Post provided by Holly Niven.

I’m Holly, an ecology PhD student at the University of Glasgow, with a background in mathematics and physics. My research is in quantitative ecology, with a current focus on investigating the exposure of animals to disturbances in their environment and understanding the drivers of their population dynamics. 

What are home ranges and why are they useful?

Home ranges (HRs) describe the space use of animals in carrying out their daily activities such as foraging and commuting. More technically, it is the probability distribution of an animal’s space use and is often defined as the space where an animal is found 95% of the time.

Estimating HRs can help us answer space use questions. For example: are seabird HRs segregated and to what extent? Or, how are animals exposed to stressors in their environment? The exposure of an animal to a stressor in its environment can be estimated by calculating the overlap of its HR with the stressor.

Colonial animals are particularly vulnerable to stressors within their surrounding environment or HR, as they are constrained to commute to and from their colony. Accurate estimation of HRs and exposure to environmental stressors are important for environmental risk assessment.

Existing home range models

Existing models of home ranges can be considered on a spectrum ranging from individual based models (IBMs) to a population level perspective in density estimation methods or species distributions models (SDMs). IBMs may fail to capture large-scale movement patterns and be computationally challenging to fit to data, while density estimation methods may have high data requirements, struggle with prediction and be hard to integrate with biological realism. Other more mechanistic models (e.g., projected distributions) have been developed for systems with low data availability; however, they may lack biological realism and may not be fitted to data at all.

Our home range model for colonial animals

In our recent article, we present a novel method for home range estimation in colonial animals, which combines the strengths of existing approaches: akin to SDMs, it can be pragmatically fitted to minimal data, akin to IBMs, it incorporates biological complexities. And it is predictive. The best of both worlds! Instead of modelling the movement of individuals, like in an IBM, we model the movement of animal usage through space, which is influenced by complex interactions between landscape accessibility, energetic constrains and between- and within- colony competition. Our model requires tracking data from as few as two proximate colonies to tune the model to a species. Once tuned, only colony locations and sizes for the colonies and years of interest are needed to make HR predictions. This means that we can make HR predictions for colonies and years without tracking data, which for many reasons are not always obtainable (especially for the future!).

Does it work?

As an example, we applied our model to the Northeast Atlantic Northern gannet colony network foraging around the British Isles. It is important to estimate accurate gannet colony home ranges as they are considered vulnerable to offshore wind farms through collision risk with turbine blades and avoidance of wind farm areas, leading to loss of habitat. Their largest breeding colonies are also in close proximity to current and planned developments (Figure 1).

Optimising the model to the gannet using GPS tracking data from just two gannet colonies (Grassholm and Great Saltee), we predicted gannet HRs for all colonies using colony size estimates and locations.  Predicted HRs showed striking similarity to validation tracking data (Figure 2). It works!

Figure 2: A: Predicted gannet 95% HRs in 2011 for all gannet colonies surrounding the British Isles with tracking data. B: GPS tracking data from 2011 for comparison.

Does it matter?

We validated our predictions against two other industry-standard home range estimation methods, the foraging range method (which assumes constant density across a defined radius) and the projected distributions method (which additionally incorporates a decline in usage with distance from the colony). Our method approximately doubled on the predictive performance of the other existing methods, assigning 74% of validation tracking locations to their correct colonies, compared to 41% and 31% from the existing methods.

Estimating the exposure of gannets to planned offshore wind farms produced different results for each method. Overall, existing methods underestimated the exposure of gannets to planned offshore wind farms compared to our method, but whether they under- or overestimate exposure will depend on the colony and scenario. It matters!

Because of these improvements, our method can be used to more accurately estimate colony exposure to stressors and assign seabirds detected using at-sea surveys to their likely colony of origin. Our method can also be used to predict future HRs and exposure given projected future colony sizes.

What next?

We are in the process of developing our method into an R package for more convenient use and are working to include the method in the offshore wind farm environmental risk assessment process for seabirds.

Our method is applicable to any colonial species, e.g., bats, seals and social insects. Want to try it out with your study species or want to know more? Read the full article here or contact me, Holly Niven at holly.niven@glasgow.ac.uk.

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Post edited by Sthandiwe Nomthandazo Kanyile