Biodiversity Dynamics on Islands: Explicitly Accounting for Causality in Mechanistic Models

Ludwig Leidinger, Juliano Cabral
2017 Diversity  
Island biogeography remains a popular topic in ecology and has gained renewed interest due to recent theoretical development. As experimental investigation of the theory is difficult to carry out, mechanistic simulation models provide useful alternatives. Several eco-evolutionary mechanisms have been identified to affect island biodiversity, but integrating more than a few of these processes into models remains a challenge. To get an overview of what processes mechanistic island models have
more » ... and models have integrated so far and what conclusions they came to, we conducted an exhaustive literature review of studies featuring island-specific mechanistic models. This was done using an extensive systematic literature search with subsequent manual filtering. We obtained a list of 28 studies containing mechanistic island models, out of 647 total hits. Mechanistic island models differ greatly in their integrated processes and computational structure. Their individual findings range from theoretical (such as humped-shaped extinction rates for oceanic islands) to system-specific dynamics (e.g., equilibrium and non-equilibrium dynamics for Galápagos' birds). However, most models so far only integrate theories and processes pair-wise, while focusing on hypothetical systems. Trophic interactions and explicit micro-evolution are largely underrepresented in models. We expect future models to continue integrating processes, thus promoting the full appraisal of biodiversity dynamics. Diversity 2017, 9, 30 2 of 17 topographic complexity, facilitating species radiations. The last stage sees further erosion of the island and ultimately atoll formation, resulting in increased numbers of locally extinct species. While biodiversity theories, in particular the ETIB, were developed and tested experimentally, nowadays, conservation considerations render it unfeasible to conduct experiments of a scale comparable to that of the classical experiment by Simberloff and Wilson [9] . Moreover, Borregaard et al. [10] and Whittaker and Fernández-Palacios [11] among others point out that issues such as anthropogenic disturbances and above all the long timescales relevant when considering evolution make studying phenomena affecting the dynamics and maintenance of island biodiversity difficult and complex. As a general consequence of these limitations, many studies investigating species diversity patterns on islands can only draw conclusions of a correlative nature, often by fitting regression models [12] . This has helped in identifying many possible drivers of biodiversity distribution [13, 14] . However, the underlying causal relationships remain generally debatable, considering that the representation of causality in correlative models is limited. Therefore, definitive statements on evolution and on the impact of geological processes based solely on field data are generally inconclusive. This holds true particularly for islands, due to the destructive nature of geological phenomena, such as volcanism or erosion. One way to overcome this data limitation is to employ a space-for-time substitution using islands of different ontogenic stages as snapshots in time [15] . However, archipelagic dynamics, such as geomorphological changes in island size, connectivity and heterogeneity, as well as island hopping, might have confounding effects on empirical data [16] . Another possible alternative, still involving empirical testing, is using smaller scaled model systems such as microbiota [17, 18] . Yet, for studying biogeography dynamics of longer living organisms, process-explicit models remain the most viable option to date. With the advances in technology and scientific knowledge, process-explicit simulation models have become even more feasible, both in implementation, as well as conducting. In principle, process-explicit (or mechanistic) models reflect hypotheses about how mechanisms interact to produce observed patterns. In this context, we define mechanisms (or processes) as actions that causally link elements in a model. The produced patterns are thus direct results of the interplay between integrated processes. The advantage of these models lies in their flexibility. Such flexibility can be characterized in two ways: (1) through variation of model parameter values and thus their impact [19] ; and (2) switching off particular processes or varying the model structure, e.g., the order of processes [20] . The combination of both allows for a multitude of possible alternative simulation arenas or scenarios and enables us to test the robustness, but also the importance of the respective mechanistic assumptions, while maintaining complete mechanistic control of the experiments. To get an overview of which processes and drivers have been considered thus far in mechanistic island models, what patterns they produce and what they found out about their systems, we conducted an extensive literature review. We systematically searched for aspects, like for example, the theories models are based on, whether they are stochastic or deterministic, spatially implicit or explicit and what focal level they consider. The scope of our review also entails any model explicitly assessing island biogeography theories or assumptions. In contrast to the recent review by Borregaard et al. [10], we only consider models that specifically feature causal mechanisms, detached from the scope of the GDM. Our review is similar to Cabral et al. [21] , but we focus specifically on island models and perform an exhaustive, systematic literature search.
doi:10.3390/d9030030 fatcat:iz2lh5g6wnfbtb2p6pgoeqoqyi