Abstract
Resistance models may quantify the ability of the landscape to impede species’ movement and represent suitable habitats. Moreover, the performance of resistance models parameterized by land-use/land cover attributes evidence that the integrity of the environments subject to urban sprawl is poorly understood. In this sense, the study assumed we could identify the forest functional connectivity in a landscape considering the disparity in the landscape mosaic. In this context, we sought to develop a landscape resistance index through structural equation modeling (SEM), supported by the criteria of heat emission, biomass, and anthropogenic barriers, obtained by remote sensing, called observed variables. The landscape studied in the Green Belt Biosphere Reserve of São Paulo has significant remnants of the Atlantic Forest, a biodiversity hotspot. However, our results indicated criteria variability in the landscape modeled through the SEM, obtaining a significant adjustment of the landscape resistance index, with comparative fit index (CFI) of 1.00 and root mean square error of approximation (RMSEA) of 0.00. The index reflects the resistance levels of the land use/land cover, expressed by the class interval, ranging from 0% (1.73) to 100% (493.88), with the highest values associated with the anthropized uses and forest isolation. Thus, our index based on environmental attributes reflects the structure of functional forest connectivity and offers a framework to design forest corridors across landscapes.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
Data and materials may be made available.
References
Abrahms, B., Sawyer, S. C., Jordan, N. R., McNutt, J. W., Wilson, A. M., & Brashares, J. S. (2017). Does wildlife resource selection accurately inform corridor conservation? Journal of Applied Ecology, 54(2), 412–422. https://doi.org/10.1111/1365-2664.12714
Alagador, D., Triviño, M., Cerdeira, J. O., Brás, R., Cabeza, M., & Miguel, B. A. (2012). Linking like with like: Optimising connectivity between environmentally-similar habitats. Landscape Ecology, 27, 291–301. https://doi.org/10.1007/s10980-012-9704-9
Almeida, M., Almeida, A., & Almeida, M. (2010). Reeducação psiconeurológica. Editora Biblioteca 24 horas.
Alston, J. M., Joyce, J. M., Merkle, J. A., & Moen, R. A. (2020). Temperature shapes movement and habitat selection by a heat-sensitive ungulate. Landscape Ecology, 35, 1961–1973. https://doi.org/10.1007/s10980-020-01072-y
Arroyo-Rodríguez, V., Melo, F., Martinez-Ramos, M., Bongers, F., Chazdon, J. A. M., Norden, N., Santos, B. A., Leal, I. R., & Tabarelli, M. (2017). Multiple successional pathways in human-modified tropical landscapes: New insights from forest succession, forest fragmentation and landscape ecology research. Biological Reviews, 92(1), 326–340. https://doi.org/10.1111/brv.12231
Baguette, M., Blanchet, S., Legrand, D., Stevens, V. M., & Turlure, C. (2013). Individual dispersal, landscape connectivity and ecological networks. Biological Reviews, 88(2), 310–326. https://doi.org/10.1111/brv.12000
Bailey, J., Wallis, J., & Codling, E. (2018). Navigational efficiency in a biased and correlated random walk model of individual animal movement. Ecology, 99(1), 217–223 https://www.jstor.org/stable/26624027
Bajaru, S., Pal, S., Prabhu, M., Patel, P., Khot, R., & Apte, D. (2020). A multi-species occupancy modeling approach to access the impacts of land use and land cover on terrestrial vertebrates in the Mumbai Metropolitan Region (MMR), Western Ghats, India. PLoS One, 15(10), e0240989. https://doi.org/10.1371/journal.pone.0240989
Barsi, J., Barker, J. L., & Schott, J. R. (2003). An atmospheric correction parameter calculator for a single thermal band earth-sensing instrument. IEEE International Geoscience and Remote Sensing Symposium, 5, 3014–3016. https://doi.org/10.1109/IGARSS.2003.1294665
Belote, R. T., Dietz, M. S., McRae, B. H., Theobald, D. M., McClure, M. L., Irwin, G. H., McKinley, P. S., Gage, J. A., & Aplet, G. H. (2016). Identifying corridors among large protected areas in the United States. PLoS One, 11(4), e0154223. https://doi.org/10.1371/journal.pone.0154223
Benza, M., Weeks, J. R., Stow, D. A., López-Carr, D., & Clarke, K. C. (2016). A pattern-based definition of urban context using remote sensing and GIS. Remote Sensing of Environment, 183, 250–264. https://doi.org/10.1016/j.rse.2016.06.011
Berger-Tal, O., & Saltz, D. (2019). Invisible barriers: anthropogenic impacts on inter- and intra-specific interactions as drivers of landscape-independent fragmentation. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 374, 20180049. https://doi.org/10.1098/rstb.2018.0049
Bonthoux, S., Lefèvre, S., Herrault, P., & Sheeren, D. (2018). Spatial and temporal dependency of NDVI satellite imagery in predicting bird diversity over France. Remote Sensing, 10(7), 1136. https://doi.org/10.3390/rs10071136
Breckheimer, I., Haddad, N. M., Morris, W. F., Trainor, A. M., Fields, W. R., Jobe, R. T., Hudgens, B. R., Moody, A., & Walters, J. R. (2014). Defining and evaluating the umbrella species concept for conserving and restoring landscape connectivity. Conservation Biology, 28(6), 1584–1593. https://doi.org/10.1111/cobi.12362
Broders, H. G., Coombs, A. B., & McCarron, J. R. (2012). Ecothermic responses of moose (Alces alces) to thermoregulatory stress on mainland Nova Scotia. Alces: A Journal Devoted to the Biology and Management of Moose, 48, 53–61 Retrieved March 2, 2022, from https://alcesjournal.org/index.php/alces/article/view/102
Brose, U., Dunne, J. A., Montoya, J. M., Petchey, O. L., Schneider, F. D., & Jacob, U. (2012). Climate change in size-structured ecosystems. Philosophical Transactions of the Royal Society B, 367, 2903–2912. https://doi.org/10.1098/rstb.2012.0232
Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M., West, G., & B. (2004). Toward a metabolic theory of ecology. Ecology Society of America, 85(7), 1771–1789. https://doi.org/10.1890/03-9000
Brown, L. M., Fuda, R. K., Schtickzelle, N., Coffman, H., Jost, A., Kazberouk, E. K., Sass, E., & Crone, E. E. (2017). Using animal movement behavior to categorize land cover and predict consequences for connectivity and patch residence times. Landscape Ecology, 32, 1657–1670. https://doi.org/10.1007/s10980-017-0533-8
Butsch, C., & Heinkel, S. (2020). Periurban transformations in the global south and their impact on water-based livelihoods. Water, 12(2), 458. https://doi.org/10.3390/w12020458
Byrne, B. M. (2013). Structural equation modeling with AMOS: Basic concepts, applications, and programming. Routledge.
Ceia-Hasse, A., Borda-de-água, L., Grilo, C., & Pereira, H. M. (2017). Global exposure of carnivores to roads. Global Ecology and Biogeography, 26(5), 592–600. https://doi.org/10.1111/geb.12564
Chapron, G., Kaczensky, P., JDC. Linnell, Huber, D., Andrén, H., López-Bao, J. V., Adamec, M., Álvares, F., Anders, O., Balčiauskas, L., Balys, V., Bedő, P., Bego, F., Blanco, J. C., Breitenmoser, U., Brøseth, H., Bufka, L., Bunikyte, R., Ciucci, P., . . . Boitani, L. (2014). Recovery of large carnivores in Europe’s modern human-dominated landscapes. Science. https://doi.org/10.1126/science.1257553
Chazdon, R. L., & Uriarte, M. (2016). Natural regeneration in the context of large-scale forest and landscape restoration in the tropics. Biotropica, 48(6), 709–715. https://doi.org/10.1111/btp.12409
Chen, Y., Li, X., Liu, X., Ai, B., & Li, S. (2016). Capturing the varying effects of driving forces over time for the simulation of urban growth by using survival analysis and cellular automata. Landscape and Urban Planning, 152, 59–71. https://doi.org/10.1016/j.landurbplan.2016.03.011
Choudhury, D., Das, K., & Das, A. (2019). Assessment of land use land cover changes and its impact on variations of land surface temperature in Asansol-Durgapur Development Region. The Egyptian Journal of Remote Sensing and Space Science, 22(2), 203–218. https://doi.org/10.1016/j.ejrs.2018.05.004
Churko, G., Kienast, F., & Bolliger, J. (2020). A multispecies assessment to identify the functional connectivity of amphibians in a human-dominated landscape. ISPRS International Journal of Geo-Information, 9(5), 287. https://doi.org/10.3390/ijgi9050287
Clarke, A., Taylor, K. I., Devereux, B., Randall, B., & Tyler, L. K. (2013). From perception to conception: How meaningful objects are processed over time. Cerebral Cortex, 23(1), 187–197. https://doi.org/10.1093/cercor/bhs002
Cushman, S. A., & Landguth, E. L. (2010). Scale dependent inference in landscape genetics. Landscape Ecology, 25, 967–979. https://doi.org/10.1007/s10980-010-9467-0
Cushman, S. A., Landguth, E. L., & Flather, C. H. (2013). Evaluating population connectivity for species of conservation concern in the American Great Plains. Biodiversity and Conservation, 22, 2583–2605. https://doi.org/10.1007/s10531-013-0541-1
Cushman, S. A., Lewis, J. S., & Landguth, E. L. (2014). Why did the bear cross the road? Comparing the performance of multiple resistance surfaces and connectivity modeling methods. Diversity, 6(4), 844–854. https://doi.org/10.3390/d6040844
Cushman, S. A., Wasserman, T. N., Landguth, E. L., & Shirk, A. J. (2013). Re-evaluating causal modeling with mantel tests in landscape genetics. Diversity, 5(1), 51–72. https://doi.org/10.3390/d5010051
Da Silveira, N. S., Niebuhr, B. B. S., Muylaert, R. D. L., Ribeiro, M. C., & Pizo, M. A. (2016). Effects of land cover on the movement of frugivorous birds in a heterogeneous landscape. PLoS One, 11(6), e0156688. https://doi.org/10.1371/journal.pone.0156688
Dallabrida, J. P., Silva, A. C., Higuchi, P., Larsen, J. G., Santos, G. N., Lima, C. L., Rodrigues Júnior, L. C., Machado, F. D., Cruz, A. P., & Nunes, A. da S. (2019). Expansão da vegetação arbustivo-arbórea em áreas abertas alto-montanas adjacentes a fragmentos florestais, no Planalto Sul Catarinense. Ciência Florestal, 29(1), 130–143. https://doi.org/10.5902/1980509825998
Davies, T. W., & Smyth, T. (2018). Why artificial light at night should be a focus for global change research in the 21st century. Global Change Biology, 24(3), 872–882. https://doi.org/10.1111/gcb.13927
De Castro, A. I., Six, J., Plant, R. E., & Peña, J. M. (2018). Mapping crop calendar events and phenology-related metrics at the parcel level by object-based image analysis (OBIA) of MODIS-NDVI time-series: A case study in central California. Remote Sensing, 10(11), 1745. https://doi.org/10.3390/rs10111745
Dell, A. I., Pawar, S., & Savage, V. M. (2014). Temperature dependence of trophic interactions are driven by asymmetry of species responses and foraging strategy. Journal of Animal Ecology, 83(1), 70–84. https://doi.org/10.1111/1365-2656.12081
DeLong, J. P. (2012). Experimental demonstration of a ‘rate-size’ trade-off governing body size optimization. Evolutionary Ecology Research, 14, 343–352.
Deng, Y., Wang, S., Bai, X., Tian, Y., Wu, L., Xiao, J., Chen, F., & Qian, Q. (2018). Relationship among land surface temperature and LUCC, NDVI in typical karst area. Scientific Reports, 8(1), 1–12. https://doi.org/10.1038/s41598-017-19088-x
Dong, J., Xiao, X., Menarguez, M. A., Zhang, G., Qin, Y., Thau, D., Biradar, C., & Moore, B. (2016). Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine. Remote Sensing of Environment, 185, 142–154. https://doi.org/10.1016/j.rse.2016.02.016
Du, H., Wang, D., Wang, Y., Zhao, X., Qin, F., Jiang, H., & Cai, Y. (2016). Influences of land cover types, meteorological conditions, anthropogenic heat and urban area on surface urban heat island in the Yangtze River Delta Urban Agglomeration. Science of The Total Environment, 571, 461–470. https://doi.org/10.1016/j.scitotenv.2016.07.012
Ducci, L., Agnelli, P., Di Febbraro, M., Frate, L., Russo, D., Loy, A., Carranza, M. L., Santini, G., & Roscioni, F. (2015). Different bat guilds perceive their habitat in different ways: A multiscale landscape approach for variable selection in species distribution modelling. Landscape Ecology, 30, 2147–2159. https://doi.org/10.1007/s10980-015-0237-x
Estel, S., Kuemmerle, T., Alcántara, C., Levers, C., Prishchepov, A., & Hostert, P. (2015). Mapping farmland abandonment and recultivation across Europe using MODIS NDVI time series. Remote Sensing of Environment, 163, 312–325. https://doi.org/10.1016/j.rse.2015.03.028
Feng, Y., Luo, G., Lu, L., Zhou, D., Han, Q., Xu, W., Yin, C., Zhu, L., Dai, L., Li, Y., & Li, C. (2011). Effects of land use change on landscape pattern of the Manas River watershed in Xinjiang, China. Environmental Earth Sciences, 64, 2067–2077. https://doi.org/10.1007/s12665-011-1029-5
Froidevaux, J. S., Zellweger, F., Bollmann, K., Jones, G., & Obrist, M. K. (2016). From field surveys to LiDAR: Shining a light on how bats respond to forest structure. Remote Sensing of Environment, 175, 242–250. https://doi.org/10.1016/j.rse.2015.12.038
Gama-Rodrigues, A. C., Sales, M. V. S., Silva, P. S. D., Comerford, N. B., Cropper, W. P., & Gama-Rodrigues, E. F. (2014). An exploratory analysis of phosphorus transformations in tropical soils using structural equation modeling. Biogeochemistry, 118, 453–469. https://doi.org/10.1007/s10533-013-9946-x
Gaston, K. J., & Holt, L. A. (2018). Nature, extent and ecological implications of night-time light from road vehicles. Journal of Applied Ecology, 55(5), 2296–2307. https://doi.org/10.1111/1365-2664.13157
Gaur, A., Eichenbaum, M. K., & Simonovic, S. P. (2018). Analysis and modelling of surface Urban Heat Island in 20 Canadian cities under climate and land-cover change. Journal of Environmental Management, 206, 145–157. https://doi.org/10.1016/j.jenvman.2017.10.002
Gibert, J. P., Chelini, C., Rosenthal, M. F., & DeLong, J. P. (2016). Crossing regimes of temperature dependence in animal movement. Global Change Biology, 22(5), 1722–1736. https://doi.org/10.1111/gcb.13245
Gitelson, A. A., Peng, Y., & Huemmrich, K. F. (2014). Relationship between fraction of radiation absorbed by photosynthesizing maize and soybean canopies and NDVI from remotely sensed data taken at close range and from MODIS 250 m resolution data. Remote Sensing of Environment, 147, 108–120. https://doi.org/10.1016/j.rse.2014.02.014
Gómez Giménez, M., de Jong, R., Della Peruta, R., Keller, A., & Schaepman, M. E. (2017). Determination of grassland use intensity based on multi-temporal remote sensing data and ecological indicators. Remote Sensing of Environment, 198, 126–139. https://doi.org/10.1016/j.rse.2017.06.003
Goodwin, B. J. (2003). Is landscape connectivity a dependent or independent variable? Landscape Ecology, 18, 687–699. https://doi.org/10.1023/B:LAND.0000004184.03500.a8
Gracanin, A., & Mikac, K. M. (2023). Evaluating modelled wildlife corridors for the movement of multiple arboreal species in a fragmented landscape. Landscape Ecology, 38, 1321–1337. https://doi.org/10.1007/s10980-023-01628-8
Grace, J. B., Anderson, T. M., Olff, H., & Scheiner, S. M. (2010). On the specification of structural equation models for ecological systems. Ecological Monographs, 80(1), 67–87. https://doi.org/10.1890/09-0464.1
Grossmann, K., Frankenberg, C., Magney, T. S., Hurlock, S. C., Seibt, U., & Stutz, J. (2018). PhotoSpec: A new instrument to measure spatially distributed red and far-red Solar-Induced Chlorophyll Fluorescence. Remote Sensing of Environment, 216, 311–327. https://doi.org/10.1016/j.rse.2018.07.002
Gruber, B., & Adamack, A. T. (2015). Landgenreport: A new r function to simplify landscape genetic analysis using resistance surface layers. Molecular Ecology Resources, 15(5), 1172–1178. https://doi.org/10.1111/1755-0998.12381
Guetté, A., Godet, L., Juigner, M., & Robin, M. (2018). Worldwide increase in Artificial Light At Night around protected areas and within biodiversity hotspots. Biological Conservation, 223, 97–103. https://doi.org/10.1016/j.biocon.2018.04.018
Guha, S., Govil, H., Dey, A., & Gill, N. (2018). Analytical study of land surface temperature with NDVI and NDBI using Landsat 8 OLI and TIRS data in Florence and Naples city, Italy. European Journal of Remote Sensing, 51(1), 667–678. https://doi.org/10.1080/22797254.2018.1474494
Hair, J. F., Black, W. C., Babin, B. J., Anderson, E., & Tathan, R. L. (2009). Análise Multivariada de Dados. Bookman.
Hanks, E. M., Hooten, M. B., & Alldredge, M. W. (2015). Continuous-time discrete-space models for animal movement. Annals of Applied Statistics, 9(1), 145–165. https://doi.org/10.1214/14-AOAS803
Hoagland, S. J., Beier, P., & Lee, D. (2018). Using MODIS NDVI phenoclasses and phenoclusters to characterize wildlife habitat: Mexican spotted owl as a case study. Forest Ecology and Management, 412, 80–93. https://doi.org/10.1016/j.foreco.2017.12.017
Hooten, M. B., Johnson, D. S., Hanks, E. M., & Lowry, J. H. (2010). Agent-based inference for animal movement and selection. Journal of Agricultural, Biological and Environmental Statistics, 15, 523–538.
Huang, Q., Yang, X., Gao, B., Yang, Y., & Zhao, Y. (2014). Application of DMSP/OLS nighttime light images: A meta-analysis and a systematic literature review. Remote Sensing, 6(8), 6844–6866. https://doi.org/10.3390/rs6086844
IBGE – Instituto Brasileiro de Geografia e Estatística. (2021a, March 2). Mapa de Vegetação do Brasil. Retrieved March 2, 2022, from https://geoftp.ibge.gov.br/informacoes_ambientais/vegetacao/mapas/brasil/vegetacao.pdf.
IBGE – Instituto Brasileiro de Geografia e Estatística. São Paulo/SP. (2021b, March 2). Retrieved March 2, 2022, from https://cidades.ibge.gov.br/brasil/sp/sao-paulo/panorama.
Ito, T. Y., Sakamoto, Y., Lhagvasuren, B., Kinugasa, T., & Shinoda, M. (2018). Winter habitat of Mongolian gazelles in areas of southern Mongolia under new railroad construction: An estimation of interannual changes in suitable habitats. Mammalian Biology, 93, 13–20. https://doi.org/10.1016/j.mambio.2018.07.006
Jiao, M., Hu, M., & Xia, B. (2019). Spatiotemporal dynamic simulation of land-use and landscape-pattern in the Pearl River Delta, China. Sustainable Cities and Society, 49, 101581. https://doi.org/10.1016/j.scs.2019.101581
Johnson, H. E., Gustine, D. D., Golden, T. S., Adams, L. G., Parrett, L. S., Lenart, E. A., & Barboza, P. S. (2018). NDVI exhibits mixed success in predicting spatiotemporal variation in caribou summer forage quality and quantity. Ecosphere, 9(10), e02461. https://doi.org/10.1002/ecs2.2461
Kaplan, D. (2009). Structural equation modeling: Foundations and extensions. SAGE Publications, Inc. https://doi.org/10.4135/9781452226576
Keenleyside, K. A., Dudley, N., Cairns, S., Corredor, C. M., & Stolton, S.(2012). Ecological restoration for protected areas : principles, guidelines and best practices. International Union for Conservation of Nature. Retrieved March, 2, 2022, from https://portals.iucn.org/library/efiles/documents/PAG-018.pdf
König, H. J., Kiffner, C., Kramer-Schadt, S., Fürst, C., Keuling, O., & Ford, A. T. (2020). Human–wildlife coexistence in a changing world. Conservation Biology, 34(4), 786–794. https://doi.org/10.1111/cobi.13513
Krishnamurthy, R., Cushman, S. A., Sarkar, M. S., Malviya, M., Naveen, M., Johnson, J. A., & Sen, S. (2016). Multi-scale prediction of landscape resistance for tiger dispersal in central India. Landscape Ecology, 31, 1355–1368. https://doi.org/10.1007/s10980-016-0363-0
Krosby, M., Breckheimer, I., John Pierce, D., Singleton, P. H., Hall, S. A., Halupka, K. C., Gaines, W. L., Long, R. A., McRae, B. H., Cosentino, B. L., & Schuett-Homes, J. O. (2015). Focal species and landscape “naturalness” corridor models offer complementary approaches for connectivity conservation planning. Landscape Ecology, 30, 2121–2132. https://doi.org/10.1007/s10980-015-0235-z
Kurz, D. J., Nowakowski, A. J., Tingley, M. W., Donnelly, M. A., & Wilcove, D. S. (2014). Forest-land use complementarity modifies community structure of a tropical herpetofauna. Biological Conservation, 170, 246–255. https://doi.org/10.1016/j.biocon.2013.12.027
Levin, N., & Zhang, Q. (2017). A global analysis of factors controlling VIIRS nighttime light levels from densely populated areas. Remote Sensing of Environment, 190, 366–382. https://doi.org/10.1016/j.rse.2017.01.006
Li, J., Rodrigues, D., & Tang, X. (2017). Effects of land lease policy on changes in land use, mechanization and agricultural pollution. Land Use Policy, 64, 405–413. https://doi.org/10.1016/j.landusepol.2017.03.008
Lin, B. B., Egerer, M. H., Liere, H., Jha, S., Bichier, P., & Philpott, S. M. (2018). Local- and landscape-scale land cover affects microclimate and water use in urban gardens. Science of The Total Environment, 610-611, 570–575. https://doi.org/10.1016/j.scitotenv.2017.08.091
Lindenmayer, D. B., Blanchard, W., Foster, C. N., Scheele, B. C., Westgate, M. J., Stein, J., Crane, M., & Florance, D. (2020). Habitat amount versus connectivity: An empirical study of bird responses. Biological Conservation, 241, 108377. https://doi.org/10.1016/j.biocon.2019.108377
Liu, C., Newell, G., White, M., & Bennett, A. F. (2018). Identifying wildlife corridors for the restoration of regional habitat connectivity: A multispecies approach and comparison of resistance surfaces. PLoS One, 13(11), e0206071. https://doi.org/10.1371/journal.pone.0206071
Loveridge, A. J., Valeix, M., Elliot, N. B., & Macdonald, D. W. (2017). The landscape of anthropogenic mortality: How African lions respond to spatial variation in risk. Journal of Applied Ecology, 54(3), 815–825. https://doi.org/10.1111/1365-2664.12794
Luhring, T. M., & DeLong, J. P. (2017). Scaling from metabolism to population growth rate to understand how acclimation temperature alters thermal performance. Integrative and Comparative Biology, 57(1), 103–111. https://doi.org/10.1093/icb/icx041
Marulli, J., & Mallarach, J. M. (2005). A GIS methodology for assessing ecological connectivity: Application to the Barcelona Metropolitan Area. Landscape and Urban Planning, 71(2-4), 243–262. https://doi.org/10.1016/j.landurbplan.2004.03.007
Mateo-Sánchez, M. C., Balkenhol, N., Cushman, S., Pérez, T., Domínguez, A., & Santiago, S. (2015). A comparative framework to infer landscape effects on population genetic structure: Are habitat suitability models effective in explaining gene flow? Landscape Ecology, 30, 1405–1420b. https://doi.org/10.1007/s10980-015-0194-4
Matos, C., Petrovan, S. O., Wheeler, P. M., & Ward, A. I. (2019). Landscape connectivity and spatial prioritization in an urbanising world: A network analysis approach for a threatened amphibian. Biological Conservation, 237, 238–247. https://doi.org/10.1016/j.biocon.2019.06.035
McCann, N. P., Moen, R. A., Windels, S. K., & Harris, T. R. (2016). Bed sites as thermal refuges for a cold-adapted ungulate in summer. Wildlife Biology, 22(5), 228–237. https://doi.org/10.2981/wlb.00216
Melin, M., Matala, J., Mehtätalo, L., Pusenius, J., & Packalen, P. (2016). Ecological dimensions of airborne laser scanning — Analyzing the role of forest structure in moose habitat use within a year. Remote Sensing of Environment, 173, 238–247. https://doi.org/10.1016/j.rse.2015.07.025
Milanesi, P., Holderegger, R., Bollmann, K., Gugerli, F., & Zellweger, F. (2017). Three-dimensional habitat structure and landscape genetics: A step forward in estimating functional connectivity. Ecology, 98(2), 393–402. https://doi.org/10.1002/ecy.1645
Mills, E. C., Poulsen, J. R., Fay, J. M., Morkel, P., Clark, C. J., Meier, A., Beirne, C., & White, L. J. T. (2018). Forest elephant movement and habitat use in a tropical forest-grassland mosaic in Gabon. PLoS One, 13(7), e0199387. https://doi.org/10.1371/journal.pone.0199387
MMA - Ministério do Meio Ambiente, (2021, March 2). Áreas prioritárias para conservação da biodiversidade brasileira. Retrieved March 2, 2022, from http://areasprioritarias.mma.gov.br/1-atualizacao-das-areas-prioritarias.
Moraes, A. M., Ruiz-Miranda, C. R., Galetti, P. M., Jr., Niebuhr, B. B., Alexandre, B. R., Muylaert, R. L., Grativol, A. D., Ribeiro, J. W., Ferreira, A. N., & Ribeiro, M. C. (2018). Landscape resistance influences effective dispersal of endangered golden lion tamarins within the Atlantic Forest. Biological Conservation, 224, 178–187. https://doi.org/10.1016/j.biocon.2018.05.023
Mörtberg, U., Haas, J., Zetterberg, A., Franklin, J. P., Jonsson, D., & Deal, B. (2013). Urban ecosystems and sustainable urban development—Analysing and assessing interacting systems in the Stockholm region. Urban Ecosystems, 16, 763–782. https://doi.org/10.1007/s11252-012-0270-3
Nandy, S., Singh, R., Ghosh, S., Watham, T., Kushwaha, S. P. S., Kumar, A. S., & Dadhwal, V. K. (2017). Neural network-based modelling for forest biomass assessment. Carbon Management, 8, 305–317. https://doi.org/10.1080/17583004.2017.1357402
Nascimento-Júnior, L. (2017). Urbanização e cidade dispersa: implicações da produção do espaço urbano no Brasil, em Moçambique e na Austrália. GEOUSP Espaço E Tempo (Online), 21(2), 550–569. https://doi.org/10.11606/issn.2179-0892.geousp.2017.125392
Neumann, J. E., Price, J., Chinowsky, P., Wright, L., Ludwig, L., Streeter, R., Jones, R., Smith, J. B., Perkins, W., Jantarasami, L., & Martinich, J. (2015). Climate change risks to US infrastructure: Impacts on roads, bridges, coastal development, and urban drainage. Climatic Change, 131, 97–109. https://doi.org/10.1007/s10584-013-1037-4
Nimmo, D. G., Mac Nally, R., Cunningham, S. C., Haslem, A., & Bennett, A. F. (2015). Vive la résistance: reviving resistance for 21st century conservation. Trends in Ecology & Evolution, 30(9), 516–523. https://doi.org/10.1016/j.tree.2015.07.008
Niu, X., Tang, J., Wang, S., & Fu, C. (2019). Impact of future land use and land cover change on temperature projections over East Asia. Climate Dynamics, 52, 6475–6490. https://doi.org/10.1007/s00382-018-4525-4
Nowakowski, A. J., Watling, J. I., Whitfield, S. M., Todd, B. D., Kurz, D. J., & Donnelly, M. A. (2017). Tropical amphibians in shifting thermal landscapes under land-use and climate change. Conservation Biology, 31(1), 96–105. https://doi.org/10.1111/cobi.12769
Ojima, R. (2007). Dimensões da urbanização dispersa e proposta metodológica para estudos comparativos: uma abordagem socioespacial em aglomerações urbanas brasileiras. Revista Brasileira de Estudos de População, 24(2), 277–300. https://doi.org/10.1590/S0102-30982007000200007
Olyer-McCance, S. J., Fedy, B. C., & Landguth, E. L. (2013). Sample design effects in landscape genetics. Conservation Genetics, 14, 275–285. https://doi.org/10.1007/s10592-012-0415-1
Osmond, M. M., Barbour, M. A., Bernhardt, J. R., Pennell, M. W., Sunday, J. M., & O’Connor, M. L. (2017). Warming-induced changes to body size stabilize consumer-resource dynamics. The American Naturalist. https://doi.org/10.1086/691387
Pawar, S., Dell, A. I., Savage, V. M., & Knies, J. L. (2016). Real versus artificial variation in the thermal sensitivity of biological traits. The American Naturalist. https://doi.org/10.1086/684590
Peterman, W. E. (2018). ResistanceGA: An R package for the optimization of resistance surfaces using genetic algorithms. Methods in Ecology and Evolution, 9(6), 1638–1647. https://doi.org/10.1111/2041-210X.12984
Peterman, W. E., Connette, G. M., Semlitsch, R. D., & Eggert, L. S. (2014). Ecological resistance surfaces predict fine-scale genetic differentiation in a terrestrial woodland salamander. Molecular Ecology, 23(10), 2402–2413. https://doi.org/10.1111/mec.12747
Peterman, W. E., Winiarski, K. J., Moore, C. E., da Silva Carvalho, C., Gilbert, A. L., & Spear, S. F. (2019). A comparison of popular approaches to optimize landscape resistance surfaces. Landscape Ecology, 34, 2197–2208. https://doi.org/10.1007/s10980-019-00870-3
Pettorelli, N., Ryan, S. J., Mueller, T., Bunnefeld, N., Jedrzejewska, B., Lima, M., & Kausrud, K. (2011). The Normalized Difference Vegetation Index (NDVI): Unforeseen successes in animal ecology. Climate Reaserch, 46, 15–27. https://doi.org/10.3354/cr00936
Poniatowski, D., Löffler, F., Stuhldreher, G., Borchard, F., Krämer, B., & Fartmann, T. (2016). Functional connectivity as an indicator for patch occupancy in grassland specialists. Ecological Indicators, 67, 735–742. https://doi.org/10.1016/j.ecolind.2016.03.047
Reding, D. M., Cushman, S., Gosselink, T. E., & Clark, W. R. (2013). Linking movement behavior and fin-scale genetic structure to model landscape connectivity for bobcats (Lynx rufus). Landscape Ecology, 28, 471–486. https://doi.org/10.1007/s10980-012-9844-y
Remelgado, R., Safi, K., & Wegmann, M. (2020). From ecology to remote sensing: Using animals to map land cover. Remote Sensing in Ecology and Conservation, 6(1), 93–104. https://doi.org/10.1002/rse2.126
Remelgado, R., Wegmann, M., & Safi, K. (2019). Rsmove—An r package to bridge remote sensing and movement ecology. Methods in Ecology and Evolution, 10(8), 1212–1221. https://doi.org/10.1111/2041-210X.13199
Rickbeil, G. J., Hermosilla, T., Coops, N. C., White, J. C., & Wulder, M. A. (2017). Barren-ground caribou (Rangifer tarandus groenlandicus) behaviour after recent fire events; integrating caribou telemetry data with Landsat fire detection techniques. Global Change Biology, 23(3), 1036–1047. https://doi.org/10.1111/gcb.13456
Rigden, A. J., & Li, D. (2017). Attribution of surface temperature anomalies induced by land use and land cover changes. Geophysical Research Letters, 44(13), 6814–6822. https://doi.org/10.1002/2017GL073811
Robinson, N. M., Leonard, W. J., Ritchie, E. G., Bassett, M., Chia, E. K., Buckingham, S., Gibb, H., Bennett, A. F., & Clarke, M. F. (2013). REVIEW: Refuges for fauna in fire-prone landscapes: Their ecological function and importance. Journal of Applied Ecology, 50(6), 1321–1329. https://doi.org/10.1111/1365-2664.12153
Rousta, I., Sarif, M. O., Gupta, R. D., Olafsson, H., Ranagalage, M., Murayama, Y., Zhang, H., & Mushore, T. D. (2018). Spatiotemporal analysis of land use/land cover and its effects on surface urban heat island using Landsat data: A case study of metropolitan city Tehran (1988–2018). Sustainability, 10(12), 4433. https://doi.org/10.3390/su10124433
Row, J. R., Knick, S. T., Oyler-McCance, S. J., Lougheed, S. C., & Fedy, B. C. (2017). Developing approaches for linear mixed modeling in landscape genetics through landscape-directed dispersal simulations. Ecology and Evolution, 7(11), 3751–3761. https://doi.org/10.1002/ece3.2825
Rudnick, D., Beier, P., Cushman, S., Dieffenbanch, F., Epps, C. W., Gerber, L. R., Hartter, J., Jenness, J. S., Kintsch, J., Merenlender, A. M., Perkl, R. M., Preziosi, D. V., & Trombulak, S. C. (2012). The role of landscape connectivity in planning and implementing conservation and restoration priorities. Issues in Ecology, 16(Fall), 1–20.
Salgueiro, P. A., Valerio, F., Silva, C., Mira, A., Rabaça, J. E., & Santos, S. M. (2021). Multispecies landscape functional connectivity enhances local bird species’ diversity in a highly fragmented landscape. Journal of Environmental Management, 284, 112066. https://doi.org/10.1016/j.jenvman.2021.112066
Salt, J. L., Bulit, C., Zhang, W., Qi, H., & Montagnes, D. J. S. (2017). Spatial extinction or persistence: landscape-temperature interactions perturb predator–prey dynamics. Ecography, 40(10), 1177–1186. https://doi.org/10.1111/ecog.02378
Santos, A. R., Oliveira, F. S., Silva, A. G., Gleriani, J. M., Gonçalves, W., Moreira, G. L., Silva, F. G., Branco, E. R. F., Moura, M. M., da Silva, R. G., Juvanhol, R. S., de Souza, K. B., Ribeiro, C. A. A. S., de Queiroz, V. T., Costa, A. V., Lorenzon, A. S., Domingues, G. F., Marcatti, G. E., de Castro, N. L. M., . . . Mota, P. H. S. (2017). Spatial and temporal distribution of urban heat islands. Science of The Total Environment, 605-606, 946-956. https://doi.org/10.1016/j.scitotenv.2017.05.275
Sawyer, S. C., Epps, C. W., & Brashares, J. S. (2011). Placing linkages among fragmented habitats: Do least-cost models reflect how animals use landscapes? Journal of Applied Ecology, 48(3), 668–678. https://doi.org/10.1111/j.1365-2664.2011.01970.x
Shen, M., Piao, S., Chen, X., An, S., Fu, Y. H., Wang, S., Cong, N., & Janssens, I. A. (2016). Strong impacts of daily minimum temperature on the green-up date and summer greenness of the Tibetan Plateau. Global Change Biology, 22(9), 3057–3066. https://doi.org/10.1111/gcb.13301
Shi, Y., Huang, W., Dong, Y., Peng, D., Zheng, Q., & Yang, P. (2018). The influence of landscape’s dynamics on the Oriental Migratory Locust habitat change based on the time-series satellite data. Journal of Environmental Management, 218, 280–290. https://doi.org/10.1016/j.jenvman.2018.04.028
Shirk, A. J., Landguth, E. L., & Cushman, S. A. (2018). A comparison of regression methods for model selection in individual-based landscape genetic analysis. Molecular Ecology Resources, 18(1), 55–67. https://doi.org/10.1111/1755-0998.12709
Silva, J. S., Silva, R. M. D., & Santos, C. A. G. (2018). Spatiotemporal impact of land use/land cover changes on urban heat islands: A case study of Paço do Lumiar, Brazil. Building and Environment, 136, 279–292. https://doi.org/10.1016/j.buildenv.2018.03.041
Sinsch, U., Oromi, N., Miaud, C., Denton, J., & Sanuy, D. (2012). Connectivity of local amphibian populations: Modelling the migratory capacity of radio-tracked Natterjack toads. Animal Conservation, 15, 388–396. https://doi.org/10.1111/j.1469-1795.2012.00527.x
Smith, M. J., Goodchild, M. F., & Longley, P. A. Geospatial Analysis. (2020, May 5). A comprehensive guide to principles techniques and software tools. Retrieved March 2, 2022, from https://www.spatialanalysisonline.com/extractv6.pdf.
Spear, S. F., & Storfer, A. (2010). Anthropogenic and natural disturbance lead to differing patterns of gene flow in the Rocky Mountain tailed frog, Ascaphus montanus. Biological Conservation, 143(3), 778–786. https://doi.org/10.1016/j.biocon.2009.12.021
Su, Y., Chen, X., Wang, C., Zhang, H., Liao, J., Ye, Y., & Wang, C. (2015). A new method for extracting built-up urban areas using DMSP-OLS nighttime stable lights: A case study in the Pearl River Delta, southern China. GIScience & Remote Sensing, 52(2), 218–238. https://doi.org/10.1080/15481603.2015.1007778
Sunday, J. M., Bates, A. E., Kearney, M. R., Colwell, R. K., Dulvy, N. K., Longino, J. T., & Huey, R. B. (2014). Thermal-safety margins and the necessity of thermoregulatory behavior across latitude and elevation. Proceedings of the National Academy of Sciences, 111(15), 5610–5615. https://doi.org/10.1073/pnas.1316145111
Theobald, D. M., Reed, S. E., Fields, K., & Soulé, M. (2012). Connecting natural landscapes using a landscape permeability model to prioritize conservation activities in the United States. Conservation Letters, 5(2), 123–133. https://doi.org/10.1111/j.1755-263X.2011.00218.x
Ullman, J. (2006). Structural equation modeling: Reviewing the basics and moving forward. Journal of Personality Assessment, 87, 35–50. https://doi.org/10.1207/s15327752jpa8701_03
Unesco - Union Nations Educations, Scientific and Cultural Organization. (2019, November 11). Biosphere Reserves. Retrieved March 2, 2022, from https://en.unesco.org/biosphere.
USGS - U.S. Geological Survey (U.S. Department of the Interior). (2019, September 12). Landsat Missions: Using the USGS Landsat8 Product.. Retrieved March 2, 2022, from https://www.usgs.gov/land-resources/nli/landsat/using-usgs-landsat-level-1-data-product.
Vasudev, D., Goswami, V. R., Srinivas, N., Syeim, B. L. N., & Sarna, A. (2021). Identifying important connectivity areas for the wide-ranging Asian elephant across conservation landscapes of Northeast India. Diversity and Distribuitions, 27(12), 2510–2526. https://doi.org/10.1111/ddi.13419
Wasserman, T. N., Cushman, S. A., Schwartz, M. K., & Wallin, O. (2010). Spatial scaling and multi-model inference in landscape genetics: Martes americana in northern Idaho. Landscape Ecology, 25, 1601–1612. https://doi.org/10.1007/s10980-010-9525-7
Weber, D., Schaepman-Strub, G., & Ecker, K. (2018). Predicting habitat quality of protected dry grasslands using Landsat NDVI phenology. Ecological Indicators, 91, 447–460. https://doi.org/10.1016/j.ecolind.2018.03.081
Wei, B., Xie, Y., Jia, X., Wang, X., He, H., & Xue, X. (2018). Land use/land cover change and it’s impacts on diurnal temperature range over the agricultural pastoral ecotone of Northern China. Land Degradation & Development, 29(9), 3009–3020. https://doi.org/10.1002/ldr.3052
Wilson, K., Hanks, E., & Johnson, D. (2018). Estimating animal utilization densities using continuous-time Markov chain models. Methods in Ecology and Evolution, 9(5), 1232–1240. https://doi.org/10.1111/2041-210X.12967
Winiarski, K. J., Peterman, W. E., & McGarigal, K. (2020). Evaluation of the R package ‘resistancega’: A promising approach towards the accurate optimization of landscape resistance surfaces. Molecular Ecology Resources, 20(6), 1583–1596. https://doi.org/10.1111/1755-0998.13217
Woodley, S. J. (2010). Ecological integrity and Canada’s National Parks. Fórum George Wright, 27, 151–160.
Zeller, K. A., Creech, T. G., Millette, K. L., Crowhurst, R. S., Long, R. A., Wagner, H. H., Balkenhol, N., & Landguth, E. L. (2016). Using simulations to evaluate Mantel-based methods for assessing landscape resistance to gene flow. Ecology and Evolution, 6(12), 4115–4128. https://doi.org/10.1002/ece3.2154
Zeller, K. A., McGarigal, K., & Whiteley, A. R. (2012). Estimating landscape resistance to movement: A review. Landscape Ecology, 27, 777–797. https://doi.org/10.1007/s10980-012-9737-0
Zhang, L., Peng, J., Liu, Y., & Wu, J. (2017). Coupling ecosystem services supply and human ecological demand to identify landscape ecological security pattern: A case study in Beijing–Tianjin–Hebei region, China. Urban Ecosystems, 20, 701–714. https://doi.org/10.1007/s11252-016-0629-y
Zhou, X., & Chen, H. (2018). Impact of urbanization-related land use land cover changes and urban morphology changes on the urban heat island phenomenon. Science of The Total Environment, 635, 1467–1476. https://doi.org/10.1016/j.scitotenv.2018.04.091
Zhou, Y., Smith, S. J., Elvidge, C. D., Zhao, K., Thomson, A., & Imhoff, M. (2014). A cluster-based method to map urban area from DMSP/OLS nightlights. Remote Sensing of Environment, 147, 173–185. https://doi.org/10.1016/j.rse.2014.03.004
Funding
This work was supported by the São Paulo Research Foundation – FAPESP (process number 2018/21612-8).
Author information
Authors and Affiliations
Contributions
Ivan Vanderley Silva: conceptualization, validation, formal analysis, investigation, data curation, writing—original draft, writing—review and editing, visualization, and project administration. Roberta Averna Valente: writing—review and editing, conceptualization, validation, formal analysis, visualization, supervision, resources, and funding acquisition.
Corresponding author
Ethics declarations
Ethics approval
Not applicable.
Consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Vanderley-Silva, I., Valente, R.A. Landscape resistance index aiming at functional forest connectivity. Environ Monit Assess 195, 1224 (2023). https://doi.org/10.1007/s10661-023-11749-x
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10661-023-11749-x