In general, I integrate empirical methods with computational modeling to address how spatial and temporal hetero-geneity influence individual performance, especially in the context of species interactions (e.g. predator-prey interactions). I currently work in two study systems (1: Lake Erie, focusing on piscivore-larval fish-zooplankton interactions, as well as harmful algal blooms, and 2: small ponds, focusing on dragonfly-tadpole interactions) that have some similarities, but also operate at different scales and introduce different key processes. I believe that using a variety of approaches (observational, experimental, computational/mathematical) at a range of organizational levels is useful for developing a coherent and mechanistic understanding of ecological systems. As a result, my research is often interdisciplinary and collaborative. 

 

As with many fishes, the number of larval walleye in Lake Erie that survive to the juvenile stage (at which time re-cruitment to the fishery is set) varies considerably from year to year. My research in this area is focused on identifying how physical, chemical, and biological processes interact to influence walleye early life history and re-cruitment success. 

I have worked with a number of collaborators to develop an individual-based, coupled physical-biological model (ICPBM) for this research. The ICPBM includes a hydrodynamic model that simulates the physical conditions in the lake, a lower food-web model that simulates the biogeochemistry of the lake and the distribution and abundance of planktonic prey, and an individual-based model of larval walleye that uses bioenergetic and predator-prey functions to simulate walleye growth, development, and survival. As part of this process, I have been using field-collected data to set model parameters and initial conditions, as well as to test model predictions. 

 

I have been primarily exploring 1) how water warming, wind-driven water circulation, and allochthonous inputs from rivers can generate spatiotemporal heterogeneity in habitat quality (e.g. zooplankton prey availability, temperature, turbidity) for larvae, 2) how hydrodynamic circulation influences larval dispersal, and in particular, whether and for how long larvae encounter high-quality habitat patches, and 3) how the growth, development, and survival rates of larvae to the juvenile stage vary in response to these processes. Additionally, I have used the physi-cal model in conjunction with genetic data (microsatellites) to identify patterns of connectivity and gene flow and improve our understanding of the population structure and origins of juveniles and adults.  

 

I have found ICPBMs to be powerful tools, as I can test for hypothesized effects of biophysical processes both in model runs that simulate conditions actually observed during previous years, as well as in model runs that isolate or magnify key processes (e.g. warming trends, zooplankton density). This latter approach has been particularly useful for identifying the sensitivity of the ecosystem to specific processes. Future work will focus on identifying which biophysical processes must co-occur and at what strengths to permit or block high recruitment, and how their inter-action strengths vary. Additionally, I have begun to incorporate individual behavior into the model to explore how various movement and foraging rules influence the above patterns, especially as larvae grow and become increasingly capable swimmers. 

 

While this research has obvious fisheries management applications, it also will help to advance our basic under-standing of the fish recruitment process under changing ecosystem states. For example, I have begun to use this modeling framework to predict how large freshwater systems might respond to climate change (e.g. water warming, shifts in wind patterns, increased precipitation). Additionally, as physical processes strongly influence many aquatic and terrestrial systems through their effects on dispersal, this research is improving our ability to describe the ecological and evolutionary mechanisms underlying the match-mismatch dynamics of individuals and their optimal habitats. 

 

The Great Lakes ecosystem is severely compromised by elevated anthropogenic nutrient loadings that have substantially degraded water quality and increased the incidence and intensity of harmful algal blooms (HABs). In Lake Erie, sedimentation and nutrient runoff from agricultural fields and livestock are primary contributors of in-creased phosphorus loadings. Through releasing algal toxins such as microcystin and producing hypoxic conditions, HABs degrade many ecosystem services that generate value for the Lake Erie region, including fisheries production, clean and safe drinking water, and recreational activities. 

 

My collaborators and I are exploring the trade-offs between increased costs to agricultural producers of improved nutrient management and the benefits to the Lake Erie region in terms of improved ecosystem services by integrat-ing existing models of agricultural production and watershed flows in the Maumee River watershed, the largest watershed in the Great Lakes region, with the Lake Erie hydrodynamic-lower food web-fish model that I have been developing (see above). This integrated set of models will then be used to trace the impacts of farmer cropping and management decisions on the occurrence and duration of HABs in Lake Erie. Economic valuation techniques will be used to value the economic benefits of improvements in multiple HABs-impacted Lake Erie ecosystem services through nutrient management policies. This integrated modeling framework will first allow us to quantify the net economic outcome of the current lack of nutrient management policies in terms of the benefits and costs to agricul-tural producers, Ohio residents, and Lake Erie visitors and businesses. We will then use this linked model to test the hypotheses that 1) the benefits of improved ecosystem services are greater than potential production losses and nutrient management costs incurred by farmers, and 2) policies that incentivize agricultural producers to reduce nutrient runoff can generate net economic benefits through avoided degradation of ecosystem services impacted by HABs. 

 

How individuals assess and respond to spatiotemporal heterogeneity within their biotic and abiotic environment often fundamentally affects their own condition and their direct and indirect interactions with other species. However, the underlying mechanisms remain poorly understood. In this area, my research aims to identify connections among perception of the environment, physiological response, and resulting phenotypic plasticity and fitness consequences in a model ecological system. 

 

I focus on a predatory larval dragonfly-tadpole prey system for this research. In so doing, I have used a game theo-retic perspective, as a change in one species’ phenotype can change the optimal strategy of the other species. In such a setting, dragonfly predators try to maximize their ability to capture prey while minimizing other costs, whereas tadpole prey try to avoid predation while simultaneously collecting resources. Both need to survive to metamorphosis and beyond. The direct interactions between individual predators and prey, as well as the direct and indirect effects of both species on resources, competitors, and other species in the community, depend on each species’ assessment of the environment and its resulting phenotypic response (e.g. behavior, morphology, life history). 

 

As a doctoral student and postdoctoral researcher, I have focused on elucidating how the information available to both predators and prey drives individual behavior (e.g. space use, movement, activity level), how joint behavior impacts individual performance, population dynamics, and species interactions, and how both behavior and its resultant consequences vary over time and space (i.e. over an individual’s development, over a season, among communities). I have used a variety of approaches in my research and have initiated collaborations with researchers in several disciplines to address my interests. First, I have begun to characterize the chemical cue that tadpoles use to assess predation risk, in addition to beginning to identify how the physical environment affects its distribution and persistence. Concurrently, I have measured the neuroendocrine stress response (e.g., whole-body corticosterone concentration, neuronal gene expression) in tadpoles exposed to the cue so as to begin to describe the physiological mechanisms that govern predation risk assessment of tadpoles. Second, I have begun to relate information availability and the underlying prey physiology to individual predator and prey behavior by conducting numerous empirical studies in laboratory and field settings in which I manipulated the information available to both predators and prey, when alone and together. By comparing behavioral responses among treatments, I have been able to infer perception and information use. These experiments: 1) have begun to identify some of the rules describing how tadpoles assess risk, including how prior experience matters; 2) have illustrated how spatial and temporal context can influence predation risk assessment; and 3) have shown how the multiple states of individuals (e.g. the initial level of perceived risk, energetic state, or circadian activity level) can interact at different temporal scales to determine an individual’s overall activity pattern. I also have manipulated information availability to relate behavior to individual growth rates, population-level spatial distributions, and predator-prey interaction rates. Finally, I have used computational modeling (e.g. individual-based models using genetic algorithms, dynamic state variable models) to explore how behavior may evolve across potential conditions (e.g., depending on the form of physiological constraints, predator and prey perceptual ranges, information reliability), as well as to quantify the impact of predator and prey behavior on population dynamics and interaction strengths over time. I have used empirical experiments to both parameterize these models, as well as test their predictions.

 

I currently have an NSF full proposal in review that will integrate and extend my previous work. The proposed re-search will focus on investigating the proximate mechanisms of predator-induced phenotypic plasticity (behavior, morphology, and development) in tadpoles and its resulting impacts on prey fitness. My collaborators and I will use endocrinological measurements and modifications (i.e., blocking or enhancing the stress response with exogenous hormones or drugs) in conjunction with ecological lab and mesocosm experiments and dynamic state variable modeling. The modeling will complement the empirical work by using parameters specific to the larval dragonfly-tadpole system (obtained from the empirical data). The modeling will also generalize by exploring broader, hypo-thetical conditions. Our objectives are to understand: 1) how the prey neuroendocrine stress response operates over time under a complex predation environment, 2) how stress hormones govern the expression and integration of the prey phenotypic response in an ecological context, and 3) what the fitness consequences of this regulation are. This research will provide a deeper understanding of how physiology and development regulate phenotypic plasticity in adaptive traits, how organisms integrate phenotypic traits, and what the ecological impacts of this regulation are. The long-term goal is to identify the mechanisms that govern prey stress physiology and predator-induced phenotypic plasticity, including how both interact under varying ecosystem conditions (e.g., heterogeneity in predation risk and resource availability) to influence the outcome of predator-prey interactions. Our research will lay a foundation for understanding the role of physiological regulation in predator-prey interactions, and make possible future work that will investigate variation in non-consumptive effects of predators. The future work will incorporate similar research approaches that focuses on the dragonfly response and how regulation of the prey and predator responses influences the joint predator-prey game.

 

In addition to continuing to address basic questions in ecology within the dragonfly-tadpole system, I also am inter-ested in beginning to investigate broader, applied aspects. For example, the ability to predict the effects of human-driven environmental change (e.g. reduced water quality, increased light pollution) would be enhanced by under-standing what sensory information species use and how their environment in turn affects the optimality of possible phenotypic strategies. Secondly, I am interested in using similar approaches to better understand the post-metamorphic dispersal and performance of frogs, and applying this understanding to their conservation. 

 

 

DuFour, M. R., J. J. Davis, M. E. Fraker, E. A. Marschall, C. J. May, C. M. Mayer, J. G. Miner, J. J. Pritt, E. F. Roseman, J. T. Tyson, and S. A. Ludsin. in press. Portfolio theory as a management tool to guide conservation and restoration of multi-stock fish populations. Ecosphere.

 

Fraker, M. E., E. J. Anderson, K.-Y. Chen, J. J. Davis, K. M. DeVanna, M. R. DuFour, E. A. Marschall, C. J. May, C. M. Mayer, J. G. Miner, K. L. Pangle, J. J. Pritt, E. F. Roseman, J. T. Tyson, Y. Zhao, and S. A. Ludsin. in press. Variation in larval advection and early life history of Lake Erie walleye (Sander vitreus): insights from an individual-based biophysical model. Journal of Great Lakes Research  

 

Fraker, M. E., E. J. Anderson, R. Brodnik, L. Carreon-Martinez, K. M. DeVanna, B. J. Fryer, D. D. Heath, J. M. Reichert, and S. A. Ludsin. 2014. Particle backtracking improves breeding subpopulation discrimination and natal-source identification in mixed populations. PLoS ONE 10:e0120752.

 

Fraker, M. E. and B. Luttbeg. 2012. A spatially explicit model of predator-prey space games. Oikos 121:1935-1944.

 

Fraker, M. E. and B. Luttbeg. 2012. Predator-prey space use and the spatial distribution of predation events. Behaviour 149:555-574.

 

Fraker, M. E., V. Cuddapah, S. A. McCollum, R. A. Relyea, J. Hempel, and R. J. Denver. 2009. The behavioral and endocrine stress response of tadpoles to a chemical cue of predation secreted by conspecifics. Hormones and Behavior 55: 520-529.

 

Fraker, M. E. 2010. Risk perception and anti-predator behavior of wood frog (Rana sylvatica) tadpoles: a comparison with green frog (Rana clamitans) tadpoles. Journal of Herpetology 44:390-398.

 

Fraker, M. E. 2009. Predation risk assessment through chemical cues produced by multiple prey. Behavioral Ecology and Sociobiology 63: 1397-1402.

 

Fraker, M. E. 2009. The effect of prior experience on a prey’s current perceived risk. Oecologia 158: 765-774.

 

Fraker, M. E. 2009. The perceptual ability of tadpoles limits the accuracy of their predation risk assessment. Behaviour 146: 1025-1036.

 

Fraker, M. E. 2008. The influence of the circadian rhythm of green frog (Rana clamitans) tadpoles on their antipredator behavior and the strength of the nonlethal effects of  predators. American Naturalist 171: 545-552.

 

Fraker, M. E. 2008. The effect of hunger on the strength and duration of the anti-predator behavioral response of green frog (Rana clamitans) tadpoles. Behavioral Ecology and Sociobiology 62: 1201-1205.

 

Fraker, M. E. and S. D. Peacor. 2008. Statistical tests for biological interactions: a comparison of permutation tests and analysis of variance. Acta Oecologia 33: 66-72.

 

Fraker, M. E. 2008. The dynamics of predation risk assessment: responses of anuran larvae to chemical cues of predators. Journal of Animal Ecology 77: 638-645.

 

Fraker, M. E., J. W. Snodgrass, and F. Morgan. 2002. Differences in growth and maturation of blacknose dace (Rhinichthys atratulus) across an urban-rural gradient. Copeia 2002 (4): 1122-1127.

 

 

Fraker, M. E., R. Brodnik, E. J. Anderson, L. Carreon-Martinez, K. M. DeVanna, B. J. Fryer, D. D. Heath, J. M. Reichert, and S. A. Ludsin. in revision. Combining microsatellite data with dispersal trajectories of larvae reveals novel stock structure and demographically- important population connectivity in a freshwater fish. (Molecular Ecology)

 

DeVanna, K. M., R. E. H. Smith, M. E. Fraker, and 17 coauthors. in review. A perspective on managing Great Lakes fisheries in the face of human-driven ecosystem change. (Canadian Journal of Fisheries and Aquatic Sciences) 

 

Ludsin, S. A., X. Zhang, D. M. Mason, S. B. Brandt, M. R. Roman, W. C. Boicourt, M. E. Fraker, and M. Costantini. in review. Hypoxia reduces availability of quality habitat for Bay anchovy (Anchoa mitchilli) in Chesapeake Bay. (Estuaries and Coasts)   

 

Fraker, M. E., Y. Zhao, and S. A. Ludsin. in prep. A calibrated and validated hydrodynamic-ecological model of plankton dynamics in the western basin of Lake Erie.

 

Ludsin, S. A., and 15 coauthors. in prep. A review and prospectus of likely impacts of climate change on the Great Lakes.