Novel Approaches to Modeling and Mapping Terrestrial Vertebrate Occurrence in the Northwest and Alaska: An Evaluation

Aycrigg, Jocelyn L.; Beauvais, Gary P.; Gotthardt, Tracey; Huettmann, Falk; Pyare, Sanjay; Andersen, Mark D.; Keinath, Doug; Lonneker, Jeff; Spathelf, Miles; Walton, Kelly M.

doi:10.3955/046.089.0405

Cited by 13 publications

(5 citation statements)

References 57 publications

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“…In the wide absence of any relevant research design specific for GGOW (see 108 - 110 for road bias and how resolved), representative sampling, of an Alaskan Bird Atlas and Nesting Survey for that matter (compare with Birds of Yukon 111 , or bird banding/ringing work elsewhere in the GGOW range, e.g. 112 ), and unsubstantiated narratives 113 this question currently cannot be answered with ultimate accuracy (compare with 114 ; see 101 for owls in Southeast Alaska). Table 3 shows that more data and information exist that actually could be used, but unfortunately it is not presented to us, communicated with the public, and available to the public or science’s use.…”

Section: Discussionmentioning

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

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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distribution and range maps for the Great Grey Owl (GGOW; Strix nebulosa) are ambiguous, contradictory, imprecise, outdated, often hand-drawn and thus not quantified, not based on data or scientific.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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Section: Discussionmentioning

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

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“…We generated maps of potential species richness by summing potential habitat maps of individual species (McKerrow et al, ). While these individual GAP species potential habitat maps were not assessed for accuracy, previous iterations of GAP modelling efforts related to species potential distribution projects at state and regional level using nearly identical deductive methodologies produced accuracy rates of 65%–80% (Aycrigg et al, ; Maxwell & Gergely, ; Maxwell, Gergely, Aycrigg, & Davidson, ; McKerrow, Williams, & Collazo, ). The latest update of the complete data set has undergone a USGS data review and has been included in McKerrow et al (.…”

Section: Methodsmentioning

confidence: 99%

“…We generated maps of potential species richness by summing potential habitat maps of individual species (McKerrow et al, 2018). While these individual GAP species potential habitat maps were not assessed for accuracy, previous iterations of GAP modelling efforts related to species potential distribution projects at state and regional level using nearly identical deductive methodologies produced accuracy rates of 65%-80% (Aycrigg et al, 2015;Maxwell & Gergely, 2005;Maxwell, Gergely, Aycrigg, & Davidson, 2009;McKerrow, Williams, & Collazo, 2006). The latest update of the complete data set has undergone a USGS data review and has been included in McKerrow et al (2018. Since abundance data are not available for all species in the study area, we were not able to assess biodiversity indicators based on species abundance, such as the Simpson index (Simpson, 1949), Shannon index (Shannon-Wiener, Weaver, & Weater, 1950) or the mean species abundance (Alkemade et al, 2009).…”

Section: Species Richness Datamentioning

confidence: 99%

Impact of increased wood pellet demand on biodiversity in the south‐eastern United States

et al. 2018

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Increasing wood pellet exports from the United States are projected to lead to changes in land use and timberland management, including a shift from natural timberland to pine plantations. These projected changes may impact biodiversity. This study aims to quantify potential biodiversity impacts of increased wood pellet demand in the south‐eastern United States in a spatially explicit manner. We determined differences according to an index of potential species richness (for total, threatened and endemic species and four taxonomic groups) between scenarios of high and low demand for wood pellets, while taking into account potential developments in other wood markets and other land uses. Increased demand for wood pellets was projected to cause both positive and negative biodiversity impacts. Negative shifts in total potential species richness were projected for areas in Florida, coastal Virginia and North Carolina, and parts of the Gulf Coast. Positive shifts in total potential species richness were projected in parts of Oklahoma and Arkansas. In some locations, the direction of change differed per taxonomic group, highlighting the importance of analysing different taxonomic groups. Shifts in potential species richness due to increased wood pellet demand were considerably smaller compared to the changes due to other drivers, such as urbanization and increased timber demand. Biodiversity impacts due to wood pellet demand should therefore be considered in the context of other drivers of land‐use change and biodiversity loss. Our results provide information that allows policymakers, industry and NGOs to focus on areas of concern and take appropriate mitigation measures to limit negative biodiversity impacts and promote positive impacts. The spatially explicit approach presented in this study can be applied to different regions and drivers of land‐use change, to show how projected demand for an internationally traded commodity may lead to impacts on land use and biodiversity in the procurement region.

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“…Beyond being ‘cute’, small and abundant mammals, they are not really in the focus of the public for all their habitat and conservation needs. Red squirrels virtually lack a (conservation-) management program in Alaska Department of Fish & Game (2021) and in most parts of North America, as well as any abundance, population, distribution or trend estimates ( Aycrigg et al, 2015 ); the USDA still uses density numbers which are over 20 years old ( Sullivan, 1995 ).…”

Section: Introductionmentioning

confidence: 99%

High-resolution prediction of American red squirrel in Interior Alaska: a role model for conservation using open access data, machine learning, GIS and LIDAR

Robold

Huettmann

2021

PeerJ

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American red squirrels (Tamiasciurus hudsonicus) are small mammals that are abundantly distributed throughout North America. Urbanization in the Anthropocene is now a global process, and squirrels live in affected landscapes. This leads to squirrels adjusting to human developments. Not much is known about the distribution of squirrels and squirrel middens near humans, especially not in the subarctic and sub-urbanized regions. Although this species is hunted, there are no real publicly available distribution and abundance estimates nor management plans and bag limits for squirrels in Alaska or in the United States known by us, except the endangered Mt. Graham squirrel. In general, insufficient squirrel conservation research is carried out; they are underrepresented in research and its literature. To further the science-based management for such species, this study aims to generate the first digital open access workflow as a generic research template for small mammal work including the latest machine learning of open source and high-resolution LIDAR data in an Open Source Geographic Information System (QGIS) and ArcGIS. Machine learning has proven to be less modeler biased and improve accuracy of the analysis outcome, therefore it is the preferred approach. This template is designed to be rapid, simple, robust, generic and effective for being used by a global audience. As a unique showcase, here a squirrel midden survey was carried out for two years (2016 and 2017). These squirrel middens were detected in a research area of 45,5 hectares (0,455 km2) in downtown Fairbanks, interior boreal forest of Alaska, U.S. Transect distances were geo-referenced with a GPS and adjusted to the visual conditions to count all squirrel middens within the survey area. Different layers of proximity to humans and habitat characteristics were assembled using aerial imagery and LIDAR data (3D data needed for an arboreal species like the red squirrels) consisting of a 3 × 3 m resolution. The layer data was used to train a predictive distribution model for red squirrel middens with machine learning. The model showed the relative index of occurrence (RIO) in a map and identified canopy height, distance to trails, canopy density and the distance to a lake, together, as the strongest predictors for squirrel midden distribution whereas open landscape and disturbed areas are avoided. It is concluded that squirrels select for high and dense forests for middens while avoiding human disturbance. This study is able to present a machine learning template to easily and rapidly produce an accurate abundance prediction which can be used for management implications.

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Novel Approaches to Modeling and Mapping Terrestrial Vertebrate Occurrence in the Northwest and Alaska: An Evaluation

Cited by 13 publications

References 57 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

Impact of increased wood pellet demand on biodiversity in the south‐eastern United States

High-resolution prediction of American red squirrel in Interior Alaska: a role model for conservation using open access data, machine learning, GIS and LIDAR

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