Numerous hypotheses have been proposed to explain the dynamics in abundance of individual species, how species interact, how communities assemble, and how interactions between biotic and abiotic processes shape ecosystem stability. Many if not most of these hypotheses find some degree of support, but often only within relatively narrow spatial and temporal ranges. This is because conditions vary over time and from place to place, and so the strength and extent of processes that were the focus of a given a hypothesis become altered by other forces. Ecologists have confronted variability from two perspectives; conceptual and statistical. Conceptually, spatial and temporal variability are now recognized as being scale dependent and hierarchical. Statistically, there are many models that ecologists readily use that account for the hierarchical and scale-dependence of variability present in many datasets. But linking the two perspectives into a meaningful understanding of what variability means in real systems has been much less successful. For example, it is common to see studies where the fixed effects of a generalized linear mixed model are reported, but very often random effects are completely ignored or, at best, given scant attention. The likelihood of this being a significant problem increases greatly in what are rapidly becoming more common studies that utilize datasets spanning long temporal and/or large spatial scales, or when extreme and often unpredictable events (gray and black swans) occur.
Scientists in the environmental field have done a very good job of accounting for variability in statistical models but have been less successful understanding what that variability means "on the ground", especially for practical application in conservation and management programs. Linking the conceptual and statistical perspectives of variability can be advanced by first interpreting the results from statistical models relative to the spatial and temporal scales of the data, then clearly addressing what the implications of studies are relative to the hierarchical structure of variability. Our goals in this special issue are to: (1) examine the difference between variability in statistical models and accounting for that variability in actual ecological systems; (2) explore links between the conceptual and statistical perspectives on variability; and (3) address the conditions and scale at which context dependency is a necessary or sufficient framework for interpreting variability in ecological studies. We are particularly interested in addressing how Goals 1 and 2 inform Goal 3. Context dependency has been receiving an increasing amount of attention over the last several decades, but there are consistent and recognizable ecological patterns. This implies that at some point strong, consistent processes override more local or even regional variability. Thus, three specific objectives are to explore: (1) at what spatial scale do local processes start to override larger scale processes (or vice versa); (2) how long do patterns persist; and (3) how long and over what extent do local and larger scale processes interact and, potentially, shift in importance.
We are looking for contributions that represent a wide range of ecological systems, processes, and biological organization (i.e. genetic, population, community, ecosystem, etc.). We are not interested in papers focused just on statistical methods; the literature on that is vast and ever-growing. Rather, we are first interested in how models and statistical analyses are interpreted, especially as it relates to any hierarchical and scale-dependent structure in the study, and then what the implications of the model/analytical results are (i.e. what are the spatial and temporal limits of inference). We are not putting strong conditions on the spatial scale or duration of a study, but we will favor those with either a large spatial extent, long duration or, of course, both of those. We strongly encourage studies that report interactions between different processes, especially those where there are demonstrable shifts in relative importance from one to another. Original, stand-alone studies and meta-analyses are both suitable for the topic.
Numerous hypotheses have been proposed to explain the dynamics in abundance of individual species, how species interact, how communities assemble, and how interactions between biotic and abiotic processes shape ecosystem stability. Many if not most of these hypotheses find some degree of support, but often only within relatively narrow spatial and temporal ranges. This is because conditions vary over time and from place to place, and so the strength and extent of processes that were the focus of a given a hypothesis become altered by other forces. Ecologists have confronted variability from two perspectives; conceptual and statistical. Conceptually, spatial and temporal variability are now recognized as being scale dependent and hierarchical. Statistically, there are many models that ecologists readily use that account for the hierarchical and scale-dependence of variability present in many datasets. But linking the two perspectives into a meaningful understanding of what variability means in real systems has been much less successful. For example, it is common to see studies where the fixed effects of a generalized linear mixed model are reported, but very often random effects are completely ignored or, at best, given scant attention. The likelihood of this being a significant problem increases greatly in what are rapidly becoming more common studies that utilize datasets spanning long temporal and/or large spatial scales, or when extreme and often unpredictable events (gray and black swans) occur.
Scientists in the environmental field have done a very good job of accounting for variability in statistical models but have been less successful understanding what that variability means "on the ground", especially for practical application in conservation and management programs. Linking the conceptual and statistical perspectives of variability can be advanced by first interpreting the results from statistical models relative to the spatial and temporal scales of the data, then clearly addressing what the implications of studies are relative to the hierarchical structure of variability. Our goals in this special issue are to: (1) examine the difference between variability in statistical models and accounting for that variability in actual ecological systems; (2) explore links between the conceptual and statistical perspectives on variability; and (3) address the conditions and scale at which context dependency is a necessary or sufficient framework for interpreting variability in ecological studies. We are particularly interested in addressing how Goals 1 and 2 inform Goal 3. Context dependency has been receiving an increasing amount of attention over the last several decades, but there are consistent and recognizable ecological patterns. This implies that at some point strong, consistent processes override more local or even regional variability. Thus, three specific objectives are to explore: (1) at what spatial scale do local processes start to override larger scale processes (or vice versa); (2) how long do patterns persist; and (3) how long and over what extent do local and larger scale processes interact and, potentially, shift in importance.
We are looking for contributions that represent a wide range of ecological systems, processes, and biological organization (i.e. genetic, population, community, ecosystem, etc.). We are not interested in papers focused just on statistical methods; the literature on that is vast and ever-growing. Rather, we are first interested in how models and statistical analyses are interpreted, especially as it relates to any hierarchical and scale-dependent structure in the study, and then what the implications of the model/analytical results are (i.e. what are the spatial and temporal limits of inference). We are not putting strong conditions on the spatial scale or duration of a study, but we will favor those with either a large spatial extent, long duration or, of course, both of those. We strongly encourage studies that report interactions between different processes, especially those where there are demonstrable shifts in relative importance from one to another. Original, stand-alone studies and meta-analyses are both suitable for the topic.