Where do they go when they die?

Food webs and matrices are vital to understanding feeding relationships and ecology. Adjacency matrices can be employed to present the direct relationships between predators and prey; these binary matrices utilize 0’s to denote no direct link and 1’s to denote a direct link. We analyzed a variety of published food webs ranging from pine forests in the United States to tussock grasslands in New Zealand. The food webs varied in number of distinguishable taxa present, functional diversity, climates and habitats. Consequently, we expect that our results are not specific to a given system. The published food webs lack flows from organisms to detritus despite the fact that organisms in these webs consume detritus. This discrepancy leads us to question how the inclusion of flows to detritus influences indirect connectance within large food webs. By including the flows to detritus, the number of indirect paths of length n as well as indirect relationships throughout the systems increased. Null model simulations were compared to detrital models in power series and eigen analysis. Pathway proliferation was found in all simulations with detrital models exhibiting greater potential indirect paths and detritus contributing greatly to energetic cycling by serving as energy storage to dead and decaying organic matter in ecosystems

Identifying and characterizing extrapolation in multivariate response data

Extrapolation is defined as making predictions beyond the range of the data used to estimate a statistical model. In ecological studies, it is not always obvious when and where extrapolation occurs because of the multivariate nature of the data. Previous work on identifying extrapolation has focused on univariate response data, but these methods are not directly applicable to multivariate response data, which are more and more common in ecological investigations. In this paper, we extend previous work that identified extrapolation by applying the predictive variance from the univariate setting to the multivariate case. We illustrate our approach through an analysis of jointly modeled lake nutrients and indicators of algal biomass and water clarity in over 7000 inland lakes from across the Northeast and Mid-west US. In addition, we illustrate novel exploratory approaches for identifying regions of covariate space where extrapolation is more likely to occur using classification and regression trees.Comment: 28 pages, 2 supplementary files, 6 main figures, 2 supplementary figures, 2 supplementary table

Increasing accuracy of lake nutrient predictions in thousands of lakes by leveraging water clarity data

Abstract Aquatic scientists require robust, accurate information about nutrient concentrations and indicators of algal biomass in unsampled lakes in order to understand and predict the effects of global climate and land‐use change. Historically, lake and landscape characteristics have been used as predictor variables in regression models to generate nutrient predictions, but often with significant uncertainty. An alternative approach to improve predictions is to leverage the observed relationship between water clarity and nutrients, which is possible because water clarity is more commonly measured than lake nutrients. We used a joint‐nutrient model that conditioned predictions of total phosphorus, nitrogen, and chlorophyll a on observed water clarity. Our results demonstrated substantial reductions (8–27%; median = 23%) in prediction error when conditioning on water clarity. These models will provide new opportunities for predicting nutrient concentrations of unsampled lakes across broad spatial scales with reduced uncertainty