Model-based prediction of a vacant summer niche in a subarctic urbanscape: A multi-year open access data analysis of a ‘niche swap’ by short-billed Gulls

Huettmann, Falk; Kövér, László; Robold, Richard; Spangler, Mark; Steiner, Moriz

doi:10.1016/j.ecoinf.2023.102364

Cited by 4 publications

(4 citation statements)

References 45 publications

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“…Those sightings are linked with man-made, urban and industrial habitats indeed, beyond ‘myth’. It matches other wildlife research findings in Alaska, such as 50 .…”

Section: Discussionsupporting

confidence: 89%

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A super SDM (species distribution model) ‘in the cloud’ for better habitat-association inference with a ‘big data’ application of the Great Gray Owl for Alaska

Huettmann,

Andrews,

Steiner

et al. 2024

Sci Rep

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The currently available distribution and range maps for the Great Grey Owl (GGOW; Strix nebulosa) are ambiguous, contradictory, imprecise, outdated, often hand-drawn and thus not quantified, not based on data or scientific. In this study, we present a proof of concept with a biological application for technical and biological workflow progress on latest global open access ‘Big Data’ sharing, Open-source methods of R and geographic information systems (OGIS and QGIS) assessed with six recent multi-evidence citizen-science sightings of the GGOW. This proposed workflow can be applied for quantified inference for any species-habitat model such as typically applied with species distribution models (SDMs). Using Random Forest—an ensemble-type model of Machine Learning following Leo Breiman’s approach of inference from predictions—we present a Super SDM for GGOWs in Alaska running on Oracle Cloud Infrastructure (OCI). These Super SDMs were based on best publicly available data (410 occurrences + 1% new assessment sightings) and over 100 environmental GIS habitat predictors (‘Big Data’). The compiled global open access data and the associated workflow overcome for the first time the limitations of traditionally used PC and laptops. It breaks new ground and has real-world implications for conservation and land management for GGOW, for Alaska, and for other species worldwide as a ‘new’ baseline. As this research field remains dynamic, Super SDMs can have limits, are not the ultimate and final statement on species-habitat associations yet, but they summarize all publicly available data and information on a topic in a quantified and testable fashion allowing fine-tuning and improvements as needed. At minimum, they allow for low-cost rapid assessment and a great leap forward to be more ecological and inclusive of all information at-hand. Using GGOWs, here we aim to correct the perception of this species towards a more inclusive, holistic, and scientifically correct assessment of this urban-adapted owl in the Anthropocene, rather than a mysterious wilderness-inhabiting species (aka ‘Phantom of the North’). Such a Super SDM was never created for any bird species before and opens new perspectives for impact assessment policy and global sustainability.

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“…Those sightings are linked with man-made, urban and industrial habitats indeed, beyond ‘myth’. It matches other wildlife research findings in Alaska, such as 50 .…”

Section: Discussionsupporting

confidence: 89%

“…Here we apply published and alternative data, e.g. coming from a research design, as well as several citizen science source data for this species overall within Alaska (examples show in 50 ).…”

Section: Methodsmentioning

confidence: 99%

A super SDM (species distribution model) ‘in the cloud’ for better habitat-association inference with a ‘big data’ application of the Great Gray Owl for Alaska

Huettmann,

Andrews,

Steiner

et al. 2024

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…Due to this fact, analyzing is getting more challenging, not only because of the growing size of data, but also because data evolve into more complex data structures. Depending on the data, various models and techniques can be applied for dispersal and predicting animal migrations, usually ranging from statistics [3][4][5][6] to machine learning [2,[7][8][9] and deep learning [10][11][12][13][14] models to spatial analysis techniques applied on GIS data [3,7,14,15] appearing in recent animal movement research, as well as already mentioned simulations [16,17].…”

Section: Introductionmentioning

confidence: 99%

“…In an additional supervised learning study, Butts et al [8] emphasized using and finding appropriate mathematical models for one deer and extrapolated this to model groups of deer through a so-called "data-driven agent-based" modeling approach. Huettmann et al [9] looked at seasonal plot surveys for short-billed gull presence at Fairbanks in Alaska using classification and regression trees (CARTs), CART Ensembles and Bagger, TreeNet, random forests, and multivariate adaptive regression splines (MARS). The simulation models presented in [2,8] contributed to our studies and were the sources of inspiration for gaining insights into ML model approaches.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach to Simulation of Mallard Movements

Einarson,

Frisk,

Klonowska

et al. 2024

Applied Sciences

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Machine learning (ML) is increasingly used in diverse fields, including animal behavior research. However, its application to ambiguous data requires careful consideration to avoid uncritical interpretations. This paper extends prior research on ringed mallards where sensors revealed their movements in southern Sweden, particularly in areas with small lakes. The primary focus is to distinguish the movement patterns of wild and farmed mallards. While well-known statistical methods can capture such differences, ML also provides opportunities to simulate behaviors outside of the core study span. Building on this, this study applies ML techniques to simulate these movements, using the previously collected data. It is crucial to note that unrefined application of ML can lead to incomplete or misleading outcomes. Challenges in the data include disparities in swimming and flying records, farmed mallards’ biased data due to feeding points, and extended intervals between data points. This research highlights these data challenges, while identifying discernible patterns, as well as proposing approaches to meet such challenges. The key contribution lies in separating incompatible data and, through different ML models, handle these separately to enhance the reliability of the simulation models. This approach ensures a more credible and nuanced understanding of mallard movements, demonstrating the importance of critical analysis in ML applications in wildlife studies.

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Declining planetary health as a driver of camera-trap studies: Insights from the web of science database

Dhakal,

Kim,

Kim

et al. 2024

Ecological Informatics

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Model-based prediction of a vacant summer niche in a subarctic urbanscape: A multi-year open access data analysis of a ‘niche swap’ by short-billed Gulls

Cited by 4 publications

References 45 publications

A super SDM (species distribution model) ‘in the cloud’ for better habitat-association inference with a ‘big data’ application of the Great Gray Owl for Alaska

A super SDM (species distribution model) ‘in the cloud’ for better habitat-association inference with a ‘big data’ application of the Great Gray Owl for Alaska

A Machine Learning Approach to Simulation of Mallard Movements

Declining planetary health as a driver of camera-trap studies: Insights from the web of science database

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