scholarly journals Influence of Habitat on Presence of Striped Skunks in Midwestern North America

Diversity ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 83
Author(s):  
Katelyn Amspacher ◽  
F. Agustín Jiménez ◽  
Clayton Nielsen
Keyword(s):  
North America ◽  
Land Cover ◽  
Open Space ◽  
Forest Cover ◽  
Species Distribution Model ◽  
Distribution Model ◽  
Natural Food ◽  
Striped Skunk ◽  
Human Modification ◽  
Striped Skunks

Striped skunks (Mephitis mephitis) are urban-adapted, generalist mesocarnivores widely distributed throughout North America. Although striped skunks have been studied extensively at small scales, knowledge of habitat influences on striped skunks at large scales is lacking. We developed a species distribution model (SDM) to examine potential striped skunk presence in a 16,058 km2 portion of southern Illinois, USA. We built models using SDM Toolbox and MaxEnt, and incorporated known presence locations, 1 km2 land cover data, and an index of human modification of the landscape. Land cover and human modification explained 98% and 2% of variation in our model, respectively. The highest presence of striped skunks existed in areas with forest cover and developed open space with moderate human modification. The striped skunk presence was lowest in areas with cultivated crops and woody wetlands with either low or high human modification. Forest cover provides natural food and shelter resources for striped skunks, but resources are likely augmented by human activity in developed open space. Cultivated crops only provide seasonal resources, and inundation limits denning in wooded wetlands. Our model indicated striped skunks are a synanthropic species that regularly inhabits both natural and anthropogenic habitats over a large scale.

Mapping the Potential Distribution of Oak Wilt (Bretziella fagacearum) in East Central and Southeast Minnesota Using Maxent

2019 ◽  
Vol 117 (6) ◽  
pp. 579-591 ◽  
Author(s):  
Melissa Gearman ◽  
Mikhail S Blinnikov
Keyword(s):  
Land Cover ◽  
Species Distribution Model ◽  
Distribution Model ◽  
Oak Wilt ◽  
East Central ◽  
Jackknife Test ◽  
Forest Pathogen ◽  
Sensing Technology ◽  
Forest Diseases ◽  
Better Than

Abstract With the advancement of spatial analysis and remote sensing technology, potentially devastating forest pathogens can be managed through spatial modeling. This study used Maxent, a presence-only species-distribution model, to map the potential probability distribution of the invasive forest pathogen oak wilt (Bretziella fagacearum) in eastern and southeastern Minnesota. The model related oak wilt occurrence data to environmental variables including climate, topography, land cover, soil, and population density. Results showed that areas with the highest probability of oak wilt occur within and surrounding the Minneapolis/St. Paul metropolitan area. The jackknife test of variable importance indicated land cover and soil type as important variables contributing to the prediction of the distribution. Multiple methods of analysis showed that the model performed better than random at predicting the occurrence of oak wilt. This study shows Maxent’s potential as an accurate tool in the early detection and management of forest diseases.

scholarly journals Integrating global land cover products for improved forest cover characterization: an application in North America

2013 ◽  
Vol 7 (9) ◽  
pp. 709-724 ◽  
Author(s):  
Xiao-Peng Song ◽  
Chengquan Huang ◽  
Min Feng ◽  
Joseph O. Sexton ◽  
Saurabh Channan ◽  
...  
Keyword(s):  
North America ◽  
Land Cover ◽  
Forest Cover ◽  
Global Land Cover ◽  
Global Land

scholarly journals GEOSPATIAL ECOLOGICAL FOREST CORRIDOR MODELLING IN THE MOUNT LANTOY KEY BIODIVERSITY AREA

2019 ◽  
Vol XLII-4/W19 ◽  
pp. 347-352
Author(s):  
I. P. Quijano ◽  
M. J. L. Flores ◽  
A. B. Malaki
Keyword(s):  
Forest Cover ◽  
Species Distribution Model ◽  
Bird Species ◽  
Canopy Cover ◽  
Distribution Model ◽  
Forest Patches ◽  
Biodiversity Indicator ◽  
Ecological Corridors ◽  
Forest Corridors ◽  
The Cost

Abstract. In biodiversity conservation, ecological corridors are assumed to increase landscape-level connectivity and to enhance the viability of otherwise isolated wildlife populations. Mapping these corridors serves as a feasible method to support forest management efforts in pinpointing areas to give special attention to. Here, we assess the current forest presence in the 3,000 hectare Mt. Lantoy, Key Biodiversity Area in Argao, Cebu and present potential forest corridors that could enhance the canopy cover of the current protected area. We present a method to map the potential corridors through the identification of the forest patches obtained from the global forest cover dataset and the creation of a species distribution model for the black shama, an endemic bird species in Cebu island and a great biodiversity indicator for the area. Our ecological corridors were acquired through the sum of the cost distance rasters obtained from the weighted overlay and cost surface tools of the black shama habitat suitability model. With the obtained corridors from the study, four potential forest corridors/ extensions were identified connecting five different forest patches. These corridors have areas that range from 0.47–2.17 square kilometers, with a potential to increase the forest cover in the KBA to more than 33% after corridor modelling.

scholarly journals Global invasion risk of Apocephalus borealis, a honey bee parasitoid

Apidologie ◽  
2021 ◽  
Author(s):  
Erik Tihelka ◽  
John Hafernik ◽  
Brian V. Brown ◽  
Christopher Quock ◽  
Andrew G. Zink ◽  
...  
Keyword(s):  
North America ◽  
Honey Bee ◽  
Honey Bees ◽  
Species Distribution Model ◽  
Viral Infections ◽  
Distribution Model ◽  
Climate Change Scenarios ◽  
Invasion Risk ◽  
Potential Threat ◽  
Honey Bee Health

AbstractApocephalus borealis is a parasitoid of hymenopterans native to North America that also attacks introduced honey bees (Apis mellifera). Parasitism by this species has been associated with infested bees absconding the hive and dying outside. The flies can also harbour viral infections and nosematosis. Recently, nucleotide sequences identical to A. borealis were reported from bulk screenings of honey bees from Belgium and South Korea, although no adult flies have been collected. To predict the potential invasion risk of A. borealis across the world, we constructed a MaxEnt species distribution model based on occurrence data from North America submitted to the citizen science project ZomBee Watch (zombeewatch.org) and from museum specimens. The results have shown that extensive parts of Europe, the Mediterranean Basin, Asia Minor, southern Africa, eastern Asia, Australasia, and North and South America have high degrees of climatic suitability for invasion, suggesting that the fly could establish in these regions. The potential invasion range is expected to stay similar under different climate change scenarios. We discuss the status of A. borealis as an invasive species and measures that can be taken to reduce the risk of its introduction outside of North America. Our results highlight A. borealis as a potential threat to honey bee health worldwide that requires urgent attention of international veterinary bodies to prevent its spread.

scholarly journals A Robust Prediction Model for Species Distribution Using Bagging Ensembles with Deep Neural Networks

2021 ◽  
Vol 13 (8) ◽  
pp. 1495
Author(s):  
Jehyeok Rew ◽  
Yongjang Cho ◽  
Eenjun Hwang
Keyword(s):  
Neural Networks ◽  
Species Distribution ◽  
Best Practice ◽  
Deep Neural Networks ◽  
Species Distribution Models ◽  
Species Distribution Model ◽  
Environmental Data ◽  
Distribution Model ◽  
Distribution Models ◽  
Robust Prediction

Species distribution models have been used for various purposes, such as conserving species, discovering potential habitats, and obtaining evolutionary insights by predicting species occurrence. Many statistical and machine-learning-based approaches have been proposed to construct effective species distribution models, but with limited success due to spatial biases in presences and imbalanced presence-absences. We propose a novel species distribution model to address these problems based on bootstrap aggregating (bagging) ensembles of deep neural networks (DNNs). We first generate bootstraps considering presence-absence data on spatial balance to alleviate the bias problem. Then we construct DNNs using environmental data from presence and absence locations, and finally combine these into an ensemble model using three voting methods to improve prediction accuracy. Extensive experiments verified the proposed model’s effectiveness for species in South Korea using crowdsourced observations that have spatial biases. The proposed model achieved more accurate and robust prediction results than the current best practice models.

scholarly journals Investigating impact of land-use and land cover changes on hydro-ecological balance using GIS: insights from IIT Bombay, India

2021 ◽  
Vol 3 (3) ◽  
Author(s):  
Aman Srivastava ◽  
Pennan Chinnasamy
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Surface Runoff ◽  
Forest Cover ◽  
Google Earth ◽  
Negative Consequences ◽  
Ecological Stability ◽  
Barren Land ◽  
Ecological Balance ◽  
Lulc Changes

AbstractThe present study, for the first time, examined land-use land cover (LULC), changes using GIS, between 2000 and 2018 for the IIT Bombay campus, India. Objective was to evaluate hydro-ecological balance inside campus by determining spatio-temporal disparity between hydrological parameters (rainfall-runoff processes), ecological components (forest, vegetation, lake, barren land), and anthropogenic stressors (urbanization and encroachments). High-resolution satellite imageries were generated for the campus using Google Earth Pro, by manual supervised classification method. Rainfall patterns were studied using secondary data sources, and surface runoff was estimated using SCS-CN method. Additionally, reconnaissance surveys, ground-truthing, and qualitative investigations were conducted to validate LULC changes and hydro-ecological stability. LULC of 2018 showed forest, having an area cover of 52%, as the most dominating land use followed by built-up (43%). Results indicated that the area under built-up increased by 40% and playground by 7%. Despite rapid construction activities, forest cover and Powai lake remained unaffected. This anomaly was attributed to the drastically declining barren land area (up to ~ 98%) encompassing additional construction activities. Sustainability of the campus was demonstrated with appropriate measures undertaken to mitigate negative consequences of unwarranted floods owing to the rise of 6% in the forest cover and a decline of 21% in water hyacinth cover over Powai lake. Due to this, surface runoff (~ 61% of the rainfall) was observed approximately consistent and being managed appropriately despite major alterations in the LULC. Study concluded that systematic campus design with effective implementation of green initiatives can maintain a hydro-ecological balance without distressing the environmental services.

scholarly journals A Bioclimate-Based Maximum Entropy Model for Comperiella calauanica Barrion, Almarinez and Amalin (Hymenoptera: Encyrtidae) in the Philippines

Insects ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 26
Author(s):  
Billy Joel M. Almarinez ◽  
Mary Jane A. Fadri ◽  
Richard Lasina ◽  
Mary Angelique A. Tavera ◽  
Thaddeus M. Carvajal ◽  
...  
Keyword(s):  
Maximum Entropy ◽  
Habitat Suitability ◽  
Species Distribution Model ◽  
Biological Control Agent ◽  
The Philippines ◽  
Distribution Model ◽  
Field Surveys ◽  
Probability Of Occurrence ◽  
Precipitation Seasonality ◽  
Bioclimatic Variables

Comperiella calauanica is a host-specific endoparasitoid and effective biological control agent of the diaspidid Aspidiotus rigidus, whose outbreak from 2010 to 2015 severely threatened the coconut industry in the Philippines. Using the maximum entropy (Maxent) algorithm, we developed a species distribution model (SDM) for C. calauanica based on 19 bioclimatic variables, using occurrence data obtained mostly from field surveys conducted in A. rigidus-infested areas in Luzon Island from 2014 to 2016. The calculated the area under the ROC curve (AUC) values for the model were very high (0.966, standard deviation = 0.005), indicating the model’s high predictive power. Precipitation seasonality was found to have the highest relative contribution to model development. Response curves produced by Maxent suggested the positive influence of mean temperature of the driest quarter, and negative influence of precipitation of the driest and coldest quarters on habitat suitability. Given that C. calauanica has been found to always occur with A. rigidus in Luzon Island due to high host-specificity, the SDM for the parasitoid may also be considered and used as a predictive model for its host. This was confirmed through field surveys conducted between late 2016 and early 2018, which found and confirmed the occurrence of A. rigidus in three areas predicted by the SDM to have moderate to high habitat suitability or probability of occurrence of C. calauanica: Zamboanga City in Mindanao; Isabela City in Basilan Island; and Tablas Island in Romblon. This validation in the field demonstrated the utility of the bioclimate-based SDM for C. calauanica in predicting habitat suitability or probability of occurrence of A. rigidus in the Philippines.

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