CAREER: Defining drivers and scaling algorithms for multi-scale species-environment relationships

Characteristics of both the house and the neighborhood often matter when people decide where to live. The same holds true for birds; when deciding where to place a nest, both the characteristics of the tree, and the surrounding area are important. However, these characteristics – including neighborhood size and its environmental features – are unknown for the majority of bird species.

And yet, our ability to predict where species are currently found and where they are likely to be in the future depends on understanding why birds use the places they do. The project proposed here seeks to understand the characteristics of the environmental neighborhoods that shape where birds occur across North America, then test hypotheses as to why birds are found in their respective neighborhoods.

Results from this work will be valuable to society because understanding how species respond to their surroundings can help us use wildlife management resources more effectively, and improve our predictions of how species may respond to future environmental change. This CAREER project will also train the next generation of ecologists in ecological data science.

The overarching goal of this work is to deepen our understanding of the relationships between birds and their environments and enable improved decision-making to ensure the survival of species for future generations.

Scale-dependence, where patterns observed at one scale may be different when observed at another scale, has long been acknowledged but largely omitted from investigations of species-environment relationships. The study of scale-dependence in species’ response to the environment has only recently become feasible with advances in remote sensing and specialized analytics.

This project aims to describe and understand the basis of variation in species’ scales of effect (SoE), the spatial scales at which species respond to facets of the environment, using a massive, citizen science bird observation database. The research objectives of this proposal are to: 1) characterize SoE for hundreds of North American breeding birds; 2) test prevailing hypotheses regarding the mechanisms generating SoE; and 3) define a scaling algorithm to predict species’ SoE.

Objectives of this project will be accomplished through multi-scale analyses, graduate-level ecological data science learning experiences, and undergraduate research. We will compile project results on species-specific SoE within an interactive dashboard to provide wildlife managers with information on the optimal scales at which species respond to the environment.

This dashboard of results will also serve as a dataset to begin fundamental research into the selective pressures shaping SoE, and how variation in SoE may constrain species’ response to environmental change.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.