosch, S., Verbruggen, H., Serrão, E. A., & De Clerck, O. (2018b). Bio-ORACLE v2.0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography , 27, 277–284.
Barve, N., Barve, V., Jiménez-Valverde, A., Lira-Noriega, A., Maher, S. P., Peterson, A. T., … & Villalobos, F. (2011). The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecological Modelling , 222, 1810–1819.
Basher, Z., Bowden, D. A., & Costello, M. J. (2018). Global Marine Environment Datasets (GMED). World Wide Web electronic publication. Version 2.0 (Rev.02.2018). Accessed at http://gmed.auckland.ac.nz
Baumgartner, J., & Wilson, P. (2021). rmaxent : Tools for working with Maxent in R. R package version 0.8.5.9000. https://github.com/johnbaums/rmaxent
Baumstark, R., Duffey, R., & Pu, R. L. (2016) Mapping seagrass and colonized hard bottom in Springs Coast, Florida using WorldView-2 satellite imagery. Estuarine, Coastal and Shelf Science , 181, 83–92.
Bell, G. (2017). Evolutionary rescue. Annual Review of Ecology, Evolution and Systematics , 48, 605–627.
Benito Garzón, M., Robson, T. M., & Hampe, A. (2019). ΔTrait SDMs: species distribution models that account for local adaptation and phenotypic plasticity. New Phytologist , 222, 1757–1765.
Blondel, V. D., Guillaume, J. L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics , P10008.
Blonder, B., Morrow, C. B., Maitner, B., Harris, D. J., Lamanna, C., Violle, C., … & Kerkhoff, A. J. (2018). New approaches for delineating n -dimensional hypervolumes. Methods in Ecology and Evolution , 9, 305–319.
Blonder, B., with contributions from Harris, D. J. (2019). Hypervolume: High Dimensional Geometry and Set Operations Using Kernel Density Estimation, Support Vector Machines, and Convex Hulls. R package version 2.0.12. https://CRAN.R-project.org/package=hypervolume
Bosch, S., Tyberghein, L., Deneudt, K., Hernandez, F., & De Clerck, O. (2018). In search of relevant predictors for marine species distribution modelling using the MarineSPEED benchmark dataset. Diversity and Distributions , 24, 144–157.
Breiner, F. T., Nobis, M. P., Bergamini, A., & Guisan, A. (2018). Optimizing ensembles of small models for predicting the distribution of species with few occurrences. Methods in Ecology and Evolution , 9, 802–808.
Brodie, G., Holland, E., N’Yeurt, A. D. R., Soapi, K., & Hills, J. (2020). Seagrasses and seagrass habitats in Pacific small island developing states: Potential loss of benefits via human disturbance and climate change. Marine Pollution Bulletin , 160, 111573.
Cacciapaglia, C., & van Woesik, R. (2018). Marine species distribution modelling and the effects of genetic isolation under climate change.Journal of Biogeography , 45, 154–163.
Carvalho, J. C., & Cardoso, P. (2020). Decomposing the causes for niche differentiation between species using hypervolumes. Frontiers in Ecology and Evolution , 8, 243.
Cardoso, P., Mammola, S., Rigal, F., & Carvalho, J. C. (2020). BAT: Biodiversity Assessment Tools. R package version 2.0.1. https://CRAN.R-project.org/package=BAT
Cardoso, P., Rigal, F., & Carvalho, J. C. (2015). BAT–Biodiversity Assessment Tools, an R package for the measurement and estimation of alpha and beta taxon, phylogenetic and functional diversity.Methods in Ecology and Evolution , 6, 232–236.
Chefaoui, R. M., Duarte, C. M., & Serrão, E. A. (2018). Dramatic loss of seagrass habitat under projected climate change in the Mediterranean Sea. Global Change Biology , 24, 4919–4928.
Cheung, W. W. L., Watson, R., & Pauly, D. (2013) Signature of ocean warming in global fisheries catch. Nature , 497, 365–368.
Clauset, A., Newman, M., & Moore, C. (2004). Finding community structure in very large networks. Physical Review E , 70, 066111.
Coles, R., Grech, A., Rasheed, M., McKenzie, L., Unsworth, R., & Short, F. (2011). Seagrass ecology and threats in the tropical Indo-Pacific bioregion. In: Roberts SP (ed) Seagrass: Ecology, Uses and Threats. Nova Science Publishers, pp 225–239.
Csardi, G., & Nepusz, T. (2006). The IGRAPH software package for complex network research. InterJournal Complex Systems , 1695.
Collart, F., Hedenäs, L., Broennimann, O., Guisan, A., & Vanderpoorten, A. (2021). Intraspecific differentiation: Implications for niche and distribution modelling. Journal of Biogeography , 48, 415–426.
Cushman, S. A., Max, T., Meneses, N., Evans, L. M., Ferrier, S., Honchak, B., … & Allan, G. J. (2014) Landscape genetic connectivity in a riparian foundation tree is jointly driven by climatic gradients and river networks. Ecological Applications , 24, 1000–1014.
D’Amen, M., Zimmermann, N. E., & Pearman, P. B. (2013). Conservation of phylogeographic lineages under climate change. Global Ecology and Biogeography , 22, 93–104.
Dattolo, E., Ruocco, M., Brunet, C., Lorenti, M., Lauritano, C., D’Esposito, D., DeLuca, P., Sanges, R., Mazzuca, S., & Procaccini, G. (2014). Response of the seagrass Posidonia oceanica to different light environments: Insights from a combined molecular and photo-physiological study. Marine Environmental Research , 101, 225–236.
Dormann, C. F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré, G., … Lautenbach, S. (2013). Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography , 36, 27–46.
Duarte, C. M. (1991). Seagrass depth limits. Aquatic Botany , 40, 363–377.
Duarte, B., Martins, I., Rosa, R., Matos, A. R., Roleda, M. Y., Reusch, T. B. H., … & Jueterbock, A. (2018). Climate change impacts on seagrass meadows and macroalgal forests: an integrative perspective on acclimation and adaptation potential. Frontiers in Marine Sciences , 5, 190.
Elith, J., Kearney, M., & Phillips, S. (2010). The art of modelling range‐shifting species. Methods in Ecology and Evolution , 1, 330–342.
Eklöf, J. S., Henriksson, R., & Kautsky, N. (2006) Effects of tropical open-water seaweed farming on seagrass ecosystem structure and function.Marine Ecology Progress Series , 325, 73–84.
Evans, S. M., Vergés, A., & Poore, A. G. B. (2017) Genotypic diversity and short-term response to shading stress in a threatened seagrass: does low diversity mean low resilience? Frontiers in Plant Science , 8, 1417.
Fourqurean, J. W., & Zieman, J. C. (2002) Nutrient content of the seagrass Thalassia testudinum reveals regional patterns of relative availability of nitrogen and phosphorus in the Florida Keys USA. Biogeochemistry , 61, 229–245.
Fortes, M. D. (2018), Seagrass ecosystem conservation in Southeast Asia needs to link science to policy and practice. Ocean and Coastal Management , 159, 51–56.
Franssen, S. U., Gu, J., Bergmann, N., Winters, G., Klostermeier, U. C., Rosenstiel, P., Bornberg-Bauer, E., & Reusch, T. B. H. (2011). Transcriptomic resilience to global warming in the seagrassZostera marina , a marine foundation species. Proceedings of the National Academy of Sciences USA , 108, 19276–19281.
Franssen, S. U., Gu, J., Winters, G., Huylmans, A. K., Wienpahl, I., Sparwel, M., Coyer, J. A., Olsen, J. L., Reusch, T. B. H., Bornberg-Bauer, E. (2014). Genome-wide transcriptomic responses of the seagrasses Zostera marina and Nanozostera noltii under a simulated heatwave confirm functional types. Marine Genomics , 15, 65–73.
Grech, A., Chartrand-Miller, K., Erftemeijer, P., Fonseca, M., McKenzie, L, Rasheed, M., … & Coles, R. (2012). A comparison of threats, vulnerabilities and management approaches in global seagrass bioregions.Environmental Research Letters , 7, 024006.
Green, E. E. P., & Short, F. T. (2003). World atlas of seagrasses. Berkeley, CA: University of California Press.
Guisan, A., Thuiller, W., & Zimmermann, N. E. (2017). Habitat Suitability and Distribution Models: With Applications in R. Cambridge University Press, Cambridge.
Hao, T., Elith, J., Lahoz-Monfort, J. J., & Guillera-Arroita, G. (2020). Testing whether ensemble modelling is advantageous for maximising predictive performance of species distribution models.Ecography , 43, 549–558.
Hernawan, U., van Dijk K., Kendrick, G., Feng, M., Biffin, E., … & McMahon, K. (2017). Historical processes and contemporary ocean currents drive genetic structure in the seagrass Thalassia hemprichii in the Indo-Australian Archipelago. Molecular Ecology , 26, 1008–1021.
Hirzel, A. H., Le Lay, G., Helfer, V., Randin, C., & Guisan, A. (2006). Evaluating the ability of habitat suitability models to predict species presences. Ecological modelling , 199, 142–152.
Hughes, A. R., & Stachowicz, J. J. (2011). Seagrass genotypic diversity increases disturbance response via complementarity and dominance.Journal of Ecology , 99, 445–453.
Hultine, K. R., Grady, K. C., Wood, T. E., Shuster, S. M., Stella, J. C., Whitham, T. G. (2016). Climate change perils for dioecious plant species. Nature Plants , 2, 16109.
Hutchinson, G. (1957). Concluding remarks. Cold Spring Harbor Symposia on Quantitative Biology , 22, 415–427.
Hyndes, G. A., Heck, K. L., Vergés, A., Harvey, E. S., Kendrick, G. A., Lavery, P. S., … & Wilson, S. (2016) Accelerating tropicalization and the transformation of temperate seagrass meadows.BioScience , 66, 938–948.
Ikeda, D. H., Max, T. L., Allan, G. J., Lau, M. K., Shuster, S. M., & Whitham, T. G. (2017). Genetically informed ecological niche models improve climate change predictions. Global Change Biology , 23, 164–176.
Jahnke, M., Gullström, M., Larsson, J., Asplund, M. E., Mgeleka, S., Silas, M. O., … & Nordlund, L. M. (2019a). Population genetic structure and connectivity of the seagrass Thalassia hemprichiiin the Western Indian Ocean is influenced by predominant ocean currents.Ecology and Evolution , 9, 8953–8964.
Jahnke, M., D’Esposito, D., Orru, L., Lamontanara, A., Dattolo, E., Badalamenti, F., Mazzuca, S., Procaccini, G., & Orsini, L. (2019b). Adaptive responses along a depth and a latitudinal gradient in the endemic seagrass Posidonia oceanica . Heredity , 122, 233–243.
Jayathilake, D. R., & Costello, M. J. (2018). A modelled global distribution of the seagrass biome. Biological Conservation , 226, 120–126.
Jiménez‐Valverde, A. (2012). Insights into the area under the receiver operating characteristic curve (AUC) as a discrimination measure in species distribution modelling. Global Ecology and Biogeography , 21, 498–507.
Johansson, M. L., Alberto, F., Reed, D. C., Raimondi, P. T., Coelho, N. C., Young, M. A., … & Serrão, E. A. (2015). Seascape drivers ofMacrocystis pyrifera population genetic structure in the northeast Pacific. Molecular Ecology , 24, 4866–4885.
Jordà, G., Marbà, N., & Duarte, C. M. (2012). Mediterranean seagrass vulnerable to regional climate warming. Nature Climate Change , 2, 821–824.
Jueterbock, A., Franssen, S. U., Bergmann, N., Gu, J., Coyer, J. A., Reusch, T. B. H., Bonberg-Bauer, E., & Olsen, J. L. (2016). Phylogeographic differentiation versus transcriptomic adaptation to warm temperatures in Zostera marina , a globally important seagrass.Molecular Ecology , 25, 5396–5411.
Kass, J. M., Anderson, R. P., Espinosa‐Lucas, A., Juárez‐Jaimes, V., Martínez‐Salas, E., Botello, F., … & Sánchez‐Cordero, V. (2020). Biotic predictors with phenological information improve range estimates for migrating monarch butterflies in Mexico. Ecography , 43, 341–352.
King, N. G., McKeown, N. J., Smale, D. A., & Moore, P. J. (2018). The importance of phenotypic plasticity and local adaptation in driving intraspecific variability in thermal niches of marine macrophytes.Ecography , 41, 1469–1484.
Kramer-Schadt, S., Niedballa, J., Pilgrim, J. D., Schröder, B., Lindenborn, J., Reinfelder, V., … & Wilting, A. (2013). The importance of correcting for sampling bias in MaxEnt species distribution models. Diversity and Distributions , 19, 1366–1379.
Lacap, C. D. A., Vermaat, J. E., Rollon, R. N., & Nacorda, H. M. (2002). Propagule dispersal of the SE Asian seagrasses Enhalus acoroides and Thalassia hemprichii . Marine Ecology Progress Series , 235, 75–80.
Lapointe, B. E., Tomasko, D. A., & Matzie, W. R. (1994) Eutrophication and trophic state classification of seagrass communities in the Florida Keys. Bulletin of Marine Science , 54, 696–717.
Larkum, A. W. D., Pernice, M., Schliep, M., Davey, P., Szabo, M., Raven, J. A., Lichtenberg, M., Brodersen, K. E., & Ralph, P. J. (2018). Photosynthesis and metabolism of seagrasses. In: Larkum AWD, Kendrick GA and Ralph PJ (ed) Seagrasses of Australia: structure, ecology and conservation. Springer, pp 315–342.
Lobo, J. M., Jiménez‐Valverde, A., & Real, R. (2008). AUC: a misleading measure of the performance of predictive distribution models.Global Ecology and Biogeography , 17, 145–151.
Lowe, W. H., & Allendorf, F. W. (2010). What can genetics tell us about population connectivity? Molecular Ecology , 19, 3038–3051.
Lowen, J. B., Hart, D. R., Stanley, R. R., Lehnert, S. J., Bradbury, I. R., & DiBacco, C. (2019). Assessing effects of genetic, environmental, and biotic gradients in species distribution modelling. ICES Journal of Marine Science , 76, 1762–1775.
Lyimo, T. J., Mvungi, E. F., Lugomela, C., & Björk, M. (2006). Seagrass biomass and productivity in seaweed and non-seaweed farming areas in the east coast of Zanzibar, Tanzania. Western Indian Ocean Journal of Marine Science , 5, 141–152.
Lyimo, T. J., Mvungi, E. F., & Mgaya, Y. D. (2008). Abundance and diversity of seagrass and macrofauna in the intertidal areas with and without seaweed farming activities on the east coast of Zanzibar.Tanzania Journal Science , 34, 42–52.
Mammola, S., & Cardoso, P. (2020). Functional diversity metrics using kernel density n ‐dimensional hypervolumes. Methods in Ecology and Evolution , 11, 986–995.
Marín-Guirao, L., Ruiz, J. M., Dattolo, E., Garcia-Munoz, R., & Procaccini, G. (2016). Physiological and molecular evidence of differential short-term heat tolerance in Mediterranean seagrasses.Scientific Reports , 6, 28615.
Martins, A. R. O., & Bandeira, S. O. (2001). Biomass distribution and leaf nutrient concentrations and resorption of Thalassia hemprichii at Inhaca Island, Mozambique. South African Journal of Botany , 67, 439–442.
Mukai, H. (1993). Biogeography of the tropical seagrasses in the western Pacific. Australian Journal of Freshwater Research , 44, 1–17.
Muscarella, R., Galante, P. J., Soley-Guardia, M., Boria, R. A., Kass, J. M., Uriarte, M., & Anderson, R. P. (2014). ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models. Methods in Ecology and Evolution , 5, 1198–1205.
Melo-Merino, S. M., Reyes-Bonilla, H., & Lira-Noriega, A. (2020). Ecological niche models and species distribution models in marine environments: A literature review and spatial analysis of evidence.Ecological Modelling , 415, 108837.
Merilä, J., & Hendry, A. P. (2014). Climate change, adaptation, and phenotypic plasticity: the problem and the evidence. Evolutionary Applications , 7, 1–14.
Miyoshi, K., Hattori, R. S., Strüssmann, C. A., Yokota, M., & Yamamoto, Y. (2020). Phenotypic/genotypic sex mismatches and temperature-dependent sex determination in a wild population of an Old World atherinid, the cobaltcap silverside Hypoatherina tsurugae . Molecular Ecology , 29, 2349–2358.
Olsen, Y. S., Collier, C., Ow, Y. X., & Kendrick, G. A. (2018). Global warming and ocean acidification: effects on Australian seagrass ecosystems. In: Larkum AWD, Kendrick GA and Ralph PJ (ed) Seagrasses of Australia: structure, ecology and conservation. Springer, pp. 705–742.
Orth, R. J., Carruthers, T. J. B., Dennison, W. C., Duarte, C. M., Fourqurean, J. W., Heck, Jr. K. L., … & Williams, S. L. (2006). A global contemporary crisis for seagrass ecosystems. Bioscience , 56, 987–996.
Oney, B., Reineking, B., O’Neill, G., & Kreyling, J. (2013). Intraspecific variation buffers projected climate change impacts onPinus contorta . Ecology and Evolution , 3, 437–449.
Peterson, M. L., Doak, D. F., & Morris, W. F. (2019). Incorporating local adaptation into forecasts of species’ distribution and abundance under climate change. Global Change Biology , 25, 775–793.
Phillips, S. J. (2017). A Brief Tutorial on Maxent. Available from URL: http://biodiversityinformatics.amnh.org/open_source/maxent/. Accessed on 2021-02-10.
Phillips, S. J., Anderson, R. P., Dudík, M., Schapire, R. E., & Blair, M. E. (2017). Opening the black box: An open-source release of Maxent.Ecography , 40, 887–893.
Pinsky, M. L., Selden, R. L., & Kitchel, Z. J. (2020). Climate-driven shifts in marine species ranges: scaling from organisms to communities.Annual Review of Marine Science , 12, 153–179.
Radosavljevic, A., & Anderson, R. P. (2014). Making better Maxent models of species distributions: complexity, overfitting and evaluation.Journal of Biogeography , 41, 629–643.
Ralph, P. J., CRosswell, J. R., Cannard, T., & Steven, A. D. L. (2018). Estimating seagrass blue carbon and policy implications: the Australian perspective. In: Larkum AWD, Kendrick GA and Ralph PJ (ed) Seagrasses of Australia: structure, ecology and conservation. Springer, pp 743–758.
Razgour, O., Forester, B., Taggart, J. B., Bekaert, M., Juste, J., Ibáñez, C., … & Manel, S. (2019). Considering adaptive genetic variation in climate change vulnerability assessment reduces species range loss projections. Proceedings of the National Academy of Sciences USA , 116, 10418–10423.
Repolho, T., Duarte, B., Dionísio, G., Paula, J. R., Lopes, A. R., Rosa, I. C., … & Rosa, R. (2017). Seagrass ecophysiological performance under ocean warming and acidification. Scientific Reports , 7, 41443.
Roberts, D. R., Bahn, V., Ciuti, S., Boyce, M. S., Elith, J., Guillera-Arroita, G., … & Dormann, C. F. (2017). Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography , 40, 913–929.
Robinson, L. M., Elith, J., Hobday, A. J., Pearson, R. G., Kendall, B. E., Possingham, H. P., & Richardson, A. J. (2011). Pushing the limits in marine species distribution modelling: lessons from the land present challenges and opportunities. Global Ecology and Biogeography , 20, 789–802.
Robinson, N. M., Nelson, W. A., Costello, M. J., Sutherland, J. E., & Lundquist, C. J. (2017). A systematic review of marine-based species distribution models (SDMs) with recommendations for best practice.Frontiers in Marine Science , 4, 421.
Saunders, M. I., Leon, J., Phinn, S. R., Callaghan, D. P., O’Brien, K. R., Roelfsema, C. M., Lovelock, C. E., Lyons, M. B., & Mumby, P. J. (2013). Coastal retreat and improved water quality mitigate losses of seagrass from sea level rise. Global Change Biology , 19:2569–2583
Sherman, C. D. H., Smith, T. M., York, P. H., Jarvis, J. C., Ruiz-Montoya, L. R., & Kendrick, G. A. (2018). Reproductive, dispersal and recruitment strategies in Australian seagrasses. In: Larkum AWD, Kendrick GA and Ralph PJ (ed) Seagrasses of Australia: structure, ecology and conservation. Springer, pp 213–256.
Short, F.T., Carruthers, T., Dennison, W., & Waycott M. (2007). Global seagrass distribution and diversity: A bioregional model. Journal of Experimental Marine Biology and Ecology , 350, 3–20.
Smale, D. A., Wernberg, T., Oliver, E. C. J., Thomen, M., Harvey, B. P., Straub, S. C., … & Moore, P. J. (2019). Marine heatwaves threaten global biodiversity and the provision of ecosystem services.Nature Climate Change , 9, 306–312.
Smith, A. B., Godsoe, W., Rodríguez-Sánchez, F., Wang, H. H., & Warren, D. (2019). Niche estimation above and below the species level.Trends in Ecology & Evolution , 34, 260–273.
Spalding, M. D., Fox, H. E., Allen, G. R., Davidson, N., Ferdana, Z. A., … & Robertson, J. (2007) Marine ecoregions of the world: a bioregionalization of coastal and shelf areas. BioScience , 57, 573–583.
Thomson, J. A., Burkholder, D. A., Heithaus, M. R., Fourqurean, J. W., Fraser, M. W., … & Kendrick, G. A. (2015). Extreme temperatures, foundation species, and abrupt ecosystem change: an example from an iconic seagrass ecosystem. Global Change Biology , 21, 1463–1474.
Unsworth, R. K. F., McKenzie, L. J., Nordlund, L. M., & Cullen-Unsworth, L. C. (2018). A changing climate for seagrass conservation? Current Biology , 28, R1221–R1232.
Valavi, R., Elith, J., Lahoz-Monfort, J. J., & Guillera-Arroita, G. (2019). blockCV: An r package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models. Methods in Ecology and Evolution , 10, 225–232.
Vale, C. G., Tarroso, P., & Brito, J. C. (2014). Predicting species distribution at range margins: testing the effects of study area extent, resolution and threshold selection in the Sahara–Sahel transition zone.Diversity and Distributions , 20, 20–33.
Waycott, M., Duarte, C.M., Carruthers, T. J. B., Orth, R. J., Dennison, W. C., Olyarnik, S., … & Williams, S. L. (2009). Accelerating loss of seagrasses across the globe threatens coastal ecosystems.Proceedings of the National Academy of Sciences USA , 106, 12377–12381.
Wu, K. Y., Chen, C. N. N., & Soong, K. (2016). Long distance dispersal potential of two seagrasses Thalassia hemprichii andHalophila ovalis . PLoS ONE , 11, e0156585.
Zhang, J. P., Huang, X. P., & Jiang, Z. J. (2014) Physiological responses of the seagrass Thalassia hemprichii (Ehrenb.) Aschers as indicators of nutrient loading. Marine Pollution Bulletin , 83, 508–515.
Zhang, Z., Capinha, C., Karger, D. N., Turon, X., MacIsaac, H. J., & Zhan, A. (2020a). Impacts of climate change on geographical distributions of invasive ascidians. Marine Environmental Research , 104993.
Zhang, Z., Mammola, S., McLay, C. L., Capinha, C., & Yokota, M. (2020b). To invade or not to invade? Exploring the niche-based processes underlying the failure of a biological invasion using the invasive Chinese mitten crab. Science of The Total Environment , 138815.
Zhang, Z., Mammola, S., Liang, Z., Capinha, C., Wei, Q., Wu, Y., … & Wang, C. (2020c). Future climate change will severely reduce habitat suitability of the Critically Endangered Chinese giant salamander. Freshwater Biology , 65, 971–980.
Zhang, Z., Kass, J. M., Mammola, S., Koizumi, I., Li, X., Tanaka, K., … & Usio, N. (2021). Lineage‐level distribution models lead to more realistic climate change predictions for a threatened crayfish.Diversity and Distributions .