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CURATOR
A pinboard by
Andrew Du

Postdoctoral Scholar, The University of Chicago

PINBOARD SUMMARY

How do spatial, temporal, and taxonomic scales affect how we understand ecological communities?

The most pressing questions in ecology today are concerned with large-scale phenomena, such as preserving global biodiversity and understanding the impacts of protracted climate change. These patterns are difficult to study, however, because the requisite observational scales are orders of magnitude larger than that of human experience. To aid in this endeavor, ecologists are starting to turn to the fossil record, which has the time depth necessary to see how ecological communities respond over millennia to past climate change events. However, the fossil record is preserved at coarse temporal resolutions, and it is difficult to get records finer than thousands of years (at least for large mammals). Moreover, it can be challenging to identify fossil specimens to the species level, so many are often analyzed at the genus level or above. These are all significant issues because patterns and drivers of biodiversity in ecological communities change as one increases the amount of investigated space or time (i.e., spatial or temporal scale) or the level of taxonomic resolution (i.e., taxonomic scale). Therefore, there is a large scale gap between our understanding of small-scale modern ecological communities, where most of our ecological knowledge and theory is based, versus our interpretation of coarse-scale fossil communities. My research seeks to understand how community patterns change as one increases spatial, temporal, and taxonomic scales. To this end, I study the landscape skeletal assemblages of modern large mammals in Amboseli National Park, southern Kenya. These data are ideal for my research questions because skeletal data have been collected over 300 km2 and for over 50 years. Such large spatial and temporal data extents allow me to see how actual community patterns change as the spatial and temporal scales of analysis change. I can also coarsen the species-level data to genus-level, family-level, and beyond to understand the influence of taxonomic scale on community patterns. Armed with this knowledge, I then analyze the fossil communities of the Koobi Fora Formation located in northern Kenya to see if the character of these fossil communities can be predicted from scaling up the modern Amboseli communities. This research ultimately addresses how comparable modern and fossil ecological data are and if we need to be wary of any pitfalls for using fossil data to understand how climate may affect modern communities today.

5 ITEMS PINNED

Mechanistic simulation models in macroecology and biogeography: state-of-art and prospects

Abstract: Macroecology and biogeography are concerned with understanding biodiversity patterns across space and time. In the past, the two disciplines have addressed this question mainly with correlative approaches, despite frequent calls for more mechanistic explanations. Recent advances in computational power, theoretical understanding, and statistical tools are, however, currently facilitating the development of more system-oriented, mechanistic models. We review these models, identify different model types and theoretical frameworks, compare their processes and properties, and summarize emergent findings. We show that ecological (physiology, demographics, dispersal, biotic interactions) and evolutionary processes, as well as environmental and human-induced drivers, are increasingly modelled mechanistically; and that new insights into biodiversity dynamics emerge from these models. Yet, substantial challenges still lie ahead for this young research field. Among these, we identify scaling, calibration, validation, and balancing complexity as pressing issues. Moreover, particular process combinations are still understudied, and so far models tend to be developed for specific applications. Future work should aim at developing more flexible and modular models that not only allow different ecological theories to be expressed and contrasted, but which are also built for tight integration with all macroecological data sources. Moving the field towards such a ‘systems macroecology’ will test and improve our understanding of the causal pathways through which eco-evolutionary processes create diversity patterns across spatial and temporal scales.

Pub.: 09 Dec '16, Pinned: 15 Feb '18

The relationship between the spatial scaling of biodiversity and ecosystem stability

Abstract: Ecosystem stability and its link with biodiversity have mainly been studied at the local scale. Here we present a simple theoretical model to address the joint dependence of diversity and stability on spatial scale, from local to continental.The notion of stability we use is based on the temporal variability of an ecosystem-level property, such as primary productivity. In this way, our model integrates the well-known species–area relationship (SAR) with a recent proposal to quantify the spatial scaling of stability, called the invariability–area relationship (IAR).We show that the link between the two relationships strongly depends on whether the temporal fluctuations of the ecosystem property of interest are more correlated within than between species. If fluctuations are correlated within species but not between them, then the IAR is strongly constrained by the SAR. If instead individual fluctuations are only correlated by spatial proximity, then the IAR is unrelated to the SAR. We apply these two correlation assumptions to explore the effects of species loss and habitat destruction on stability, and find a rich variety of multi-scale spatial dependencies, with marked differences between the two assumptions.The dependence of ecosystem stability on biodiversity across spatial scales is governed by the spatial decay of correlations within and between species. Our work provides a point of reference for mechanistic models and data analyses. More generally, it illustrates the relevance of macroecology for ecosystem functioning and stability.

Pub.: 04 Jan '18, Pinned: 15 Feb '18