Browsing by Author "Field, Richard"
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- Coefficient shifts in geographical ecology: an empirical evaluation of spatial and non-spatial regressionPublication . Bini, L. Mauricio; Diniz-Filho, J. Alexandre F.; Rangel, Thiago F. L. V. B.; Akre, Thomas S. B.; Albaladejo, Rafael G.; Albuquerque, Fabio S.; Aparicio, Abelardo; Araújo, Miguel B.; Baselga, Andrés; Beck, Jan; Bellocq, M. Isabel; Böhning-Gaese, Katrin; Borges, Paulo A. V.; Castro-Parga, Isabel; Chey, Vun Khen; Chown, Steven L.; Marco, Paulo de Jr; Dobkin, David S.; Ferrer-Castán, Dolores; Field, Richard; Filloy, Julieta; Fleishman, Erica; Gómez, Jose F.; Hortal, Joaquín; Iverson, John B.; Kerr, Jeremy T.; Kissling, W. Daniel; Kitching, Ian J.; León-Cortés, Jorge L.; Lobo, Jorge M.; Montoya, Daniel; Morales-Castilla, Ignacio; Moreno, Juan C.; Oberdorff, Thierry; Olalla-Tárraga, Miguel Á.; Pausas, Juli G.; Qian, Hong; Rahbek, Carsten; Rodríguez, Miguel Á.; Rueda, Marta; Ruggiero, Adriana; Sackmann, Paula; Sanders, Nathan J.; Terribile, Levi Carina; Vetaas, Ole R.; Hawkins, Bradford A.A major focus of geographical ecology and macro ecology is to understand the causes of spatially structured ecological patterns. However, achieving this understanding can be complicated when using multiple regressions, because the relative importance of explanatory variables, as measured by regression coefficients, can shift depending on whether spatially explicit or non-spatial modelling is used. However, the extent to which coefficients may shift and why shifts occur are unclear. Here, we analyze the relationship between environmental predictors and the geographical distribution of species richness, body size, range size and abundance in 97 multi-factorial data sets. Our goal was to compare standardized partial regression coefficients of non-spatial ordinary least squares regressions (i.e. models fitted using ordinary least squares without taking autocorrelation into account; “OLS models” hereafter) and eight spatial methods to evaluate the frequency of coefficient shifts and identify characteristics of data that might predict when shifts are likely. We generated three metrics of coefficient shifts and eight characteristics of the data sets as predictors of shifts. Typical of ecological data, spatial autocorrelation in the residuals of OLS models was found in most data sets. The spatial models varied in the extent to which they minimized residual spatial autocorrelation. Patterns of coefficient shifts also varied among methods and datasets, although the magnitudes of shifts tended to be small in all cases. We were unable to identify strong predictors of shifts, including the levels of autocorrelation in either explanatory variables or model residuals. Thus, changes in coefficients between spatial and non-spatial methods depend on the method used and are largely idiosyncratic, making it difficult to predict when or why shifts occur. We conclude that the ecological importance of regression coefficients cannot be evaluated with confidence irrespective of whether spatially explicit modelling is used or not. Researchers may have little choice but to be more explicit about the uncertainty of models and more cautious in their interpretation.
- Oceanic island biogeography through the lens of the General Dynamic Model : assessment and prospectPublication . Borregaard, Michael K.; Amorim, Isabel R.; Borges, Paulo A. V.; Cabral, Juliano S.; Fernández-Palacios, José María; Field, Richard; Heaney, Lawrence R.; Kreft, Holger; Matthews, Thomas J.; Olesen, Jens M.; Price, Jonathan; Rigal, François; Steinbauer, Manuel J.; Triantis, Konstantinos A.; Valente, Luis; Weigelt, Patrick; Whittaker, Robert J.The general dynamic model of oceanic island biogeography (GDM) has added a new dimension to theoretical island biogeography in recognizing that geological processes are key drivers of the evolutionary processes of diversification and extinction within remote islands. It provides a dynamic and essentially non-equilibrium framework generating novel predictions for emergent diversity properties of oceanic islands and archipelagos. Its publication in 2008 coincided with, and spurred on, renewed attention to the dynamics of remote islands. We review progress, both in testing the GDM’s predictions and in developing and enhancing ecological–evolutionary understanding of oceanic island systems through the lens of the GDM. In particular, we focus on four main themes: (i) macroecological tests using a space-for-time rationale; (ii) extensions of theory to islands following different patterns of ontogeny; (iii) the implications of GDM dynamics for lineage diversification and trait evolution; and (iv) the potential for downscaling GDM dynamics to local-scale ecological patterns and processes within islands. We also consider the implications of the GDM for understanding patterns of non-native species diversity. We demonstrate the vitality of the field of island biogeography by identifying a range of potentially productive lines for future research.
- Snapshot isolation and isolation history challenge the analogy between mountains and islands used to understand endemismPublication . Flantua, Suzette G. A.; Payne, Davnah; Borregaard, Michael K.; Beierkuhnlein, Carl; Steinbauer, Manuel J.; Dullinger, Stefan; Essl, Franz; Irl, Severin D. H.; Kienle, David; Kreft, Holger; Lenzner, Bernd; Norder, Sietze; Rijsdijk, Kenneth F.; Rumpf, Sabine B.; Weigelt, Patrick; Field, RichardAIM: Mountains and islands are both well known for their high endemism. To explain this similarity, parallels have been drawn between the insularity of "true islands" (land surrounded by water) and the isolation of habitats within mountains (so-called "mountain islands"). However, parallels rarely go much beyond the observation that mountaintops are isolated from one another, as are true islands. Here, we challenge the analogy between mountains and true islands by re-evaluating the literature, focusing on isolation (the prime mechanism underlying species endemism by restricting gene flow) from a dynamic perspective over space and time. FRAMEWORK: We base our conceptualization of "isolation" on the arguments that no biological system is completely isolated; instead, isolation has multiple spatial and temporal dimensions relating to biological and environmental processes. We distinguish four key dimensions of isolation: (a) environmental difference from surroundings; (b) geographical distance to equivalent environment [points (a) and (b) are combined as "snapshot isolation"]; (c) continuity of isolation in space and time; and (d) total time over which isolation has been present [points (c) and (d) are combined as "isolation history"]. We evaluate the importance of each dimension in different types of mountains and true islands, demonstrating that substantial differences exist in the nature of isolation between and within each type. In particular, different types differ in their initial isolation and in the dynamic trajectories they follow, with distinct phases of varying isolation that interact with species traits over time to form present-day patterns of endemism. CONCLUSIONS: Our spatio-temporal definition of isolation suggests that the analogy between true islands and mountain islands masks important variation of isolation over long time-scales. Our understanding of endemism in isolated systems can be greatly enriched if the dynamic spatio-temporal dimensions of isolation enter models as explanatory variables and if these models account for the trajectories of the history of a system.
- Topography-driven isolation, speciation and a global increase of endemism with elevationPublication . Steinbauer, Manuel J.; Field, Richard; Grytnes, John-Arvid; Trigas, Panayiotis; Ah-Peng, Claudine; Attorre, Fabio; Birks, H. John B.; Borges, Paulo A. V.; Cardoso, Pedro; Chou, Chang-Hung; De Sanctis, Michele; Sequeira, Miguel M.; Duarte, Maria C.; Elias, Rui B.; Fernández-Palacios, José María; Gabriel, Rosalina; Gereau, Roy E.; Gillespie, Rosemary G.; Greimler, Josef; Harter, David E. V.; Huang, Tsurng-Juhn; Irl, Severin D. H.; Jeanmonod, Daniel; Jentsch, Anke; Jump, Alistair S.; Kueffer, Christoph; Nogué, Sandra; Otto, Rüdiger; Price, Jonathan; Romeiras, Maria M.; Strasberg, Dominique; Stuessy, Tod; Svenning, Jens-Christian; Vetaas, Ole R.; Beierkuhnlein, CarlAIM: Higher-elevation areas on islands and continental mountains tend to be separated by longer distances, predicting higher endemism at higher elevations; our study is the first to test the generality of the predicted pattern. We also compare it empirically with contrasting expectations from hypotheses invoking higher speciation with area, temperature and species richness. Location Thirty-two insular and 18 continental elevational gradients from around the world. Methods We compiled entire floras with elevation-specific occurrence information, and calculated the proportion of native species that are endemic (‘percent endemism’) in 100-m bands, for each of the 50 elevational gradients. Using generalized linear models, we tested the relationships between percent endemism and elevation, isolation, temperature, area and species richness. RESULTS: Percent endemism consistently increased monotonically with elevation, globally. This was independent of richness–elevation relationships, which had varying shapes but decreased with elevation at high elevations. The endemism–elevation relationships were consistent with isolation-related predictions, but inconsistent with hypotheses related to area, richness and temperature. Main conclusions Higher per-species speciation rates caused by increasing isolation with elevation are the most plausible and parsimonious explanation for the globally consistent pattern of higher endemism at higher elevations that we identify. We suggest that topography-driven isolation increases speciation rates in mountainous areas, across all elevations and increasingly towards the equator. If so, it represents a mechanism that may contribute to generating latitudinal diversity gradients in a way that is consistent with both present-day and palaeontological evidence.