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

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11830
Author(s):  
Richard B. Robold ◽  
Falk Huettmann
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Open Access ◽  
Open Source ◽  
The United States ◽  
Habitat Characteristics ◽  
Distribution Model ◽  
Lidar Data ◽  
Red Squirrel ◽  
Red Squirrels

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.

scholarly journals Open educational resources for validation of global high-resolution land cover maps

2019 ◽  
Author(s):  
Candan E Kilsedar ◽  
Gorica Bratic ◽  
Monia E Molinari ◽  
Marco Minghini ◽  
Maria A Brovelli
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Open Access ◽  
Land Cover ◽  
Open Source ◽  
Open Educational Resources ◽  
Educational Resources ◽  
Validation Process ◽  
Land Cover Maps

Land cover (LC) maps are crucial to analyze and understand several phenomena, including urbanization, deforestation and climate change. This elevates the importance of their accuracy, which is assessed through a validation process. However, we observed that knowledge on the importance of LC maps and their validation is limited. Hence, a set of educational resources has been created to assist in the validation of LC maps. These resources, available under an open access license, focus on validation through open source and easy-to-use software. Moreover, addressing the lack of accurate and up-to-date reference LC data, an application has been developed that provides users a means to collect LC data.

scholarly journals Open educational resources for the validation of global high-resolution land cover maps

2018 ◽  
Author(s):  
Candan E Kilsedar ◽  
Gorica Bratic ◽  
Monia E Molinari ◽  
Marco Minghini ◽  
Maria A Brovelli
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Open Access ◽  
Land Cover ◽  
Open Source ◽  
Open Educational Resources ◽  
Educational Resources ◽  
Validation Process ◽  
Validation Procedure ◽  
Land Cover Maps

Land cover (LC) maps are crucial to understand and analyze several phenomena, including urbanization, deforestation and climate change. This elevates the importance of their accuracy, which is assessed through a validation process. However, it has been observed that knowledge on the importance of LC maps and their validation is limited. Hence, a set of educational resources has been created to assist in the validation of LC maps. These resources, available under an open access license, focus on the validation procedure through open source and easy-to-use tools. Moreover, addressing the lack of accurate and up-to-date reference LC data, an application has been developed that provides users a means to collect LC data.

scholarly journals New World origins for haemoparasites infecting United Kingdom grey squirrels (Sciurus carolinensis), as revealed by phylogenetic analysis of bartonella infecting squirrel populations in England and the United States

2002 ◽  
Vol 129 (3) ◽  
pp. 647-653 ◽  
Author(s):  
K. J. BOWN ◽  
B. A. ELLIS ◽  
R. J. BIRTLES ◽  
L. A. DURDEN ◽  
J. LELLO ◽  
...  
Keyword(s):  
United States ◽  
United Kingdom ◽  
New World ◽  
Phylogenetic Analyses ◽  
The United States ◽  
Sciurus Carolinensis ◽  
Red Squirrel ◽  
Red Squirrels ◽  
The United Kingdom ◽  
Grey Squirrels

Phylogenetic analyses of bartonella have suggested divergence between bartonellae that infect mammals native to the Old and New Worlds. We characterized bartonella isolated from Eastern grey squirrels (Sciurus carolinensis) in the United States and from grey and red squirrels (Sciurus vulgaris) in the United Kingdom by nucleotide sequence comparison (gltA and groEL). Isolates from grey squirrels in the United States and the United Kingdom were identical, and most similar to Bartonella vinsonii, a species associated with New World rodents. A single and novel bartonella genotype was obtained from all 12 red squirrel isolates. Although grey squirrels were first introduced into the United Kingdom over 125 years ago, they continue to be infected solely by the bartonella associated with grey squirrels native to the United States. These results illustrate that exotic species may be accompanied by the introduction and maintenance, over many generations, of their microparasites.

scholarly journals Open educational resources for validation of global high-resolution land cover maps

2019 ◽  
Author(s):  
Candan E Kilsedar ◽  
Gorica Bratic ◽  
Monia E Molinari ◽  
Marco Minghini ◽  
Maria A Brovelli
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Open Access ◽  
Land Cover ◽  
Open Source ◽  
Open Educational Resources ◽  
Educational Resources ◽  
Validation Process ◽  
Land Cover Maps

Land cover (LC) maps are crucial to analyze and understand several phenomena, including urbanization, deforestation and climate change. This elevates the importance of their accuracy, which is assessed through a validation process. However, we observed that knowledge on the importance of LC maps and their validation is limited. Hence, a set of educational resources has been created to assist in the validation of LC maps. These resources, available under an open access license, focus on validation through open source and easy-to-use software. Moreover, addressing the lack of accurate and up-to-date reference LC data, an application has been developed that provides users a means to collect LC data.

scholarly journals Mapping Vegetation at Species Level with High-Resolution Multispectral and Lidar Data over Large Spatial Area: A Case Study with Kudzu

2019 ◽  
Author(s):  
Wanwan Liang ◽  
Mongi Abidi ◽  
Luis Carrasco ◽  
Jack McNelis ◽  
Liem Tran ◽  
...  
Keyword(s):  
High Resolution ◽  
The United States ◽  
Vegetation Mapping ◽  
Second Step ◽  
Support Vector ◽  
Lidar Data ◽  
Sampling Effort ◽  
Large Spatial Scale ◽  
Knox County ◽  
Vegetation Species

Mapping vegetation species is critical to facilitate related quantitative assessment, and for invasive plants mapping their distribution is important to enhance monitoring and controlling activities. Integrating high resolution multispectral remote sensing (RS) image and lidar (light detection and ranging) point clouds can provide robust features for vegetation mapping. However, using multiple source of high-resolution RS data for vegetation mapping at large spatial scale can be both computationally and sampling intensive. Here we designed a two-step classification workflow to decrease computational cost and sampling effort, and to increase classification accuracy by integrating multispectral and lidar data to derive spectral, textural, and structural features for mapping target vegetation species. We used this workflow to classify kudzu, an aggressive invasive vine, in the entire Knox County (1,362 km2) of Tennessee, the United States. Object-based image analysis was conducted in the workflow. The first-step classification used 320 kudzu samples and extensive coarsely labeled samples (based on national land cover) to generate an overprediction map of kudzu using random forest (RF). For the second step, 350 samples were randomly extracted from the overpredicted kudzu and labeled manually for the final prediction using RF and support vector machine (SVM). Computationally intensive features were only used for the second-step classification. SVM had constantly better accuracy than RF, and the Producer’s Accuracy, User’s Accuracy, and Kappa for the SVM model on kudzu was 0.94, 0.96, and 0.90, respectively. SVM predicted 1010 kudzu patches covering 1.29 km2 in Knox County. We found the sample size of kudzu used for algorithm training impacted the accuracy and number of kudzu predicted. The proposed workflow could also improve sampling efficiency and specificity. Our workflow had much higher accuracy than the traditional method conducted in this research, and could be easily implemented to map kudzu in other regions or other vegetation species.

scholarly journals Predicting multi-species bark beetle (Coleoptera: Curculionidae: Scolytinae) occurrence in Alaska: First use of open access big data mining and open source GIS to provide robust inference and a role model for progress in forest conservation

2021 ◽  
Vol 16 (1) ◽  
pp. 1-19 ◽  
Author(s):  
Khodabakhsh Zabihi ◽  
Falk Huettmann ◽  
Brian Young
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Open Access ◽  
Open Source ◽  
Bark Beetle ◽  
Bark Beetles ◽  
Climatic Conditions ◽  
The State ◽  
Data Mining Algorithm ◽  
Open Source Gis

Native bark beetles (Coleoptera: Curculionidae: Scolytinae) are a multi-species complex that rank among the key disturbances of coniferous forests of western North America. Many landscape-level variables are known to influence beetle outbreaks, such as suitable climatic conditions, spatial arrangement of incipient populations, topography, abundance of mature host trees, and disturbance history that include former outbreaks and fire. We assembled the first open access data, which can be used in open source GIS platforms, for understanding the ecology of the bark beetle organism in Alaska. We used boosted classification and regression tree as a machine learning data mining algorithm to model-predict the relationship between 14 environmental variables, as model predictors, and 838 occurrence records of 68 bark beetle species compared to pseudo-absence locations across the state of Alaska. The model predictors include topography- and climate-related predictors as well as feature proximities and anthropogenic factors. We were able to model, predict, and map the multi-species bark beetle occurrences across the state of Alaska on a 1-km spatial resolution in addition to providing a good quality environmental dataset freely accessible for the public. About 16% of the mixed forest and 59% of evergreen forest are expected to be occupied by the bark beetles based on current climatic conditions and biophysical attributes of the landscape. The open access dataset that we prepared, and the machine learning modeling approach that we used, can provide a foundation for future research not only on scolytines but for other multi-species questions of concern, such as forest defoliators, and small and big game wildlife species worldwide.

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