Defining Landscapes and Scales to Model Landscape–Organism Interactions
Tóm tắt
We review and provide comment on issues of scale in ecological studies in the context of two paradigms used to define landscapes: the patch-mosaic and gradient models. Our intent is to offer guidance for structuring habitat-selection models with examples of how scale, autocorrelation, measurement error, and choice of patch-mosaic or gradient models, analysis methods, and covariates by the researcher can influence inferences regarding landscape–organism interactions. Methods that allow the organism or data to define the grain and extent of scale of the study offer promise by reducing subjectivity in choices of scale. Ultimately, we recommend that the ecological phenomenon of interest should shape the selection of models defining landscape–organism interaction; however, the choice of model remains with the researcher and is dependent on the research question and the availability of data. Clearly, both the patch-mosaic and gradient models can provide reasonable frameworks for study, and multiple scales that draw from both paradigms often may be most appropriate. Scale has been identified as a crucial feature of landscape ecology, yet scale as a paradigm has offered little direction for ecologists. Likewise, debate contrasting gradient models and patch-mosaic models offers few new insights on how ecologists might decide on an appropriate scale for analysis of organism distribution or habitat selection. Various ecological processes influence organisms at different scales and modeling approaches need to be able to accommodate multiple scales simultaneously, which may vary by landscape structure and movement ecology. The continuum of scales and combinations of both gradient and patch-mosaic landscapes provides the necessary array of structures that can be used to construct combinations of landscape covariates that coincide with the ecology of the organism across scales.
Tài liệu tham khảo
Fretwell SD, Lucas HL. On territorial behaviour and other factors influencing habitat distribution in birds. Acta Biotheor. 1970;19:16–36.
Levins R. Extinction. In: Gerstenhaber M., editor. Some mathematical questions in biology. Providence: American Mathematical Society; 1970. p. 75–107.
Charnov EL. Optimal foraging, the marginal value theorem. Theor Popul Biol. 1976;9:129–36.
Forman RTT, Godron M. Landscape ecology. New York: Wiley; 1986.
Turner MG. Landscape ecology: the effect of pattern on process. Annu Rev Ecol Syst. 1989;20:171–97.
McGarigal K, Cushman SA. The gradient concept of landscape structure. In: Wiens J, Moss M, editors. Issues and perspectives in landscape ecology. Cambridge: Cambridge University Press; 2005. p. 112–9.
Cushman SA, Gutzweiler K, Evans JS, McGarigal K. The gradient paradigm: a conceptual and analytical framework for landscape ecology. In: Cushman SA, Huettmann F, editors. Spatial complexity, informatics, and wildlife conservation. New York: Springer; 2010. p. 83–108.
Neumann C, Weiss G, Schmidtlein S, Itzerott S, Lausch A, Doktor D, et al. Gradient-based assessment of habitat quality for spectral ecosystem monitoring. Remote Sens. 2015;7:2871–98.
Pettorelli N, Laurance WF, O'Brien TG, Wegmann M, Nagendra H, Turner W. Satellite remote sensing for applied ecologists: opportunities and challenges. J Appl Ecol. 2014;51:839–48.
Davies AB, Avner GP. Advances in animal ecology from 3D-LiDAR ecosystem mapping. Trends Ecol Evol. 2014;29:681–691. https://doi.org/10.1016/j.tree.2014.10.005
Grêt-Regamey A, Weibel B, Bagstad K, Ferrari M, Geneletti D, Klug H, et al. On the effects of scale for ecosystem services mapping. PLoS One. 2014;9(12):e112601. https://doi.org/10.1371/journal.pone.0112601.
Morris LR, Proffitt KM, Blackburn JK. Mapping resource selection functions in wildlife studies: Concerns and recommendations. Appl Geogr. 2016;76:173–83.
Krebs CJ. Ecology. The experimental analysis of distribution and abundance. San Francisco: Benjamin Cummings; 1972.
Nathan R, Getz WM, Revilla E, Holyoak M, Kadmon R, Saltz D, et al. A movement ecology paradigm for unifying organismal movement research. Proc Natl Acad Sci U S A. 2008;105:19052–9.
Keskitalo CEH, Horstkotte T, Kivinen S, Forbes B, Käyhkö J. Generality of mis-fit? The real-life difficulty of matching scales in an interconnected world. Ambio. 2016;45:742–52.
• McGarigal K, Wan HY, Zeller KA, Timm BC, Cushman SA. Multi-scale habitat selection modeling: a review and outlook. Landsc Ecol. 2016;31:1161–75. https://doi.org/10.1007/s10980-016-0374-x. Major review of scale in habitat-selection studies concluding that multi-scale analysis has a powerful conceptual foundation yet relatively few studies conduct their analysis at multiple scales
• Prokopenko CM, Boyce MS, Avgar T. Extent-dependent habitat selection in a migratory large herbivore: road avoidance across scales. Landsc Ecol. 2017;32:313–25. https://doi.org/10.1007/s10980-016-0451-1. Two biologically relevant scales of behavioral response to landscape, i.e., migration and home range, were studied among individual elk. Response to landscape gradients was conserved across scales, supporting a scale-independent hypothesis of habitat selection. Consistent avoidance of roads across scales highlighted the pervasiveness of human disturbance on this population
Manly BFJ, McDonald LL, Thomas DL, McDonald TL, Erickson WP. Resource selection by animals. Second Edition. Dordrecht: Kluwer; 2002.
• Zeller KA, McGarigal K, Cushman SA, Beier P, Vickers TW, Boyce WM. Sensitivity of resource selection and connectivity models to landscape definition. Landsc Ecol. 2017;32:832–55. Fine spatial grain and multiple geo-spatial layers of data greatly enhanced the predictive ability of path selection models for cougars. Careful attention to landscape definition is recommended
Brudvig LA, Leroux SH, Alberta CH, Bruna EM, Davies KF, Ewers RM, et al. Evaluating conceptual models of landscape change. Ecography. 2017;40:74–84.
Ries L, Fletcher RJ Jr, Battin J, Sisk TD. Ecological responses to habitat edges: mechanisms, models, and variability explained. Annu Rev Ecol Evol Syst. 2004;35:491–522. https://doi.org/10.1146/annurev.ecolsys.35. 112202.130148.
Comfort EJ, Clark DA, Anthony RG, Bailey J, Betts MG. Quantifying edges as gradients at multiple scales improves habitat selection models for northern spotted owl. Landsc Ecol. 2016;31:1227–40. https://doi.org/10.1007/s10980-015-0330-1.
Seidel D, Boyce MS. Varied tastes: home range implications of foraging patch selection. Oikos. 2016;125:39–49.
Benz R, Boyce MS, Thurfjell H, Paton DG, Musiani M, Dormann C, et al. Dispersal ecology informs design of large-scale wildlife corridors. PLoS One. 2016;11(9):e0162989.
Prokopenko CM, Boyce MS, Avgar T. Characterizing wildlife behavioural responses to roads using integrated step selection analysis. J Appl Ecol. 2017; https://doi.org/10.1111/1365-2664.12768.
Lesmerises R, Ouellet J-P, Dussault C, St-Laurent M-H. The influence of landscape matrix on isolated patch use by wide-ranging animals: Conservation lessons for woodland caribou. Ecol Evol. 2013;3:2880–91.
DeCesare NJ, Hebblewhite M, Schmiegelow FKA, Hervieux D, McDermid GJ, Neufeld L, et al. Transcending scale dependence in identifying habitat with resource selection functions. Ecol Appl. 2012;22:1068–83.
Avelino AFT, Baylis K, Honey-Rosés J. Goldilocks and the raster grid: selecting scale when evaluating conservation programs. PLoS One. 2016;11(12):e0167945. https://doi.org/10.1371/journal.pone.0167945.
Bissonette JA. Avoiding the scale sampling problem: a consilient solution. J Wildl Manag. 2017;81:192–205.
Dixon Hamil K-A, Iannone BV III, Huang WK, Fei S, Zhang H. Cross-scale contradictions in ecological relationships. Landsc Ecol. 2016;31:7–18.
de Jong R, de Bruin S. Linear trends in seasonal vegetation time series and the modifiable temporal unit problem. Biogeosciences. 2012;9:71–7.
Lechner AM, Langford WT, Jones SD, Bekessy SA, Gordon A. Investigating species–environment relationships at multiple scales: Differentiating between intrinsic scale and the modifiable areal unit problem. Ecol Complex. 2012;11:91–102.
Latham ADM, Latham MC, Anderson DP, Cruz J, Herries D, Hebbblewhite M. The GPS craze: six questions to address before deciding to deploy GPS technology on wildlife. New Zeal J Ecol. 2015;39:143–52.
Sokal RR. Ecological parameters inferred from spatial correlograms. In: Patil GP, Rosenzweig ML, editors. Contemporary quantitative ecology and related econometrics, vol. 12. Fairland, Maryland: International Cooperative Publishing House; 1979. p. 167–96.
Bailey DW, Gross JE, Laca EA, Rittenhouse LR, Coughenour MB, Swift DM, et al. Mechanisms that result in large herbivore grazing distribution patterns. J Range Manag. 1996;49:386–400.
Legendre P. Spatial autocorrelation: trouble or new paradigm? Ecology. 1993;74:1659–73. https://doi.org/10.2307/1939924.
Cressie NAC. Statistics for spatial data. New York: J Wiley; 2015.
Roberts D, Bahn V, Ciuti S, Boyce MS, Elith J, Guillera-Arroita G, Hauenstein S, Lahoz-Monfort J, Schroder B, Thuiller W, Warton D, Wintle B, Hartig F, Dormann C. Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography. 2017;doi: https://doi.org/10.1111/ecog.02881.
Radeloff VC, Mladenoff DJ, Boyce MS. The changing relation of landscape pattern to jack pine budworm populations during an outbreak. Oikos. 2000;90:417–30.
Roever CL, Beyer HL, Chase MJ, Aarde RJ. The pitfalls of ignoring behaviour when quantifying habitat selection. Divers Distrib. 2014;20:322–33.
Rivest LP, Duchesne T, Nicosia A, Fortin D. A general angular regression model for the analysis of data on animal movement in ecology. J R Stat Soc C App Stat. 2016;65:445–63.
Avgar T, Potts JR, Lewis MA, Boyce MS. Integrated step selection analysis: bridging the gap between resource selection and animal movement. Methods Ecol Evol. 2016;7:619–30.
Loken E, Gelman A. Measurement error and the replication crisis. Science. 2017;355(6325):584–5. https://doi.org/10.1126/science.aam5409.
Morehouse AT, Boyce MS. Deviance from truth: telemetry location errors erode both precision and accuracy of habitat-selection models. Wildl Soc Bull. 2013;37:596–602. https://doi.org/10.1002/wsb.292.
Huque MH, Bondel HD, Ryan L. On the impact of covariate measurement error in spatial regression modelling. Environmetrics. 2014;25:560–70. https://doi.org/10.1002/env.2305.
D’Eon RG. Effects of a stationary GPS fix-rate bias on habitat-selection analyses. J Wildl Manag. 2003;67:858–63.
Frair JL, Nielsen SE, Merrill EH, Lele SR, Boyce MS, Munro RHM, et al. Removing GPS collar bias in habitat selection studies. J Appl Ecol. 2004;41:201–12.
Frair JL, Fieberg J, Hebblewhite M, Cagnacci F, DeCesare NJ, Pedrotti L. Resolving issues of imprecise and habitat-biased locations in ecological analyses using GPS telemetry data. Phil Trans Roy Soc B. 2010;365:2187–200.
Brost BM, Hooten MB, Hanks EM, Small RJ. Animal movement constraints improve resource selection inference in the presence of telemetry error. Ecology. 2015;96:2590–7.
Johnson CJ, Gillingham MP. Sensitivity of species-distribution models to error, bias, and model design: an application to resource selection functions for woodland caribou. Ecol Model. 2008;213:143–55.
Hefley TJ, Baasch DM, Tyre AJ, Blankenship ER. Correction of location errors for presence-only species distribution models. Methods Ecol Evol. 2014;5:207–14.
Moody A, Woodcock CE. The influence of scale and the spatial characteristics of landscapes on land-cover mapping using remote sensing. Landsc Ecol. 1995;10:363–79.
McKenzie H, Jerde CL, Visscher DR, Merrill EH, Lewis MA. Inferring linear feature use in the presence of GPS measurement error. Environ Ecol Stat. 2009;16:531–46.
Ladle A, Avgar T, Wheatley M, Boyce MS. Predictive modeling of ecological patterns along linear-feature networks. Methods Ecol Evol. 2017;8:329–38. https://doi.org/10.1111/2041-210X.12660.
Zhou W, Cadenasso ML. Effects of patch characteristics and within patch heterogeneity on the accuracy of urban land cover estimates from visual interpretation. Landsc Ecol. 2012;27:1291–305.
Wiegand T, Revilla E, Knauer F. Dealing with uncertainty in spatially explicit population models. Biodivers Conserv. 2014;13:53–78.
Watson SJ, Luck GW, Spooner PG, Watson DM. Land-use change: incorporating the frequency, sequence, time span, and magnitude of changes into ecological research. Front Ecol Environ. 2014;12:241–9.
Rodgers AR. Recent telemetry technology. In: Millspaugh JJ, Marzluff JM, editors. Radio tracking and animal populations. San Diego, Calif: Academic Press; 2001. p. 82–121.
Stine PA, Hunsaker CT. An introduction to uncertainty issues for spatial data. In: Hunsaker CT, Goodchild MF, Friedl MA, Case TJ, editors. Spatial uncertainty in ecology: implications for remote sensing and GIS applications. New York: Springer; 2001. p. 91–107.
Thurfjell H, Ciuti S, Boyce MS. Applications of step-selection functions in ecology and conservation. Movement Ecol. 2004;2:4, p. 1–12;doi:https://doi.org/10.1186/2051–3933–2-4.
Cristescu B, Boyce MS. Focusing ecological research for conservation. Ambio. 2013;42:805–15. https://doi.org/10.1007/s13280-013-0410-x.
Northrup JM, Hooten MB, Anderson CR, Wittemyer G. Practical guidance on characterizing availability in resource selection functions under a use-availability design. Ecology. 2013;94:1456–63.
Laforge MP, Brook RK, van Beest FM, Bayne EM, McLoughlin PD. Grain-dependent functional responses in habitat selection. Landsc Ecol. 2015:1–9. https://doi.org/10.1007/s10980-015-0298-x.
Lele SR, Merrill EH, Keim J, Boyce MS. Selection, choice, use, and occurrence: clarifying concepts in resource selection studies. J Anim Ecol. 2013;82:1183–91.
Boyce MS, Mao JS, Merrill EH, Fortin D, Turner MG, Fryxell J, et al. Scale and heterogeneity in habitat selection by elk in Yellowstone National Park. Ecoscience. 2003;10:421–31.
Hebblewhite M, Merrill EH. Trade-offs between predation risk and forage differ between migrant strategies in a migratory ungulate. Ecology. 2009;90:3445–54.
Orians GH, Wittenberger JF. Spatial and temporal scales in habitat selection. Am Nat. 1991;137:S29–49.
Rettie WJ, Messier F. Hierarchical habitat selection by woodland caribou: its relationship to limiting factors. Ecography. 2000;23:466–78. https://doi.org/10.1034/j.1600-0587.2000.230409.x.
Kittle AM, Fryxell JM, Desy GE, Hamr J. The scale-dependent impact of wolf predation risk on resource selection by three sympatric ungulates. Oecologia. 2008;157:163–75. https://doi.org/10.1007/s00442-008-1051-9.
Owen-Smith N, Fryxell JM, Merrill EH. Foraging theory upscaled: the behavioural ecology of herbivore movement. Philos Trans R Soc B. 2010;365:2267–78. https://doi.org/10.1098/rstb.2010.0095.
McGreer MT, Mallon EE, Vander Vennen LM, Wiebe PA, Baker JA, Brown GS, et al. Selection for forage and avoidance of risk by woodland caribou (Rangifer tarandus caribou) at coarse and local scales. Ecosphere. 2015;6:1–11.
Levin SA. The problem of pattern and scale in ecology. Ecology. 1992;73:1943–67.
Benhamou S. Of scales and stationarity in animal movements. Ecol Lett. 2014;17:261–72.
Moreau G, Fortin D, Couturier S, Duchesne T. Multi-level functional responses for wildlife conservation: the case of threatened caribou in managed boreal forests. J Appl Ecol. 2012;49:611–20.
Shirk AJ, Raphael MG, Cushman SA. Spatiotemporal variation in resource selection: insights from the American marten (Martes americana). Ecol Appl. 2014;24:1434–44.
Chave J. The problem of pattern and scale in ecology: what have we learned in 20 years? Ecol Lett. 2013;16:4–16.
Wheatley M, Johnson CJ. Factors limiting our understanding of ecological scale. Ecol Complex. 2009;6:150–9. https://doi.org/10.1016/j.ecocom.2008.10.011.
Killeen J, Thurfjell H, Ciuti S, Paton D, Musiani M, Boyce MS, et al. Movement Ecol. 2014;2:15. https://doi.org/10.1186/s40462-014-0015-4.
Morrison CD, Boyce MS, Nielsen SE, et al. PeerJ. 2015;3:e1118. https://doi.org/10.7717/peerj.1118.
Skelsey P, With KA, Garrett KA. Why dispersal should be maximized at intermediate scales of heterogeneity. Theor Ecol. 2013;6:203–11.
Seidel D, Boyce MS. Patch-use dynamics by a large herbivore. Movement Ecol. 2015;3:7. https://doi.org/10.1186/s40462-015-0035-8.
Boyce MS, et al. Divers Distrib. 2006;12:269–76. https://doi.org/10.1111/j.1366–9516.2006.00243.x.