Dominant climate influences on North American bird distributionsgeb_

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1 Global Ecology and Biogeography, (Global Ecol. Biogeogr.) (2011) 20, RESEARCH PAPER Dominant climate influences on North American bird distributionsgeb_ Alberto Jiménez-Valverde*, Narayani Barve, Andrés Lira-Noriega, Sean P. Maher, Yoshinori Nakazawa, Monica Papeş, Jorge Soberón, Jeet Sukumaran and A. Townsend Peterson Natural History Museum and Biodiversity Research Center, University of Kansas, Lawrence, Kansas 66045, USA ABSTRACT Aim Geographic distributions of species are constrained by several factors acting at different scales, with climate assumed to be a major determinant at broad extents. Recent studies, however, have challenged this statement and indicated that climate may not dominate among the factors governing geographic distributions of species. Here, we argue that these results are misleading due to the lack of consideration of the geographic area that has been accessible to the species. Location North America. Methods We generated null distributions for 75 North American endemic and 19 non-endemic bird species. For each species, climatic envelopes of observed and null distributions were modelled using neural networks and generalized linear models, and seven climatic predictors. Values of the area under the receiver operating characteristic curve (AUC) based on models of observed distributions were compared with corresponding AUC values for the null distributions. Results More than 82% of the endemic species showed AUC higher for the observed than for the null distributions, while 63% of the non-endemic species showed such a pattern. *Correspondence: Alberto Jiménez-Valverde, Natural History Museum and Biodiversity Research Center, University of Kansas, Lawrence, KS 66045, USA. ajvalv@ku.edu Main conclusions We demonstrate a dominant climatic signal in shaping North American bird distributions. Our results attest to the importance of climate in determining species distributions and support the use of climate-envelope models for estimating potential distributional areas at the appropriate spatial scales. Keywords Biogeography, birds, climate-envelope models, North America, null models, spatial extent, species distributions. INTRODUCTION Understanding the factors that determine species geographic distributions is a fundamental goal of ecology and biogeography (Brown et al., 1996; Gaston, 2003). It has long been known that climate affects the coarser features of distributions via wellknown ecophysiological constraints (von Humboldt & Bonpland, 1805; Grinnell, 1917; Udvardy, 1969; Gaston, 2003). Strong evidence backs the idea that climate plays an important role in limiting species distributions: convergence of range limits and climatic boundaries (Jeffree & Jeffree, 1994), observed range shifts following climate change (Parmesan et al., 1999), successful reconstructions of past climates from species palaeodistributional information (Atkinson et al., 1987) and reliable predictions of palaeodistributions based on present relationships between species distributions and climate (Benito Garzón et al., 2007). However, other processes may disrupt the match between distributions of species and climate: biotic interactions may keep species from inhabiting otherwise suitable sites (Bullock et al., 2000), and dispersal limitation may prevent colonization of other suitable areas (Svenning & Skov, 2007). Climate, dispersal limitation and biotic interactions all contribute to delimiting distributions; because each factor acts at a different spatial scale, interrelationships among them can be complex (Soberón, 2007). At the broadest extents, the biogeographic history of the species may determine its accessible region (M; Soberón & Peterson, 2005). Within M, climate may restrict population growth via physiological constraints while, at 114 DOI: /j x 2010 Blackwell Publishing Ltd

2 Climate influences on bird distributions finer resolutions, biotic interactions may produce a mosaic of occupied and unoccupied sites within climatically suitable areas (Soberón, 2007). Without making explicit reference to M, then, climatically determined borders may remain hidden. A recent, challenging study indicated frequent failure of climate parameters in setting the broad limits of species distributions. Beale et al. (2008) compared the performance of climate-envelope models for distributions of European birds with those of null distributional areas that were randomly placed with respect to climate. Because their results showed no significant difference between performance of real and null models, they argued that present distributions of European birds are not strongly constrained by climatic factors. Although methodological details such as data quality (see Araújo et al., 2009) may have influenced their results, Peterson et al. (2008) argued that Beale and collaborators did not constrain analyses to appropriate spatial extents, thus excluding key climatedetermined limits. Here, we demonstrate that distributions of North American birds show a strong climatic signal once the above-mentioned scale framework is accounted for, revealing the clear importance of climate in determining species distributions. METHODS Following the methods of a previous study (Beale et al., 2008), we generated sets of 99 null distributions for 75 North American endemic and 19 non-endemic bird species (see Appendix S1 in Supporting Information) that preserve the observed spatial structure (prevalence and semi-variogram), but break down observed distribution climate relationships (Beale et al., 2008) (Fig. 1a). For each species, climatic envelopes of observed and null distributions were modelled using neural networks (NNETs) and generalized linear models (GLMs), based on occurrence data and seven climatic variables as predictors. Values of the area under the receiver operating characteristic (a) (b) 20 Figure 1 (a) The smaller map shows the breeding distribution of Brewer s blackbird (Euphagus cyanocephalus); two example null distributions (in black and grey) corresponding to this species are shown in the main map. The dotted line in Canada represents the northern limit of the area considered (see Materials and Methods). (b) Histogram of area under the receiver operating characteristic curve (AUC) values of the 99 null distributions of Brewer s blackbird obtained with neural networks. The arrow indicates the position of the AUC of the observed distribution. Number of null species AUC Global Ecology and Biogeography, 20, , 2010 Blackwell Publishing Ltd 115

3 A. Jiménez-Valverde et al. curve (AUC) based on models of observed distributions were compared with corresponding AUC values for the 99 null distributions for each species (Fig. 1b; see Appendix S1). If real distributions fit to climate regimes consistently better than null distributions, then we can conclude that real distributions have significant climatic signal; on the other hand, little can be concluded if real distributions are not modelled better than the random patterns (Peterson et al., 2009). Species occurrence data Endemic species are those restricted as breeders to the region under consideration, with the southern limit at the US Mexican border, and the northern limit extending between 70.5 N (northern Alaska) and 54 N (south of Hudson Bay) latitude (Fig. 1a), reflecting the limits of the survey data available. The non-endemic species chosen had latitudinal limits of breeding ranges extending far beyond the southern limit and/or the northern limit. In all, 75 endemic and 19 non-endemic species were extracted from the results of the North American Breeding Bird Survey (BBS, 2001). We used the American Ornithologists Union check-list of North American birds (AOU, 1998) to compare species breeding ranges with the latitudinal limits of BBS coverage, and retained only those species that fit the definitions of endemic and non-endemic, and that were represented by >100 records in the BBS database. Climate data Seven climatic variables were used to characterize each of the km cells: annual mean temperature, annual mean temperature range, annual maximum temperature, annual minimum temperature, annual precipitation, precipitation of the wettest month and precipitation of the driest month. These variables were obtained from the WorldClim interpolated map database (Hijmans et al., 2005). Climatic-envelope models Real and null distributions were modelled using NNETs (Venables & Ripley, 2002) with seven neurons in the hidden layer, initial connection weights set to 0.03 and the maximum number of iterations set to NNETs were run 10 times and the mean was used as the predictor (Thuiller, 2003). GLMs (McCullagh & Nelder, 1989) were also run with linear and second-order terms; two-way interaction of linear terms was also included. No model selection (e.g. stepwise) function was applied. We trained GLMs and NNETs with 70% of the occurrence data chosen at random, and validated the models with the remaining 30% via the AUC (Fawcett, 2006). Finally, AUC values of real distribution models were compared with those corresponding to the 99 null distribution models for each species. Despite recent criticisms of the AUC approach(see Loboet al., 2008, and Peterson et al., 2008), we provide two reasons to justify its use in this study. First, we are comparing AUC of the same species, i.e. the real distributional data against null ones, and so the complicating effects of extent (probably the major problem with the AUC; see Lobo et al., 2008) are less of a problem. Second, use of AUC-based approaches makes possible a direct comparison with the results of Beale et al. (2008), placing our results in a richer context. The R code published by Beale et al. (2008) was run to model observed and null distributions, using the NNET (Venables & Ripley, 2002) and verification (NCAR, 2008) packages for R (R Development Core Team, 2008). RESULTS AND DISCUSSION In both NNET and GLM analyses the great majority (92.0 and 82.7%, respectively) of endemic species showed significant climate determination (Fig. 2; Appendix S1). In both cases, fewer non-endemic species showed climate determination (63.2%, in the two cases) than endemic species; the difference was statistically significant with NNETs (c 2 = , d.f. = 1, P < 0.01) and almost significant with GLMs (c 2 = 3.445, d.f. = 1, P = 0.06; Fig. 2; Appendix S1). Null distributions We followed the testing procedures detailed in Beale et al. (2008). Observed distribution patterns were characterized by a clumping statistic that measures the probability of a cell being occupied given the state (empty or occupied) of nearby cells (Roxburgh & Chesson, 1998; Beale et al., 2008). Then, a random pattern is generated for each species, maintaining the observed prevalence, and the clumping statistic is calculated. A match between both statistics (that corresponding to the observed distribution and that corresponding to the null distribution) is achieved iteratively by swapping empty versus occupied cells two by two (Roxburgh & Chesson, 1998). As a result, null patterns maintaining the same prevalence and spatial structure (as characterized by semi-variograms) as observed distributions are generated (Roxburgh & Chesson, 1998; Beale et al., 2008). We generated 99 null patterns for each species using the R code (R Development Core Team, 2008) published by Beale et al. (2008). Percentage of species 100% 67% 33% 0% P-values Figure 2 Histograms of P-values obtained with neural networks in the case of endemic (grey) and non-endemic (black) species (generalized linear models show a similar pattern). 116 Global Ecology and Biogeography, 20, , 2010 Blackwell Publishing Ltd

4 Climate influences on bird distributions It is well known that different ecological processes operate at different spatial scales (Wiens, 1989). Species distributions are conditioned by factors in a hierarchical way (Boyce, 2006; Meyer & Thuiller, 2006), and numerous authors have shown that the relative importance of environmental variables changes with the spatial scale of analyses (e.g. Johnson et al., 2004, and Olivier & Wotherspoon, 2005, among many others). As mentioned above, climate, biotic interactions and dispersal limitation all contribute to delimiting species distributions, but because they are hierarchical factors (Soberón, 2007) their importance will depend on the scale being considered. Our results show climate to be an important determinant of the distribution of North American birds. The significant differences between endemic and non-endemic species in our analyses indicate the importance of appropriate spatial extents: in the case of non-endemics, M exceeds the region under consideration, so climatic limits may not be accounted for in these species. Missing the climatic limits of the species may lead to the incorrect assertion that distributions of species are not determined by climate (Beale et al., 2008). Although intuitive, testing the hypothesis of climatic determination of ranges has been challenging, owing to the impossibility of experimentation across such broad spatial extents, and leaving only correlational evidence to support or refute the idea. However, developments in spatial null models have opened new ways to advance this field. Here, using refined and appropriate applications of these methods, we provide evidence that distributions of North American birds are structured by climatic factors. This corroboration has broad and profound implications in numerous areas of biogeographic research, including for climate change projections of species potential distributions, evolutionary ecology of species distributions and biogeographic reconstructions of geographic history. ACKNOWLEDGEMENTS We thank Colin M. Beale for his help with running the R code for generating null distributions. A.J.-V. was supported by a MEC (Ministerio de Educación y Ciencia, Spain) post-doctoral fellowship (ref. EX ). A.L.-N. received graduate studies scholarship support from CONACyT (189216). A.T.P and J.S. were supported by a grant from Microsoft Research. REFERENCES AOU (1998) Check-list of North American birds, 7th edn. American Ornithologists Union, Washington, DC. Araújo, M.B., Thuiller, W. & Yoccoz, N.G. (2009) Reopening the climate envelope reveals macroscale associations with climate in European birds. Proceedings of the National Academy of Sciences USA, 106, E45 E46. 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5 A. Jiménez-Valverde et al. Peterson, A.T., Papeş, M. & Soberón, J. (2008) Rethinking receiver operating characteristic analysis applications in ecological niche modelling. Ecological Modelling, 213, Peterson, A.T., Barve, N., Bini, L.M., Diniz-Filho, J.A., Jiménez- Valverde, A., Lira-Noriega, A., Lobo, J., Maher, S., de Marco, P., Jr, Martínez-Meyer, E., Nakazawa, Y. & Soberón, J. (2009) The climate envelope may not be empty. Proceedings of the National Academy of Sciences USA, 106, E47. R Development Core Team (2008) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available at: (accessed October 2008). Roxburgh, S.H. & Chesson, P. (1998) A new method for detecting species associations with spatially autocorrelated data. Ecology, 79, Soberón, J. (2007) Grinnellian and Eltonian niches and geographic distributions of species. Ecology Letters, 10, Soberón, J. & Peterson, A.T. (2005) Interpretation of models of fundamental ecological niches and species distributional areas. Biodiversity Informatics, 2, Svenning, J.-C. & Skov, F. (2007) Could the tree diversity pattern in Europe be generated by postglacial dispersal limitation? Ecology Letters, 10, Thuiller, W. (2003) BIOMOD optimizing predictions of species distributions and projecting potential future shifts under global change. Global Change Biology, 9, Udvardy, M. (1969) Dynamic zoogeography. With special reference to land animals. van Nostrand Reinhold, New York. Venables, W.N. & Ripley, B.D. (2002) Modern applied statistics with S, 4th edn. Springer, New York. Wiens, J.A. (1989) Spatial scaling in ecology. Functional Ecology, 3, SUPPORTING INFORMATION Additional Supporting Information may be found in the online version of this article: Appendix S1 Summary of the 94 bird species used in the analyses. As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer-reviewed and may be re-organized for online delivery, but are not copy-edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors. BIOSKETCH Alberto Jiménez-Valverde is interested in broad-scale patterns of biodiversity. He is particularly interested in methods for modelling potential distributions of species in order to understand the relative importance of environmental, biotic and historical factors in limiting geographical ranges. Editor: Katrin Böhning-Gaese 118 Global Ecology and Biogeography, 20, , 2010 Blackwell Publishing Ltd