1. Food webs are a powerful way to represent the diversity, structure, and function of ecological systems. However, the accurate description of food webs requires significant effort in time and resources, limiting their widespread use in ecological studies. Newly published methods allow for the inference of feeding interactions using proxy variables. Here, we compare the accuracy of two recently described methods, as well as describe a composite model of the two, for the inference of feeding interactions using a large, well-described dataset.2. Both niche and neutral processes are involved in determining whether or not two species will form a feeding link in communities. Three different models for determining niche constraints of feeding interactions are compared, and all three models are extended by incorporating neutral processes, based on relative abundances.The three models compared here infer niche processes through (a) phylogenetic relationships, (b) local species trait distributions (e.g., body size), and (c) a composite of phylogeny and local traits.3. We show that all three methods perform well at predicting individual species interactions, and that these individual predictions scale up to the network level, resulting in food web structure of inferred networks being similar to their empirical counterparts. 4.Our results indicate that inferring food web structure using phylogenies can be an efficient way of getting summary webs with minimal data, and offers a conservative test of changes in food web structure, particularly when there is low species turnover between sites. Inferences made using traits require more data, but allows for greater understanding of the mechanisms underlying trophic interactions. A composite model of the two methods provides a framework for investigating the importance of how phylogeny, trait distributions, and relative abundances, affect species interactions, and network structure. K E Y W O R D S body size, food web inference, food web structure, neutral theory, niche model, traitmatching, WebBuilder | 357 Methods in Ecology and Evoluঞon POMERANZ Et Al.
Parameters describing the negative relationship between abundance and body size within ecological communities provide a summary of many important biological processes. While it is considered to be one of the few consistent patterns in ecology, spatiotemporal variation of this relationship across continental scale temperature gradients is unknown. Using a database of stream communities collected across North America (18-68°N latitude, −4 to 25°C mean annual air temperature) over 3 years, we constructed 160 individual size distribution (ISD) relationships (i.e. abundance size spectra). The exponent parameter describing ISD's decreased (became steeper) with increasing mean annual temperature, with median slopes varying by ~0.2 units across the 29°C temperature gradient. In addition, total community biomass increased with increasing temperatures, contrary with theoretical predictions. Our study suggests conservation of ISD relationships in streams across broad natural environmental gradients. This supports the emerging use of size-spectra deviations as indicators of fundamental changes to the structure and function of ecological communities.
Sampling emergent aquatic insects is of interest to many freshwater ecologists. Many quantitative emergence traps require the use of aspiration for collection. However, aspiration is infeasible in studies with large amounts of replication that is often required in large biomonitoring projects. We designed an economic, collapsible pyramid-shaped floating emergence trap with an external collection bottle that avoids the need for aspiration. This design was compared experimentally to a design of similar dimensions that relied on aspiration to ensure comparable results. The pyramid-shaped design captured twice as many total emerging insects. When a preservative was used in bottle collectors, >95% of the emergent abundance was collected in the bottle. When no preservative was used, >81% of the total insects were collected from the bottle. In addition to capturing fewer emergent insects, the traps that required aspiration took significantly longer to sample. Large studies and studies sampling remote locations could benefit from the economical construction, speed of sampling, and capture efficiency.
Bayesian data analysis is increasingly used in ecology, but prior specification remains focused on choosing non-informative priors (e.g., flat or vague priors). One barrier to choosing more informative priors is that priors must be specified on model parameters (e.g., intercepts, slopes, and sigmas), but prior knowledge often exists on the level of the response variable. This is particularly true for common models in ecology, like generalized linear mixed models that have a link function and potentially dozens of parameters, each of which needs a prior distribution. We suggest that this difficulty can be overcome by simulating from the prior predictive distribution and visualizing the results on the scale of the response variable. In doing so, some common choices for non-informative priors on parameters can easily be seen to produce biologically impossible values of response variables. Such implications of prior choices are difficult to foresee without visualization. We demonstrate a workflow for prior selection using simulation and visualization with two ecological examples (predator-prey body sizes and spider responses to food competition). This approach is not new, but its adoption by ecologists will help to better incorporate prior information in ecological models, thereby maximizing one of the benefits of Bayesian data analysis.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.