Tyler Wagner

Cross‐scale interactions: quantifying multi‐scaled cause–effect relationships in macrosystems

Ecologists are increasingly discovering that ecological processes are made up of components that are multi-scaled in space and time. Some of the most complex of these processes are cross-scale interactions (CSIs), which occur when components interact across scales. When undetected, such interactions may cause errors in extrapolation from one region to another. CSIs, particularly those that include a regional scaled component, have not been systematically investigated or even reported because of the challenges of acquiring data at sufficiently broad spatial extents. We present an approach for quantifying CSIs and apply it to a case study investigating one such interaction, between local and regional scaled land-use drivers of lake phosphorus. Ultimately, our approach for investigating CSIs can serve as a basis for efforts to understand a wide variety of multi-scaled problems such as climate change, land-use/land-cover change, and invasive species.

Accounting for Multilevel Data Structures in Fisheries Data using Mixed Models

Multilevel data structures are those that have a hierarchical structure, in which response variables are measured at the lowest level of the hierarchy and modeled as a function of predictor variables measured at that level and higher levels of the hierarchy. For example, a multilevel data structure may consist of measurements taken on individual fish (lower level) that are nested within lakes or streams (higher level). Multilevel data structures are a common feature in fisheries research. We provide simulated fisheries data examples, similar in structure to other published studies, to illustrate the application of multilevel models and discuss how hypothesis testing and inferences can be incorrect if multilevel data structures are ignored. Ignoring multilevel data structures has implications for the use of commonly-used ordinary least squares (OLS) approaches to test hypotheses and to make inferences. Multilevel models are an alternate approach that circumvents problems associated with traditional OLS methods and allows valid inferences to be made.

Using Landscape Limnology to Classify Freshwater Ecosystems for Multi-ecosystem Management and Conservation

Governmental entities are responsible for managing and conserving large numbers of lake, river, and wetland ecosystems that can be addressed only rarely on a case-by-case basis. We present a system for predictive classification modeling, grounded in the theoretical foundation of landscape limnology, that creates a tractable number of ecosystem classes to which management actions may be tailored. We demonstrate our system by applying two types of predictive classification modeling approaches to develop nutrient criteria for eutrophication management in 1998 north temperate lakes. Our predictive classification system promotes the effective management of multiple ecosystems across broad geographic scales by explicitly connecting management and conservation goals to the classification modeling approach, considering multiple spatial scales as drivers of ecosystem dynamics, and acknowledging the hierarchical structure of freshwater ecosystems. Such a system is critical for adaptive management of complex mosaics of freshwater ecosystems and for balancing competing needs for ecosystem services in a changing world.

Buruli ulcer disease prevalence in Benin, West Africa: associations with land use/cover and the identification of disease clusters

BackgroundBuruli ulcer (BU) disease, caused by infection with the environmental mycobacterium M. ulcerans, is an emerging infectious disease in many tropical and sub-tropical countries. Although vectors and modes of transmission remain unknown, it is hypothesized that the transmission of BU disease is associated with human activities in or around aquatic environments, and that characteristics of the landscape (e.g., land use/cover) play a role in mediating BU disease. Several studies performed at relatively small spatial scales (e.g., within a single village or region of a country) support these hypotheses; however, if BU disease is associated with land use/cover characteristics, either through spatial constraints on vector-host dynamics or by mediating human activities, then large-scale (i.e., country-wide) associations should also emerge. The objectives of this study were to (1) investigate associations between BU disease prevalence in villages in Benin, West Africa and surrounding land use/cover patterns and other map-based characteristics, and (2) identify areas with greater and lower than expected prevalence rates (i.e., disease clusters) to assist with the development of prevention and control programs.ResultsOur landscape-based models identified low elevation, rural villages surrounded by forest land cover, and located in drainage basins with variable wetness patterns as being associated with higher BU disease prevalence rates. We also identified five spatial disease clusters. Three of the five clusters contained villages with greater than expected prevalence rates and two clusters contained villages with lower than expected prevalence rates. Those villages with greater than expected BU disease prevalence rates spanned a fairly narrow region of south-central Benin.ConclusionOur analyses suggest that interactions between natural land cover and human alterations to the landscape likely play a role in the dynamics of BU disease. For example, urbanization, potentially by providing access to protected water sources, may reduce the likelihood of becoming infected with BU disease. Villages located at low elevations may have higher BU disease prevalence rates due to their close spatial proximity to high risk environments. In addition, forest land cover and drainage basins with variable wetness patterns may be important for providing suitable growth conditions for M. ulcerans, influencing the distribution and abundance of vectors, or mediating vector-human interactions. The identification of disease clusters in this study provides direction for future research aimed at better understanding these and other environmental and social determinants involved in BU disease outbreaks.

A Landscape-based Model for Predicting Mycobacterium ulcerans Infection (Buruli Ulcer Disease) Presence in Benin, West Africa

Mycobacterium ulcerans infection (Buruli ulcer [BU] disease) is an emerging tropical disease that causes severe morbidity in many communities, especially those in close proximity to aquatic environments. Research and control efforts are severely hampered by the paucity of data regarding the ecology of this disease; for example, the vectors and modes of transmission remain unknown. It is hypothesized that BU presence is associated with altered landscapes that perturb aquatic ecosystems; however, this has yet to be quantified over large spatial scales. We quantified relationships between land use/land cover (LULC) characteristics surrounding individual villages and BU presence in Benin, West Africa. We also examined the effects of other village-level characteristics which we hypothesized to affect BU presence, such as village distance to the nearest river. We found that as the percent urban land use in a 50-km buffer surrounding a village increased, the probability of BU presence decreased. Conversely, as the percent agricultural land use in a 20-km buffer surrounding a village increased, the probability of BU presence increased. Landscape-based models had predictive ability when predicting BU presence using validation data sets from Benin and Ghana, West Africa. Our analyses suggest that relatively small amounts of urbanization are associated with a decrease in the probability of BU presence, and we hypothesize that this is due to the increased availability of pumped water in urban environments. Our models provide an initial approach to predicting the probability of BU presence over large spatial scales in Benin and Ghana, using readily available land use data.

Approaches to advance scientific understanding of macrosystems ecology

The emergence of macrosystems ecology (MSE), which focuses on regional- to continental-scale ecological patterns and processes, builds upon a history of long-term and broad-scale studies in ecology. Scientists face the difficulty of integrating the many elements that make up macrosystems, which consist of hierarchical processes at interacting spatial and temporal scales. Researchers must also identify the most relevant scales and variables to be considered, the required data resources, and the appropriate study design to provide the proper inferences. The large volumes of multi-thematic data often associated with macrosystem studies typically require validation, standardization, and assimilation. Finally, analytical approaches need to describe how cross-scale and hierarchical dynamics and interactions relate to macroscale phenomena. Here, we elaborate on some key methodological challenges of MSE research and discuss existing and novel approaches to meet them.

Effects of Land Use on Lake Nutrients: The Importance of Scale, Hydrologic Connectivity, and Region

Catchment land uses, particularly agriculture and urban uses, have long been recognized as major drivers of nutrient concentrations in surface waters. However, few simple models have been developed that relate the amount of catchment land use to downstream freshwater nutrients. Nor are existing models applicable to large numbers of freshwaters across broad spatial extents such as regions or continents. This research aims to increase model performance by exploring three factors that affect the relationship between land use and downstream nutrients in freshwater: the spatial extent for measuring land use, hydrologic connectivity, and the regional differences in both the amount of nutrients and effects of land use on them. We quantified the effects of these three factors that relate land use to lake total phosphorus (TP) and total nitrogen (TN) in 346 north temperate lakes in 7 regions in Michigan, USA. We used a linear mixed modeling framework to examine the importance of spatial extent, lake hydrologic class, and region on models with individual lake nutrients as the response variable, and individual land use types as the predictor variables. Our modeling approach was chosen to avoid problems of multi-collinearity among predictor variables and a lack of independence of lakes within regions, both of which are common problems in broad-scale analyses of freshwaters. We found that all three factors influence land use-lake nutrient relationships. The strongest evidence was for the effect of lake hydrologic connectivity, followed by region, and finally, the spatial extent of land use measurements. Incorporating these three factors into relatively simple models of land use effects on lake nutrients should help to improve predictions and understanding of land use-lake nutrient interactions at broad scales.

Long-Term Citizen-Collected Data Reveal Geographical Patterns and Temporal Trends in Lake Water Clarity

We compiled a lake-water clarity database using publically available, citizen volunteer observations made between 1938 and 2012 across eight states in the Upper Midwest, USA. Our objectives were to determine (1) whether temporal trends in lake-water clarity existed across this large geographic area and (2) whether trends were related to the lake-specific characteristics of latitude, lake size, or time period the lake was monitored. Our database consisted of >140,000 individual Secchi observations from 3,251 lakes that we summarized per lake-year, resulting in 21,020 summer averages. Using Bayesian hierarchical modeling, we found approximately a 1% per year increase in water clarity (quantified as Secchi depth) for the entire population of lakes. On an individual lake basis, 7% of lakes showed increased water clarity and 4% showed decreased clarity. Trend direction and strength were related to latitude and median sample date. Lakes in the southern part of our study-region had lower average annual summer water clarity, more negative long-term trends, and greater inter-annual variability in water clarity compared to northern lakes. Increasing trends were strongest for lakes with median sample dates earlier in the period of record (1938–2012). Our ability to identify specific mechanisms for these trends is currently hampered by the lack of a large, multi-thematic database of variables that drive water clarity (e.g., climate, land use/cover). Our results demonstrate, however, that citizen science can provide the critical monitoring data needed to address environmental questions at large spatial and long temporal scales. Collaborations among citizens, research scientists, and government agencies may be important for developing the data sources and analytical tools necessary to move toward an understanding of the factors influencing macro-scale patterns such as those shown here for lake water clarity.

Can Habitat Alteration and Spring Angling Explain Largemouth Bass Nest Success

Abstract Largemouth bass Micropterus salmoides nest in shallow littoral areas, making them vulnerable to negative effects of habitat alteration due to development of lake shorelines and fishing during the spring nesting period. For instance, alteration of shorelines may reduce the quality and abundance of nesting habitat, and the high visibility of nests and the aggressive guarding behavior of nesting males increase their vulnerability to fishing. In 2004, we monitored nest distribution and success and quantified local nest habitat features, lakewide angler effort, and lakeshore development patterns in five Michigan lakes to determine the extent to which habitat alteration and fishing limit the number of nests that produce swim-up fry. Lakes spanned a range of lakeshore dwelling densities (8–22 dwellings/km), allowing us to determine the extent to which nest success varies within and among lakes due to local (e.g., substrate and cover) and lakewide (e.g., dwellings/km and fishing effort) factors. Surprisi...

Detecting Temporal Trends in Freshwater Fisheries Surveys: Statistical Power and the Important Linkages between Management Questions and Monitoring Objectives

ABSTRACT Monitoring to detect temporal trends in biological and habitat indices is a critical component of fisheries management. Thus, it is important that management objectives are linked to monitoring objectives. This linkage requires a definition of what constitutes a management-relevant “temporal trend.” It is also important to develop expectations for the amount of time required to detect a trend (i.e., statistical power) and for choosing an appropriate statistical model for analysis. We provide an overview of temporal trends commonly encountered in fisheries management, review published studies that evaluated statistical power of long-term trend detection, and illustrate dynamic linear models in a Bayesian context, as an additional analytical approach focused on shorter term change. We show that monitoring programs generally have low statistical power for detecting linear temporal trends and argue that often management should be focused on different definitions of trends, some of which can be better...