Acta Limnologica Brasiliensia
https://actalb.org/article/doi/10.1590/S2179-975X2723
Acta Limnologica Brasiliensia
Original Article

Incorporating symmetrical and asymmetrical dispersal into Ecological Niche Models in freshwater environments

Incorporando dispersão simétrica e assimétrica em Modelos de Nicho Ecológico em ambientes de água doce

Micael Rosa Parreira; Geiziane Tessarolo; João Carlos Nabout

Downloads: 0
Views: 781

Abstract

Aim: Ecological niche models (ENMs) are based mainly on environmental (mostly climatic) and occurrence data to predict the potential distribution of species. In freshwater habitats, species dispersal is not restricted only by physical barriers but also by the directional movement of the hydrographic network, which can be considered through spatial predictors. Here, we aim to evaluate the effect of including asymmetrical and symmetrical spatial predictors in the potential geographic distribution of a freshwater fish in the Tocantins-Araguaia River basin, Brazil.

Methods: For this, we built models with seven variable sets representing the climatic and spatial models, as well as their interactions.

Results: We found that the overall best models (higher evaluation and lower variation among modeling methods) are those built using AEM (asymmetrical dispersal [i.e., dispersal along the river flow path]), either alone or in combination with environmental variables (ENV). Moreover, the inclusion of asymmetrical dispersal variables, taking into account dispersal limitations of species, decreased the overprediction to climatically suitable but disconnected areas through rivers.

Conclusions: Therefore, future ENM studies, especially those using species groups with directional dispersal, should consider the inclusion of asymmetrical spatial predictors to increase the model’s accuracy and ecological reality.

Keywords

Asymmetric Eigenvector Maps, Ecological Niche Models, directional dispersal, Principal Coordinates of Neighbour Matrices, spatial modeling

Resumo

Objetivo: Os modelos de nicho ecológico (ENMs) são baseados principalmente em dados ambientais (principalmente climáticos) e de ocorrência para prever a distribuição potencial das espécies. Em habitats de água doce, a dispersão das espécies não é restrita apenas por barreiras físicas, mas também pelo movimento direcional da rede hidrográfica, o qual pode ser considerado por meio de preditores espaciais. Neste estudo, nosso objetivo é avaliar o efeito da inclusão de preditores espaciais assimétricos e simétricos na distribuição geográfica potencial de um peixe de água doce na bacia do rio Tocantins-Araguaia, Brasil.

Métodos: Para isso, construímos modelos com sete conjuntos de variáveis representando os modelos climáticos e espaciais, bem como suas interações.

Resultados: Descobrimos que os melhores modelos em geral (maior avaliação e menor variação entre os métodos de modelagem) são aqueles construídos usando AEM (dispersão assimétrica [ou seja, dispersão ao longo do fluxo do rio]), seja sozinho ou em combinação com variáveis ambientais (ENV). Além disso, a inclusão de variáveis de dispersão assimétrica, levando em consideração as limitações de dispersão das espécies, diminuiu a superpredição de áreas climaticamente adequadas, mas desconectadas por rios.

Conclusões: Portanto, futuros estudos de ENM, especialmente aqueles que envolvem grupos de espécies com dispersão direcional, devem considerar a inclusão de preditores espaciais assimétricos para aumentar a precisão do modelo e sua aplicabilidade ecológica.
 

Palavras-chave

Mapas Assimétricos de Autovalores, Modelos de Nicho Ecológico, dispersão direcional, Coordenadas Principais de Matrizes de Vizinhos, modelagem espacial

References

Acevedo, M.A., & Fletcher Junior, R.J., 2017. The proximate causes of asymmetric movement across heterogeneous landscapes. Landsc. Ecol. 32(6), 1285-1297. http://dx.doi.org/10.1007/s10980-017-0522-y.

Acevedo, M.A., Beaudrot, L., Meléndez-Ackerman, E.J., & Tremblay, R.L., 2020. Local extinction risk under climate change in a neotropical asymmetrically dispersed epiphyte. J. Ecol. 108(4), 1553-1564. http://dx.doi.org/10.1111/1365-2745.13361.

Acevedo, M.A., Fletcher Junior, R.J., Tremblay, R.L., & Meléndez-Ackerman, E.J., 2015. Spatial asymmetries in connectivity influence colonization−extinction dynamics. Oecologia 179(2), 415-424. PMid:26054613. http://dx.doi.org/10.1007/s00442-015-3361-z.

Allouche, O., Tsoar, A., & Kadmon, R., 2006. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43(6), 1223-1232. http://dx.doi.org/10.1111/j.1365-2664.2006.01214.x.

Altermatt, F., 2013. Diversity in riverine metacommunities: a network perspective. Aquat. Ecol. 47(3), 365-377. http://dx.doi.org/10.1007/s10452-013-9450-3.

Altermatt, F., Seymour, M., & Martinez, N., 2013. River network properties shape α-diversity and community similarity patterns of aquatic insect communities across major drainage basins. J. Biogeogr. 40(12), 2249-2260. http://dx.doi.org/10.1111/jbi.12178.

Anderson, R.P., 2013. A framework for using niche models to estimate impacts of climate change on species distributions. Ann. N. Y. Acad. Sci. 1297(1), 8-28. PMid:25098379. http://dx.doi.org/10.1111/nyas.12264.

Araújo, M.B., & Peterson, A.T., 2012. Uses and misuses of bioclimatic envelope modeling. Ecology 93(7), 1527-1539. PMid:22919900. http://dx.doi.org/10.1890/11-1930.1.

Arribas, P., Velasco, J., Abellán, P., Sánchez‐Fernández, D., Andujar, C., Calosi, P., Millán, A., Ribera, I., & Bilton, D.T., 2012. Dispersal ability rather than ecological tolerance drives differences in range size between lentic and lotic water beetles (Coleoptera: hydrophilidae). J. Biogeogr. 39(5), 984-994. http://dx.doi.org/10.1111/j.1365-2699.2011.02641.x.

Barbet-Massin, M., Jiguet, F., Albert, C.H., & Thuiller, W., 2012a. Selecting pseudo-absences for species distribution models: how, where and how many? Methods Ecol. Evol. 3(2), 327-338. http://dx.doi.org/10.1111/j.2041-210X.2011.00172.x.

Barbet-Massin, M., Thuiller, W., & Jiguet, F., 2012b. The fate of European breeding birds under climate, land-use and dispersal scenarios. Glob. Change Biol. 18(3), 881-890. http://dx.doi.org/10.1111/j.1365-2486.2011.02552.x.

Barve, N., Barve, V., Jiménez-Valverde, A., Lira-Noriega, A., Maher, S.P., Peterson, A.T., Soberón, J., & Villalobos, F., 2011. The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecol. Model. Elsevier B. 222(11), 1810-1819. http://dx.doi.org/10.1016/j.ecolmodel.2011.02.011.

Bie, T.D., Meester, L.D., Brendonck, L., Martens, K., Goddeeris, B., Ercken, D., Hampel, H., Denys, L., Vanhecke, L., Van der Gucht, K., Van Wichelen, J., Vyverman, W., & Declerck, S.A., 2012. Body size and dispersal mode as key traits determining metacommunity structure of aquatic organisms. Ecol. Lett. 15(7), 740-747. PMid:22583795. http://dx.doi.org/10.1111/j.1461-0248.2012.01794.x.

Blanchet, F.G., Legendre, P., & Borcard, D., 2008. Modelling directional spatial processes in ecological data. Ecol. Modell. 215(4), 325-336. http://dx.doi.org/10.1016/j.ecolmodel.2008.04.001.

Blanchet, F.G., Legendre, P., Maranger, R., Monti, D., & Pepin, P., 2011. Modelling the effect of directional spatial ecological processes at different scales. Oecologia 166(2), 357-368. PMid:21170750. http://dx.doi.org/10.1007/s00442-010-1867-y.

Borcard, D., & Legendre, P., 2002. All-scale spatial analysis of ecological data by means of principal coordinates of neighbour matrices. Ecol. Modell. 153(1-2), 51-68. http://dx.doi.org/10.1016/S0304-3800(01)00501-4.

Breiman, L., 2001. Random forests. Mach. Learn. 45(1), 5-32. http://dx.doi.org/10.1023/A:1010933404324.

Bush, A., & Hoskins, A.J., 2017. Does dispersal capacity matter for freshwater biodiversity under climate change? Freshw. Biol. 62(2), 382-396. http://dx.doi.org/10.1111/fwb.12874.

Cardador, L., Sardà-Palomera, F., Carrete, M., & Mañosa, S., 2014. Incorporating spatial constraints in different periods of the annual cycle improves species distribution model performance for a highly mobile bird species. Divers. Distrib. 20(5), 515-528. http://dx.doi.org/10.1111/ddi.12156.

Carpenter, G., Gillison, A.N., & Winter, J., 1993. Domain - a flexible modeling procedure for mapping potential distributions of plants and animals. Biodivers. Conserv. 2(6), 667-680. http://dx.doi.org/10.1007/BF00051966.

Carvalho, D.L., Sousa-Neves, T., Cerqueira, P.V., Gonsioroski, G., Silva, S.M., Silva, D.P., & Santos, M.P.D., 2017. Delimiting priority areas for the conservation of endemic and threatened Neotropical birds using a niche-based gap analysis. PLoS One 12(2), e0171838. PMid:28187182. http://dx.doi.org/10.1371/journal.pone.0171838.

Cliff, A.D., & Ord, J.k., 1981. Spatial processes - models and applications. London: Pion.

Cunha, H.F., Ferreira, É.D., Tessarolo, G., & Nabout, J.C., 2018. Host plant distributions and climate interact to affect the predicted geographic distribution of a Neotropical termite. Biotropica 50(4), 625-632. http://dx.doi.org/10.1111/btp.12555.

Dalui, S., Khatri, H., Singh, S.K., Basu, S., Ghosh, A., Mukherjee, T., Sharma, L.K., Singh, R., Chandra, K., & Thakur, M., 2020. Fine-scale landscape genetics unveiling contemporary asymmetric movement of red panda (Ailurus fulgens) in Kangchenjunga landscape, India. Sci. Rep. 10(1), 15446. PMid:32963325. http://dx.doi.org/10.1038/s41598-020-72427-3.

Diniz-Filho, J.A.F., & Bini, L.M., 2005. Modelling geographical patterns in species richness using eigenvector-based spatial filters. Glob. Ecol. Biogeogr. 14(2), 177-185. http://dx.doi.org/10.1111/j.1466-822X.2005.00147.x.

Diniz-Filho, J.A.F., Bini, L.M., Rangel, T.F., Loyola, R.D., Hof, C., Nogués-Bravo, D., & Araújo, M.B., 2009. Partitioning and mapping uncertainties in ensembles of forecasts of species turnover under climate change. Ecography 32, 897-906. http://dx.doi.org/10.1111/j.1600-0587.2009.06196.x.

Domisch, S., Jähnig, S.C., Simaika, J.P., Kuemmerlen, M., & Stoll, S., 2015. Application of species distribution models in stream ecosystems: the challenges of spatial and temporal scale, environmental predictors and species occurrence data. Arch. Hydrobiol. 186(1-2), 45-61. http://dx.doi.org/10.1127/fal/2015/0627.

Dong, X., Li, B., He, F., Gu, Y., Sun, M., Zhang, H., Tan, L., Xiao, W., Liu, S., & Cai, Q., 2016. Flow directionality, mountain barriers and functional traits determine diatom metacommunity structuring of high mountain streams. Sci. Rep. 6(1), 24711. PMid:27090223. http://dx.doi.org/10.1038/srep24711.

Dray, S., Bauman, D., Blanchet, G., Borcard, D., Clappe, S., Guenard, G., Jombart, T., Larocque, G., Legendre, P., Madi, N., & Wagner, H.H., 2019. Adespatial: multivariate multiscale spatial analysis [online]. Retrieved in 2023, June 20, from https://cran.r-project.org/package=adespatial

Engler, R., Hordijk, W., & Guisan, A., 2012. The MIGCLIM R package - seamless integration of dispersal constraints into projections of species distribution models. Ecography 35(10), 872-878. http://dx.doi.org/10.1111/j.1600-0587.2012.07608.x.

Ferreira, R.B., Parreira, M.R., & Nabout, J.C., 2021. The impact of global climate change on the number and replacement of provisioning ecosystem services of Brazilian Cerrado plants. Environ. Monit. Assess. 193(11), 731. PMid:34664119. http://dx.doi.org/10.1007/s10661-021-09529-6.

Froese, R., & Pauly, D., 2019. FishBase [online]. Retrieved in 2023, June 20, from https://www.fishbase.org

Gherghel, I., Brischoux, F., & Papeş, M., 2018. Using biotic interactions in broad-scale estimates of species’ distributions. J. Biogeogr. 45(9), 2216-2225. http://dx.doi.org/10.1111/jbi.13361.

Griffith, D.A., & Peres-Neto, P.R., 2006. Spatial modeling in ecology: the flexibility of eigenfunction spatial analyses. Ecology 87(10), 2603-2613. PMid:17089668. http://dx.doi.org/10.1890/0012-9658(2006)87[2603:SMIETF]2.0.CO;2.

Guisan, A., & Zimmermann, N.E., 2000. Predictive habitat distribution models in ecology. Ecol. Modell. 135(2-3), 147-186. http://dx.doi.org/10.1016/S0304-3800(00)00354-9.

Guisan, A., Tingley, R., Baumgartner, J.B., Naujokaitis-Lewis, I., Sutcliffe, P.R., Tulloch, A.I.T., Regan, T.J., Brotons, L., McDonald-Madden, E., Mantyka-Pringle, C., Martin, T.G., Rhodes, J.R., Maggini, R., Setterfield, S.A., Elith, J., Schwartz, M.W., Wintle, B.A., Broennimann, O., Austin, M., Ferrier, S., Kearney, M.R., Possingham, H.P., & Buckley, Y.M., 2013. Predicting species distributions for conservation decisions. Ecol. Lett. 16(12), 1424-1435. PMid:24134332. http://dx.doi.org/10.1111/ele.12189.

Heino, J., Melo, A.S., Siqueira, T., Soininen, J., Valanko, S., & Bini, L.M., 2015. Metacommunity organisation, spatial extent and dispersal in aquatic systems: patterns, processes and prospects. Freshw. Biol. 60(5), 845-869. http://dx.doi.org/10.1111/fwb.12533.

Hijmans, R.J., 2019. Raster: geographic data analysis and modeling [online]. Retrieved in 2023, June 20, from https://cran.r-project.org/package=raster

Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G., & Jarvis, A., 2005. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25(15), 1965-1978. http://dx.doi.org/10.1002/joc.1276.

Hijmans, R.J., Phillips, S.J., Leathwick, J.R., & Hortal, J., 2016. Dismo: species distribution modeling. R package version 1.1-1 [online]. Retrieved in 2023, June 20, from https://cran.r-project.org/package=dismo

Holloway, P., Miller, J.A., & Gillings, S., 2016. Incorporating movement in species distribution models: how do simulations of dispersal affect the accuracy and uncertainty of projections? Int. J. Geogr. Inf. Sci. 30, 1-25. http://dx.doi.org/10.1080/13658816.2016.1158823.

Jackson, D.A., 1993. Stopping rules in principal components analysis: a comparison of heuristical and statistical approaches. Ecology 74(8), 2204-2214. http://dx.doi.org/10.2307/1939574.

Jiménez-Valverde, A., Peterson, A.T., Soberón, J., Overton, J.M., Aragón, P., & Lobo, J.M., 2011. Use of niche models in invasive species risk assessments. Biol. Invasions 13(12), 2785-2797. http://dx.doi.org/10.1007/s10530-011-9963-4.

Mendes, P., Velazco, S.J.E., Andrade, A.F.A., & De Marco, P., 2020. Dealing with overprediction in species distribution models: how adding distance constraints can improve model accuracy. Ecol. Modell. 431, 109180. http://dx.doi.org/10.1016/j.ecolmodel.2020.109180.

Miller, J.A., & Holloway, P., 2015. Incorporating movement in species distribution models. Prog. Phys. Geogr. 39(6), 837-849. http://dx.doi.org/10.1177/0309133315580890.

Monsimet, J., Devineau, O., Pétillon, J., & Lafage, D., 2020. Explicit integration of dispersal-related metrics improves predictions of SDM in predatory arthropods. Sci. Rep. 10(1), 16668. PMid:33028838. http://dx.doi.org/10.1038/s41598-020-73262-2.

Mozzaquattro, L.B., Dala-Corte, R.B., Becker, F.G., & Melo, A.S., 2020. Effects of spatial distance, physical barriers, and habitat on a stream fish metacommunity. Hydrobiologia 847(14), 3039-3054. http://dx.doi.org/10.1007/s10750-020-04309-8.

Nabout, J.C., Oliveira, G., Magalhães, M.R., Carina, T.L., & Almeida, F.A.S., 2011. Global climate change and the production of pequi fruits (Caryocar brasiliense) in the Brazilian Cerrado. Nat. Conserv. 9(1), 55-60. http://dx.doi.org/10.4322/natcon.2011.006.

Nelder, J.A., & Wedderburn, R.W.M., 1972. Generalized linear models. J. R. Stat. Soc. Ser. A Stat. Soc. 135(3), 370-384. http://dx.doi.org/10.2307/2344614.

Nix, H.A., 1986. A biogeographic analysis of Australian elapid snakes. In: Longmore, R., ed. Atlas of elapid snakes of Australia. Canberra: Australian Government Publishing Service, 4-15.

Oksanen, J., F. Blanchet, M. Friendly, R. Kindt, & P. Legendre, 2017. Vegan: community ecology package. R package version 2.4-3 [online]. Retrieved in 2023, June 20, from https://cran.r-project.org/web/packages/vegan/index.html

Parreira, M.R., Nabout, J.C., Tessarolo, G., Lima-Ribeiro, M.S., & Teresa, F.B., 2019. Disentangling uncertainties from niche modeling in freshwater ecosystems. Ecol. Modell. 391, 1-8. http://dx.doi.org/10.1016/j.ecolmodel.2018.10.024.

Pavlacky Junior, D.C., Possingham, H.P., Lowe, A.J., Prentis, P.J., Green, D.J., & Goldizen, A.W., 2012. Anthropogenic landscape change promotes asymmetric dispersal and limits regional patch occupancy in a spatially structured bird population. J. Anim. Ecol. 81(5), 940-952. PMid:22489927. http://dx.doi.org/10.1111/j.1365-2656.2012.01975.x.

Perrin, S.W., Englund, G., Blumentrath, S., O’Hara, R.B., Amundsen, P.-A., & Finstad, A.G., 2020. Integrating dispersal along freshwater ecosystems into species distribution models. Divers. Distrib. 26(11), 1598-1611. http://dx.doi.org/10.1111/ddi.13112.

Peterson, A.T., & Soberón, J., 2012. Species Distribution Modeling and Ecological Niche Modeling: getting the concepts right. Nat. Conserv. 10(2), 102-107. http://dx.doi.org/10.4322/natcon.2012.019.

Peterson, A.T., 2006. Uses and requirements of Ecological Niche Models and Related Distributional Models. Biodivers. Inform. 3, 59-72. http://dx.doi.org/10.17161/bi.v3i0.29.

Peterson, A.T., Martínez-Campos, C., Nakazawa, Y., & Martínez-Meyer, E., 2005. Time-specific Ecological Niche Modeling predicts spatial dynamics of vector insects and human dengue cases. Trans. R. Soc. Trop. Med. Hyg. 99(9), 647-655. PMid:15979656. http://dx.doi.org/10.1016/j.trstmh.2005.02.004.

Peterson, A.T., Sánchez-Cordero, V., Martínez-Meyer, E., & Navarro-Sigüenza, A.G., 2006. Tracking population extirpations via melding ecological niche modeling with land-cover information. Ecol. Modell. 195(3-4), 229-236. http://dx.doi.org/10.1016/j.ecolmodel.2005.11.020.

Peterson, A.T., Soberón, J., Pearson, R.G., Anderson, R.P., Martínez-Meyer, E., Nakamura, M., & Araújo, M.B., 2011. Ecological niches and geographic distributions. Princenton: Princenton University Press. http://dx.doi.org/10.23943/princeton/9780691136868.001.0001.

Phillips, S.J., Anderson, R.P., & Schapire, R.E., 2006. Maximum entropy modeling of species geographic distributions. Ecol. Modell. 190(3-4), 231-259. http://dx.doi.org/10.1016/j.ecolmodel.2005.03.026.

Pringle, J.M., Blakeslee, A.M.H., Byers, J.E., & Roman, J., 2011. Asymmetric dispersal allows an upstream region to control population structure throughout a species’ range. Proc. Natl. Acad. Sci. USA 108(37), 15288-15293. PMid:21876126. http://dx.doi.org/10.1073/pnas.1100473108.

R Core Team, 2019. R: a language and environment for statistical computing [online]. Retrieved in 2023, June 20, from https://www.r-project.org/

Rangel, T.F., & Loyola, R.D., 2012. Labeling Ecological Niche Models. Nat. Conserv. 10(2), 119-126. http://dx.doi.org/10.4322/natcon.2012.030.

Reis, R.E., Kullander, S.O., & Ferraris, C.J., 2003. Check list of the freshwater fishes of South and Central America. Porto Alegre: EDIPUCRS.

Revelle, W., 2019. Psych: procedures for psychological, psychometric, and personality research [online]. Retrieved in 2023, June 20, from https://cran.r-project.org/package=psych

Rieux, A., Soubeyrand, S., Bonnot, F., Klein, E.K., Ngando, J.E., Mehl, A., Ravigne, V., Carlier, J., & Bellaire, L.L., 2014. Long-distance wind-dispersal of spores in a fungal plant pathogen: estimation of anisotropic dispersal kernels from an extensive field experiment. PLoS One 9(8), e103225. PMid:25116080. http://dx.doi.org/10.1371/journal.pone.0103225.

Riginos, C., Hock, K., Matias, A.M., Mumby, P.J., van Oppen, M.J.H., & Lukoschek, V., 2019. Asymmetric dispersal is a critical element of concordance between biophysical dispersal models and spatial genetic structure in Great Barrier Reef corals. Divers. Distrib. 25(11), 1684-1696. http://dx.doi.org/10.1111/ddi.12969.

Rocha, B.S., Souza, C.A., Machado, K.B., Vieira, L.C.G., & Nabout, J.C., 2020. The relative influence of the environment, land use, and space on the functional and taxonomic structures of phytoplankton and zooplankton metacommunities in tropical reservoirs. Freshwat. Sci. 39(2), 321-333. http://dx.doi.org/10.1086/708949.

Ruaro, R., Conceição, E.O., Silva, J.C., Cafofo, E.G., Angulo-Valencia, M.A., Mantovano, T., Pineda, A., Paula, A.C.M., Zanco, B.F., Capparros, E.M., Moresco, G.A., Oliveira, I.J., Antiqueira, J.L., Ernandes-Silva, J., Silva, J.V.F., Adelino, J.R.P., Santos, J.A., Ganassin, M.J.M., Iquematsu, M.S., Landgraf, G.O., Lemes, P., Cassemiro, F.A.S., Batista-Silva, V.F., Diniz-Filho, J.A.F., Rangel, T.F., Agostinho, A.A., & Bailly, D., 2019. Climate change will decrease the range of a keystone fish species in La Plata River Basin, South America. Hydrobiologia 836(1), 1-19. http://dx.doi.org/10.1007/s10750-019-3904-0.

Schölkopf, B., Platt, J.C., Shawe-Taylor, J., Smola, A.J., & Williamson, R.C., 2001. Estimating the support of a high-dimensional distribution. Neural Comput. 13(7), 1443-1471. PMid:11440593. http://dx.doi.org/10.1162/089976601750264965.

Soberón, J., 2007. Grinnellian and Eltonian niches and geographic distributions of species. Ecol. Lett. 10(12), 1115-1123. PMid:17850335. http://dx.doi.org/10.1111/j.1461-0248.2007.01107.x.

Swets, J.A., 1988. Measuring the accuracy of diagnostic systems. Science 240(4857), 1285-1293. PMid:3287615. http://dx.doi.org/10.1126/science.3287615.

Tamme, R., Götzenberger, L., Zobel, M., Bullock, J.M., Hooftman, D.A., Kaasik, A., & Pärtel, M., 2014. Predicting species’ maximum dispersal distances from simple plant traits. Ecology 95(2), 505-513. PMid:24669743. http://dx.doi.org/10.1890/13-1000.1.

Uribe-Rivera, D.E., Soto-Azat, C., Valenzuela-Sánchez, A., Bizama, G., Simonetti, J.A., & Pliscoff, P., 2017. Dispersal and extrapolation on the accuracy of temporal predictions from distribution models for the Darwin’s frog. Ecol. Appl. 27(5), 1633-1645. PMid:28397328. http://dx.doi.org/10.1002/eap.1556.

Vasudev, D., Fletcher Junior, R.J., Goswami, V.R., & Krishnadas, M., 2015. From dispersal constraints to landscape connectivity: lessons from species distribution modeling. Ecography 38(10), 967-978. http://dx.doi.org/10.1111/ecog.01306.

Ver Hoef, J.M., Peterson, E., & Theobald, D., 2006. Spatial statistical models that use flow and stream distance. Environ. Ecol. Stat. 13(4), 449-464. http://dx.doi.org/10.1007/s10651-006-0022-8.
 


Submitted date:
04/03/2023

Accepted date:
06/20/2023

Publication date:
07/25/2023

64c0176ea953956f3f5265d3 alb Articles
Links & Downloads

Acta Limnol. Bras. (Online)

Share this page
Page Sections