Browsing by Author "Borregaard, Michael K."
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- The gambin model provides a superior fit to species abundance distributions with a single free parameter : evidence, implementation and interpretationPublication . Matthews, Thomas J.; Borregaard, Michael K.; Ugland, Karl I.; Borges, Paulo A. V.; Rigal, François; Cardoso, Pedro; Whittaker, Robert J.The species abundance distribution (SAD) has been a central focus of community ecology for over fifty years, and is currently the subject of widespread renewed interest. The gambin model has recently been proposed as a model that provides a superior fit to commonly preferred SAD models. It has also been argued that the model's single parameter (α) presents a potentially informative ecological diversity metric, because it summarises the shape of the SAD in a single number. Despite this potential, few empirical tests of the model have been undertaken, perhaps because the necessary methods and software for fitting the model have not existed. Here, we derive a maximum likelihood method to fit the model, and use it to undertake a comprehensive comparative analysis of the fit of the gambin model. The functions and computational code to fit the model are incorporated in a newly developed free-to-download R package (gambin). We test the gambin model using a variety of datasets and compare the fit of the gambin model to fits obtained using the Poisson lognormal, logseries and zero-sum multinomial distributions. We found that gambin almost universally provided a better fit to the data and that the fit was consistent for a variety of sample grain sizes. We demonstrate how α can be used to differentiate intelligibly between community structures of Azorean arthropods sampled in different land use types. We conclude that gambin presents a flexible model capable of fitting a wide variety of observed SAD data, while providing a useful index of SAD form in its single fitted parameter. As such, gambin has wide potential applicability in the study of SADs, and ecology more generally.
- 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.