Recipient Organization
MICHIGAN STATE UNIV
(N/A)
EAST LANSING,MI 48824
Performing Department
Fisheries & Wildlife
Non Technical Summary
The science of ecology has long sought to explain why particular species are located in particular places on Earth, and why some regions contain many more species than other regions. As humans have altered the planet, ecologists have placed more and more emphasis on investigating the services that biodiversity (Earth's living species) provides to humans. Although the importance of biodiversity to human well-being is becoming very clearly established, management decisions that are relevant to biodiversity are often constrained by (1) lack of detailed information, across broad geographic regions, regarding the distribution of species, (2) incomplete understanding of the natural and human-determined features that most strongly affect biodiversity, and (3) incomplete assessment of the success of conservation efforts at safeguarding biodiversity. The 3 goals of this research project are designed to reduce these 3 constraints.This collaborative research integrates geographic context (GIS-based data), lake biological monitoring data, and spatial patterns both of anthropogenic stressors and conservation/management efforts to quantify the complex relationships and interactions, ranging multiple spatial scales, among them. In short, we are addressing the fundamental question: What drivers and interactions, at multiple spatial scales, contribute to spatial variability of lake fish community diversity? Our analyses will document critically important patterns of biodiversity, identify factors and spatial scales most highly associated with biodiversity, and evaluate the effectiveness of conservation and management programs to safeguard biodiversity.
Animal Health Component
50%
Research Effort Categories
Basic
30%
Applied
50%
Developmental
20%
Goals / Objectives
The goals of this study are to:1) Integrate multiple spatially-explicit data sources (including lake ecological setting, lake water chemistry and biological monitoring, and fisheries conservation and management actions) to address Goals 2 and 3 and facilitate lake research,2) Determine how spatial variation in ecological setting affects diversity within and among lake fish communities, and3) Evaluate effects of conservation (i.e., restoring hydrologic connectivity) and management (i.e., sportfish stocking) on fish communities and fish population demographics.
Project Methods
Goal 1: Integrate multiple spatially-explicit data sources (including lake ecological context, lake water chemistry and biological monitoring, and fisheries conservation and management actions) to address Goals 2 and 3 and facilitate lake researchThis research builds on previous accomplishments of my collaborators and I in terms of integrating ecological context (hydrogeomorphic) data, lake water chemistry data, and surface water connectivity data (Soranno et al. 2015). In this next phase of research, we are adding biological and conservation/management data to the existing lake database (LaGOS). Specifically, we will compile a macroscale lake fish database (FINS; Fish from INland lakeS) at multiple levels of biological organization and spanning multiple US states. Although macroscale studies of lake fishes are relatively rare, fish data are more widely collected than are data for other lake biota, both in terms of number of systems sampled and data at various levels of biological organization. A transboundary fish database is desperately needed by ecologists and conservationists across the US to facilitate new macroscale research in freshwaters (Bonar 2009). The creation of FINS will fill these needs. Although other fish-related databases exist (e.g., VertNet; http://vertnet.org/, FishBase; http://www.fishbase.org/home.htm), they do not contain the detailed individual-level data required to make inferences about the effects of global change on multiple levels of biological organization, nor are they linked to ecological context data. By creating FINS, we will capitalize on the vital ecological-context information available in existing databases (e.g., LAGOS-US, an NSF-funded project; Patricia Soranno, pers. comm.), while extending such databases into the biotic realm. Collectively, my collaborators and I already have fish data in-hand for several thousand lakes located in multiple states (e.g., Pennsylvania, Minnesota, Wisconsin, and Michigan).Because lake fishes are influenced by fisheries conservation/management activities such as habitat rehabilitation and stocking, we must consider these factors when integrating lake fish data into applications of macro-ecological understanding. State-specific records of fisheries management practices exist in the form of stocking records, and past and current fishing regulations, and site-specific habitat rehabilitation projects. However, these vital information sources also have not been compiled for lakes across broad geographic scales. Therefore, we will work to incorporate these data, as available from state agencies, into FINS. For example, we will compile the number, size, and species of fish stocked through time in individual lakes.Goal 2: Determine how spatial variation in ecological setting affects diversity within and among lake fish communitiesUnderstanding the multi-scaled relationship between lake ecological setting and fish community diversity requires quantifying spatial patterns of diversity's component parts (i.e., ?- and β-diversity) across a large spatial extent, and quantifying the relationships between those diversity components and drivers acting at multiple scales. There have been many debates in the ecology literature about the best way to estimate β-diversity (e.g., Chao et al. 2012; Socolar et al. 2016). We will use the pairwise β-diversity approach (Marion et al. 2017), because this approach produces unbiased indices of β-diversity when sample size differs among regions. We will calculate pairwise dissimilarity indices between all pairs of sampled lakes within a region after assembling a species presence-absence matrix. ?-diversity will be quantified as species richness for each lake. Recently my lab established a statistical protocol using rarefaction techniques that evaluates if fish survey sampling effort was sufficient to reliably characterize species richness. We will continue to apply these analyses to fish survey data that we receive from fisheries agencies as we build FINS.To quantify the multi-scaled ecological drivers and their interactions that explain variation in fish diversity, we will conduct CART analyses (De'ath and Fabricius 2000) to quantify filter architecture and identify important drivers and interactions (e.g., Figure 1). For ?-diversity, CARTs will include isolation- (e.g., surface water connectivity metrics, stocking) and extinction-based (e.g., lake morphometry and climate metrics) drivers, as well as region membership, allowing us to identify differences in the relative importance of particular isolation- and extinction-drivers among regions or across scales (CSIs). For β-diversity (the average pairwise dissimilarity value per region), CARTs will include regional-scale ecological drivers (e.g., topographic relief throughout the region, climate, prevalence of dams) to understand why some regions contain lakes with very similar fish assemblages, whereas other regions contain lakes that vary substantially in their fish assemblages.Goal 3: Evaluate effects of conservation (i.e., restoring hydrologic connectivity) and management (i.e., sportfish stocking) on fish communities and fish population demographics.This objective represents the newest component of my research program. We will gain insights into this objective through the CART analysis conducted for Goal 2. For example, we will learn if regions with restored hydrological connectivity differ from other regions in terms of lake species richness and diversity. We will also learn if prevalence of past sportfish stocking explains significant amounts of variation in fish species richness among lakes. Based on the findings from Goal 2, we will initiate analyses that address Goal 3 more specifically. To do so, we will foster discussions and collaborations with individuals with statistical modeling expertise. Additionally, we will work with state management agencies, to better identify their questions and goals related to their conservation and management efforts. We will then finetune our analytical plans to better align with the manager's questions and goals.