Interaction Diversity Maintains Resiliency in a Frequently Disturbed Ecosystem

Frequently disturbed ecosystems are characterized by resilience to ecological disturbances. Longleaf pine ecosystems are not only resilient to frequent fire disturbance, but this feature sustains biodiversity. We examined how fire frequency maintains beta diversity of multi-trophic interactions in longleaf pine ecosystems, as this community property provides a measure of functional redundancy of an ecosystem. We found that beta interaction diversity at small local scales is highest in the most frequently burned stands, conferring immediate resiliency to disturbance by fire. Interactions become more specialized and less resilient as fire frequency decreases. Local scale patterns of interaction diversity contribute to broader scale patterns and confer long-term ecosystem resiliency. Such natural disturbances are likely to be important for maintaining regional diversity of interactions for a broad range of ecosystems

Aquatic Community Interaction Diversity and Mosquito Larvae

Mosquitoes comprise a diverse group of small flies (Diptera) in the family Culicidae which includes an estimated 3,600 described species. Colloquially we know mosquitoes as biting insects that pose a threat to humans and domestic animals as important vectors of disease. Although a minority of the described species of mosquitoes are not known as competent disease vectors, many competent vector species are highly common surrounding human habitations. Despite being important flying insects, mosquitoes undergo an entirely aquatic life cycle as developing larvae and pupae. During these developmental stages immature mosquitoes are most vulnerable to predation and competition for resources. Their habitats are highly variable in many factors including size, invertebrate diversity, and spatial heterogeneity. My dissertation research focuses on the larval stages of mosquitoes. The main questions of my research include: 1. What are the important interactions of co-inhabiting invertebrates including predators and competitors, with mosquito larvae? 2. Does environmental heterogeneity in the form of plant complexity influence the structure of invertebrate diversity in aquatic communities? 3. Does interaction diversity affect the abundance of mosquito larvae? My research includes four complimentary approaches to answering these questions. First, I conducted a meta-analysis on the use of natural enemies to control mosquito populations. Second, I developed simulation models to test the effects of plant, herbivore, and enemy diversity, abundance and diet breadths on sampling interaction diversity in artificial communities. Third, I conducted two identical mesocosm experiments with experimental manipulations of plant diversity and structural complexity in order to test the effects of those on aquatic invertebrate diversity andii mosquito abundance. Finally, I measured plant diversity and environmental heterogeneity in unmanipulated aquatic field environments to test the effects of plant diversity and environmental heterogeneity on invertebrate diversity and mosquito abundance. The results from my research show several important relationships between environmental heterogeneity, invertebrate diversity, interaction diversity, and mosquito abundance. 1. Natural enemy groups including predators, competitors, parasites, and pathogens can have important negative effects on mosquitoes. 2. Increased predator and competitor diversities reduce larval mosquito abundance through direct and indirect effects. 3. Plant diversity and environmental heterogeneity have positive effects on community invertebrate diversity. 5. Greater interaction diversity in aquatic systems reduces larval mosquito abundance. These results show the importance of protecting and encouraging biodiversity as components of effective larval mosquito control programs. Careful management of aquatic macroyphyte diversity and environmental heterogeneity will help reduce larval mosquito abundance

Simulated tri-trophic networks reveal complex relationships between species diversity and interaction diversity

<div><p>Most of earth’s biodiversity is comprised of interactions among species, yet it is unclear what causes variation in interaction diversity across space and time. We define interaction diversity as the richness and relative abundance of interactions linking species together at scales from localized, measurable webs to entire ecosystems. Large-scale patterns suggest that two basic components of interaction diversity differ substantially and predictably between different ecosystems: overall taxonomic diversity and host specificity of consumers. Understanding how these factors influence interaction diversity, and quantifying the causes and effects of variation in interaction diversity are important goals for community ecology. While previous studies have examined the effects of sampling bias and consumer specialization on determining patterns of ecological networks, these studies were restricted to two trophic levels and did not incorporate realistic variation in species diversity and consumer diet breadth. Here, we developed a food web model to generate tri-trophic ecological networks, and evaluated specific hypotheses about how the diversity of trophic interactions and species diversity are related under different scenarios of species richness, taxonomic abundance, and consumer diet breadth. We investigated the accumulation of species and interactions and found that interactions accumulate more quickly; thus, the accumulation of novel interactions may require less sampling effort than sampling species in order to get reliable estimates of either type of diversity. Mean consumer diet breadth influenced the correlation between species and interaction diversity significantly more than variation in both species richness and taxonomic abundance. However, this effect of diet breadth on interaction diversity is conditional on the number of observed interactions included in the models. The results presented here will help develop realistic predictions of the relationships between consumer diet breadth, interaction diversity, and species diversity within multi-trophic communities, which is critical for the conservation of biodiversity in this period of accelerated global change.</p></div

Scatterplots displaying the relationship between the strength of each path coefficient and the number of sampled interactions included in the path analysis (Fig 5), with the exception of paths associated with connectance.

<p>The strength of the path coefficient is shown on the y-axis and number of observed interactions included in the model is shown on the x-axis. The solid line represents outcome of linear or polynomial regressions. Path coefficients used in these analyses were significant (P < 0.05).</p

A randomly selected tri-tropic network produced from one of the 1000 simulations.

<p>Each black bar is a node representing a unique species, while the grey bars are edges connecting the black bars and represent observed interactions between those two species. Green sections within some of the black bars represent individuals within that particular species that were present in the community, but not involved in trophic interactions (e.g., plants without herbivores). The width of each edge and node within the network denotes the abundance of sampled interactions or species. Only species that were sampled are shown in this network. Numbers above each node denote the species identification number from that particular simulation.</p

Posterior probabilities of: A) mean Chao1 estimates of richness for species and interactions, and B) the mean slope of rarefaction curves for species and interactions.

<p>Interactions are displayed in grey, while species are shown in white. The error bars represent the 95% High Density Intervals (HDI). Mean slopes were acquired by calculating the slope of each rarefaction curve when half of the species or interactions were sampled. Chao1 estimates of richness were acquired using the ‘estimateR’ function in the <i>vegan</i> package in R.</p

A path diagram summarizing the standardized path coefficients across all 1000 local communities (<i>χ</i>² = 3.6, df = 4, P = 0.5; AIC = 36).

<p>Each path was chosen based on <i>a priori</i> hypotheses, and compared to competing models using AIC and <i>χ</i>². Lines ending with an arrow denote positive coefficients, while lines ending with a circle denote negative coefficients. The width of the arrow indicates the relative size of the coefficient.</p