Where do Animals Spend Their Time and Energy? Theory, Simulations and GPS Trackers Can Help Us Find Out

Post provided by MATT MALISHEV (@DARWINANDDAVIS)

 An adult sleepy lizard with a GPS tracker and body temperature logger strapped to her tail. ©Mike Bull.


An adult sleepy lizard with a GPS tracker and body temperature logger strapped to her tail. ©Mike Bull.

Changes in temperature and available food determine where and when animals move, reproduce, and survive. Our understanding of how environmental change impacts biodiversity and species survival is well-established at the landscape, country and global scales. But, we know less about what could happen at finer space and time scales, such as within habitats, where behavioural responses by animals are crucial for daily survival.

Simulating Movement and Daily Survival with Individual-Based Movement Models

Key questions at these scales are how the states of individuals (things like body temperature and nutritional condition) influence movement decisions in response to habitat change, and how these decisions relate to patchiness in microclimates and food. So we need tools to make reliable forecasts of how fine-scale habitat use will change under future environments. Individual-based movement simulation models are powerful tools for these kinds of studies. They let you construct habitats that vary in temperature and food conditions in both space and time and ask ‘what if’ questions. By populating these models with activity, behaviour, and movement data of animals, we can simulate different habitat conditions and predict how animals will respond to future change.

Choosing an Ideal Study Species

©Mike Kearney

Insects, fish, and reptiles make up the majority of life on Earth and are also ectotherms. Ectotherms rely on external environment temperatures to regulate their internal body temperature. This means that they’re reliable indicators and model organisms for testing how changes in temperature and food availability impact individual movement, behaviour, and activity patterns.

The sleepy lizard, Tiliqua rugosa, is a long-lived lizard species able to thrive in the hot, dry, and arid climates of southern Australia. They adjust their body temperature by shuttling in and out shaded areas, such as plants and burrows, in search of food and mates. Their survival depends on their capacity to regulate their internal energy budget, body temperature, and water balance. This allows them to navigate the challenging thermal landscape that varies in space and time on daily and hourly cycles. All of this means that sleepy lizards are a useful model organism to simulate how animals move and establish home ranges based on changes in the external environment in space and time from the internal energy, heat, and water budget of the individual.

Bringing Energy Budgets and Movement Models Together

Simulated feeding activity time throughout the mating season for an active (dark blue) and passive (light blue) adult sleep lizard. Circumference = time of year, radius = time spent feeding.

Simulated feeding activity time throughout the mating season for an active (dark blue) and passive (light blue) adult sleep lizard. Circumference = time of year, radius = time spent feeding.

In our Methods in Ecology and Evolution paper ‘An individual-based model of ectotherm movement integrating metabolic and microclimatic constraints’, we built an individual-based simulation model using data on the physiology (e.g. body mass, energy reserves, and body temperature) and movement capacity (e.g. GPS tracking data) of adult sleepy lizards. We then coupled the model with daily and hourly cycles of temperature and food availability within their natural habitat. To make the model generic and useful, we calculated the internal state of the animal using a general, metabolic theory on energy and mass balances, Dynamic Energy Budget (DEB) theory.

DEB theory allows us to build a bottom-up understanding of the constraints on activity and movement from basic biological principles and unifying rules of energy and mass exchange between animals and their environment. With this, we can forecast movement decisions, activity patterns, and home range emergence in a predictive, bottom-up framework under changing environments.

We simulated the activity budgets—searching for food/mates, resting, eating, and movement—and home range patterns of adult animals under passive and active movement strategies. Using these, we were able to explore how life history consequences depend on constraints on activity extremes throughout the summer mating season (Sep to Dec).

Home range patterns in space for active (left) and passive (right) animals in the wild (top) and model simulations (bottom).

Our model captured movement and home range patterns in space and time of animals in the wild. Movement and activity are consistent with the extremes we would expect when driven by feeding requirements (passive movement) or limited by temperature (active movement). By explicitly simulating the energy, mass, and heat budget of individual animals, we found that the additional energy costs of being more active when searching for food and mates did not lead to benefits like growth or reproductive potential. This suggests passive movement as the lower end of the activity spectrum is a more viable and efficient way of defining home range areas under intense and frequent changes in habitat conditions in hot and arid environments.

How You Can Use this Method

By using a theory-driven understanding of energy exchange in animals, including their movement costs, we were able to create habitats of microclimate and food that vary in their spatial structure. With these, we to showed that animals actively balance their energy use against potential activity windows based on changing habitat conditions throughout the day.

As we continue to integrate richer data on climate and habitat conditions with GPS tracking data on species activity, tools like simulation models become the eyes and ears of what future environments will hold. This doesn’t only apply to species in extreme habitats, but those entering these unfamiliar habitats as their current ones undergo inevitable change. These data we collect feed our models with key information on the states of the individual, its environment, and the direct feedbacks defining basic ecology. All of these are needed to accurately predict species survival and preserve biodiversity on an ever-changing planet.

To find out more, read our article ‘An individual‐based model of ectotherm movement integrating metabolic and microclimatic constraints’.

 This article was shortlisted for the Robert May Prize 2018. You can find all of the shortlisted articles is this Virtual Issue.

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