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Ecological Modeling
Ecological Modeling

ECOLOGICAL MODELINGON THIS PAGE

Insight into the ecology of our world is difficult to obtain. Organisms, habitats, and the physical world are interconnected as ecosystems in such a way that studying one in the absence of the others can be misleading. It’s a bit like juggling with one ball: sure it’s possible, but it is really even juggling anymore? We also know that that our observations of the environment are fundamentally flawed. If you want to know how many birds are in the local park, you go out and count them. But, to determine how many actually use the park, you need to know how many you counted, how good you are at counting animals, when they are around, and how often they move in at out of the park.

This is why experimental design and statistical modeling are so important to ecology. Experimental design allows us to keep all the juggling balls in the air while we focus on the one that is important. Statistical modeling helps us understand where we err, why it happened, and how it influences our understanding of ecological processes. To make accurate predictions about ecosystems—that is, to describe how the world works and project what will happen in the future—is a daunting task. Through quantitative ecology, we can make these predictions better by assessing the accuracy of our knowledge and striving for improvement in our science.

 

Hierarchical Population Modeling

Describing densities and distributions of plants and animals and the ecological processes that govern them are essential to the field of ecology. To get estimates of population size and densities of animals we employ hierarchical models that use count data to estimate the true population size by accounting for detection probability during a survey. These techniques can be employed over a wide range of surveys from boat-based seabird surveys of seabirds to auditory surveys of songbirds in the forest.
 

Demographic Rate Estimation and Population Prediction

Populations rise and fall over time; to understand why, we need to know basic demographic parameters. Populations decrease with mortality or emigration and increase with births and immigration, but these parameters are often difficult to estimate in the wild. Using standard techniques such as mark-recapture and nest or colony monitoring, we can estimate parameters including survival and fecundity along with how they vary over space and time or with environmental correlates. Researchers then use these data to project population sizes into the future using population or individual-based modeling techniques.
 

Movement Behavior

The spatial ecology of animals—the relationship of their space use patterns to ecological processes—is important for understanding where animals forage, what habitats they use most frequently, and describing migratory movements and patterns. Using tracking techniques, such as satellite telemetry or geolocators, can provide us information on how individuals move around the landscape. Analytical techniques including state-space modeling, utilization distributions, kernel density estimation, and Brownian bridge models can describe how individuals or groups move through the world around them and provide estimates of certainty for the patterns described.
 

Developing New Monitoring Techniques

Monitoring animals and their environment is essential to learning about the ecological world, and as monitoring techniques advance, the number of possible answerable research questions increases. In addition to thinking about how best to describe the world with mathematical models, we also have to think about the basics of information gathering and the technology behind it. New techniques such as remote sensing from satellites have revolutionized the ecological world and we always need to be pushing such technology forward. At BRI, we have been working on wildlife monitoring in the offshore environment using novel equipment and techniques to make understanding the ocean easier for scientists.
Biodiversity Research Institute