scholarly journals Defining Conservation Requirements for the Suweon Treefrog (Dryophytes suweonensis) Using Species Distribution Models

Diversity ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 69
Author(s):  
Il-Kook Park ◽  
Daesik Park ◽  
Amaël Borzée
Keyword(s):  
Habitat Loss ◽  
Species Distribution ◽  
Species Distribution Model ◽  
Rice Paddy ◽  
Habitat Characteristics ◽  
Distribution Model ◽  
Suitable Habitat ◽  
Rice Paddies ◽  
Agricultural Wetlands ◽  
North East

Numerous amphibian species are declining because of habitat loss and fragmentation due to urbanization of landscapes and the construction of roads. This is a mounting threat to species restricted to habitats close to urban areas, such as agricultural wetlands in North East Asia. The Suweon treefrog (Dryophytes suweonensis) falls into the list of species threatened with habitat loss and most populations are under threat of extirpation. Over the last decades, sub-populations have become increasingly disconnected and specifically the density of paved roads has increased around the only site connecting northern and southern Seoul populations. We surveyed this locality in Hojobeol, Siheung, Republic of Korea in 2012, 2015 and 2019 to first confirm the decline in the number of sites where D. suweonensis was present. The second objective was to analyze the habitat characteristics and determine the remaining suitable habitat for D. suweonensis through a species distribution model following the maximum entropy method. Our results show that rice paddy cover and distance from the paved road are the most important factor defining suitable habitat for D. suweonensis. At this locality, uninterrupted rice paddies are a suitable habitat for the species when reaching at least 0.19 km2, with an average distance of 138 ± 93 m2 from the roads. We link the decrease in the number of sites where D. suweonensis is present with the decrease in rice paddy cover, generally replaced by localized infrastructures, greenhouses and habitat fragmentation. Rice paddies should remain connected over a large area for the protection of the remaining populations. In addition, habitat requirements should be integrated in the requisites to designate protected areas.

Species Distribution Models (SDM) – A Strategic Tool for Predicting Suitable Habitats for Conserving the Target Species

2017 ◽  
pp. 427-440
Author(s):  
Balaguru Balakrishnan ◽  
Nagamurugan Nandakumar ◽  
Soosairaj Sebastin ◽  
Khaleel Ahamed Abdul Kareem
Keyword(s):  
Species Distribution ◽  
Spatial Data ◽  
Environmental Variables ◽  
Species Distribution Models ◽  
Species Distribution Model ◽  
Distribution Model ◽  
Suitable Habitat ◽  
Target Species ◽  
Distribution Models ◽  
Suitable Habitats

Conservation of the species in their native landscapes required understanding patterns of spatial distribution of species and their ecological connectivity through Species Distribution Models (SDM) by generation and integration of spatial data from different sources using Geographical Information System (GIS) tools. SDM is an ecological/spatial model which combines datasets and maps of occurrence of target species and their geographical and environmental variables by linking various algorithms together, that has been applied to either identify or predict the regions fulfilling the set conditions. This article is focused on comprehensive review of spatial data requirements, statistical algorithms and softwares used to generate the SDMs. This chapter also includes a case study predicting the suitable habitat distribution of Gnetum ula, an endemic and vulnerable plant species using maximum entropy (MaxEnt) species distribution model for species occurrences with inputs from environmental variables such as bioclimate and elevation.

scholarly journals Predicting the Current and Future Suitable Habitat Distribution of Swimming Crab (Portunus trituberculatus) using the Species Distribution Model under Climate Change

2021 ◽  
Author(s):  
Xinyu Liu ◽  
Xiaolu Han ◽  
Zhiqiang Han
Keyword(s):  
Climate Change ◽  
Species Distribution ◽  
Species Distribution Model ◽  
Seawater Temperature ◽  
The Yellow Sea ◽  
Distribution Model ◽  
Portunus Trituberculatus ◽  
Suitable Habitat ◽  
Different Seasons ◽  
Rcp 8.5

Species have shown their habital variations in responding to climate change, especially during the spring and summer spawning seasons. The species distribution model (SDM) is considered the most favorable tool to study the potential effects of climate change on species distribution. Therefore, we developed the ensemble SDM to predict the changes in species distribution of Portunus trituberculatus among different seasons in 2050 and 2100 under the climate scenarios RCP4.5 and RCP8.5. The results of SDM indicate that the distribution of this species will move northward and have obviouse seasonal variations. Meanwhile, the suitable habitat for the species will be significantly reduced in summer, with loses rates ranging from 45.23% (RCP4.5) to 88.26% (RCP.8.5) by 2100s. Habitat reduction will mainly occur in the East China Sea and southern part of the Yellow Sea, while there will be a small increase in the northern Bohai Sea. These findings will be important to manage the ecosystem and fishery, provide an information forecast of this species in the future, and maintain species diversity if the seawater temperature rises.

scholarly journals Undetected but widespread: the cryptic invasion of non-native cattail (Typha spp.) in a Pacific Northwest estuary

2021 ◽  
Author(s):  
Daniel James Stewart ◽  
W. Gregory Hood ◽  
Tara G. Martin
Keyword(s):  
Cultural Values ◽  
Pacific Northwest ◽  
Species Distribution ◽  
Species Distribution Model ◽  
River Estuary ◽  
Typha Latifolia ◽  
Tidal Marshes ◽  
Distribution Model ◽  
Suitable Habitat ◽  
Hybrid Cattail

Abstract Early detection of invasive species is an important predictor of management success. Non-native narrow-leaved cattail (Typha angustifolia) has been detected in the Fraser River Estuary (FRE) in recent decades, but questions around their degree of establishment, and the potential emergence of hybrid cattail (Typha x glauca), remain unanswered. This study models the current and potential future distribution of non-native cattails in the FRE using a unique combination of spectral imagery analysis and species distribution modelling. Contrary to our expectation, we find that non-native cattails are already widespread, currently occupying approximately 4% of FRE tidal marshes. Though never formally recorded in the FRE, hybrid cattail is the more abundant of the two taxa, suggesting that heterosis may be facilitating this invasion. In our species distribution model, we distinguish between site suitability (ability to establish and persist) and site susceptibility (risk of being colonized when suitable). Our model predicts that 28% of the estuary has > 50% probability of suitability, and 21% has > 50% probability of susceptibility to non-native Typha, indicating the scale of this invasion may increase over time. Restoration projects had proportionally more cattail, susceptible habitat, and suitable habitat than the overall estuary, casting doubt on their effectiveness at mitigating wetland destruction. Due to their resemblance to native Typha latifolia, these cattails qualify as cryptic invaders, which explains how they were able to establish and remain undetected for decades. Regional eradication is unlikely given the extent of invasion, therefore management should prioritize areas of high conservation and cultural values.

Modelling suitable breeding habitat and GAP analysis for the endangered Scaly-sided Merganser Mergus squamatus: implications for conservation

2021 ◽  
pp. 1-12
Author(s):  
WENYU XU ◽  
DIANA SOLOVYEVA ◽  
SERGEY VARTANYAN ◽  
HAIFENG ZHENG ◽  
VLADIMIR PRONKEVICH ◽  
...  
Keyword(s):  
East Asia ◽  
Species Distribution Model ◽  
Gap Analysis ◽  
Deciduous Forest ◽  
Breeding Habitat ◽  
Distribution Model ◽  
Suitable Habitat ◽  
Mountainous Regions ◽  
North East ◽  
Predictive Map

Summary The Scaly-sided Merganser Mergus squamatus is a globally ‘Endangered’ species breeding in north-east Asia. Limited by information on the geographic distribution of suitable habitat, the conservation management programme has not been comprehensive or spatially explicit for the breeding population. This study combines potentially important environmental variables with extensive data on species occurrence to create the first species distribution model for the breeding Scaly-sided Merganser, followed by a GAP analysis to highlight the unprotected areas containing suitable habitat. The predictive map showing the most suitable breeding habitat for the Scaly-sided Merganser covered broad-leaved deciduous forest distributed in six provincial regions in south-east Russia, north-east China, and North Korea. The conservation GAP, i.e. 90% (38,813 km2) of highly suitable habitat, is mainly concentrated in the Sikhote-Alin and Changbai mountain ranges. This study suggests that prioritizing conservation of unprotected broad-leaved deciduous riverine forests within the above two mountainous regions should be included in international conservation planning, and the remaining suitable patches need to be preserved to allow range expansion in future. This predictive map improves the expert global assessment of breeding Scaly-sided Merganser distribution and provides a basic reference for establishing conservation areas or implementing conservation actions for the breeding Scaly-sided Merganser in north-east Asia.

Species Distribution Models (SDM) – A Strategic Tool for Predicting Suitable Habitats for Conserving the Target Species

2019 ◽  
pp. 555-568
Author(s):  
Balaguru Balakrishnan ◽  
Nagamurugan Nandakumar ◽  
Soosairaj Sebastin ◽  
Khaleel Ahamed Abdul Kareem
Keyword(s):  
Species Distribution ◽  
Spatial Data ◽  
Environmental Variables ◽  
Species Distribution Models ◽  
Species Distribution Model ◽  
Distribution Model ◽  
Suitable Habitat ◽  
Target Species ◽  
Distribution Models ◽  
Suitable Habitats

Conservation of the species in their native landscapes required understanding patterns of spatial distribution of species and their ecological connectivity through Species Distribution Models (SDM) by generation and integration of spatial data from different sources using Geographical Information System (GIS) tools. SDM is an ecological/spatial model which combines datasets and maps of occurrence of target species and their geographical and environmental variables by linking various algorithms together, that has been applied to either identify or predict the regions fulfilling the set conditions. This article is focused on comprehensive review of spatial data requirements, statistical algorithms and softwares used to generate the SDMs. This chapter also includes a case study predicting the suitable habitat distribution of Gnetum ula, an endemic and vulnerable plant species using maximum entropy (MaxEnt) species distribution model for species occurrences with inputs from environmental variables such as bioclimate and elevation.

scholarly journals Climate change-induced species distribution modeling in hyper-arid ecosystems

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 978
Author(s):  
Taoufik Saleh Ksiksi ◽  
Remya K. ◽  
Mohamed T. Mousa ◽  
Shima K. Al-Badi ◽  
Salama K. Al Kaabi ◽  
...  
Keyword(s):  
Climate Change ◽  
Species Distribution ◽  
Species Distribution Model ◽  
Arid Ecosystems ◽  
Distribution Model ◽  
Suitable Habitat ◽  
Climate System Model ◽  
Bioclimatic Variables ◽  
The Future ◽  
The Impact

Background: The impact of climate change on selected plant species from the hyper-arid landscape of United Arab Emirates (UAE) was assessed through modeling of their habitat suitability and distribution. Calotropis procera, Prosopis cineraria and Ziziphus spina-christi were used for this study. The specific objectives of this study were to identify the current and future (for 2050s and 2070s) suitable habitats distribution using MaxEnt, an Ecological Envelope Model. Methods: The adopted method consists of extraction of current and future bioclimatic variables together with their land use cover and elevation for the study area. MaxEnt species distribution model was then used to simulate the distribution of the selected species. The projections are simulated for the current date, the 2050s and 2070s using Community Climate System Model version 4 with representative concentration pathway RCP4.5. Results: The current distribution model of all three species evolved with a high suitable habitat towards the north eastern part of the country. For C. procera, an area of 1775 km2 is modeled under highly suitable habitat for the current year, while it is expected to increase for both 2050s and 2070s. The current high suitability of P. cinararia was around an area of 1335 km2 and the future projection revealed an increase of high suitability habitats. Z. spina-christi showed a potential area of 5083 km2 under high suitability and it might increase in the future. Conclusions: Precipitation of coldest quarter (BIO19) had the maximum contribution for all the three species under investigation.

scholarly journals Protected areas’ effectiveness under climate change: a latitudinal distribution projection of an endangered mountain ungulate along the Andes Range

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5222 ◽  
Author(s):  
Carlos Riquelme ◽  
Sergio A. Estay ◽  
Rodrigo López ◽  
Hernán Pastore ◽  
Mauricio Soto-Gamboa ◽  
...  
Keyword(s):  
Climate Change ◽  
Protected Areas ◽  
Species Distribution ◽  
Climate Models ◽  
Species Distribution Model ◽  
Global Climate Models ◽  
Distribution Model ◽  
Suitable Habitat ◽  
Climate Change Scenarios ◽  
Distribution Ranges

BackgroundClimate change is one of the greatest threats to biodiversity, pushing species to shift their distribution ranges and making existing protected areas inadequate. Estimating species distribution and potential modifications under climate change are then necessary for adjusting conservation and management plans; this is especially true for endangered species. An example of this issue is the huemul (Hippocamelus bisulcus), an endemic endangered deer from the southern Andes Range, with less than 2,000 individuals. It is distributed in fragmented populations along a 2,000 km latitudinal gradient, in Chile and Argentina. Several threats have reduced its distribution to <50% of its former range.MethodsTo estimate its potential distribution and protected areas effectiveness, we constructed a species distribution model using 2,813 huemul presence points throughout its whole distribution range, together with 19 bioclimatic layers and altitude information from Worldclim. Its current distribution was projected for years 2050 and 2070 using five different Global Climate Models estimated for scenarios representing two carbon Representative Concentration Routes (RCP)—RCP4.5 and RCP6.0.ResultsBased on current huemul habitat variables, we estimated 91,617 km2of suitable habitat. In future scenarios of climate change, there was a loss of suitable habitat due to altitudinal and latitudinal variation. Future projections showed a decrease of 59.86–60.26% for the year 2050 and 58.57–64.34% for the year 2070 according to RCP4.5 and RCP6.0, respectively. Protected areas only covered only 36.18% of the present distribution, 38.57–34.94% for the year 2050 and 30.79–31.94% for 2070 under climate change scenarios.DiscussionModeling current and future huemul distributions should allow the establishment of priority conservation areas in which to focus efforts and funds, especially areas without official protection. In this way, we can improve management in areas heavily affected by climate change to help ensure the persistence of this deer and other species under similar circumstances worldwide.

scholarly journals Using a Species Distribution Approach to Model Historic Camas (Camassia quamash) in Southern Idaho and Implications for Foraging in the Late Archaic

2020 ◽  
Author(s):  
Royce Johnson
Keyword(s):  
Pacific Northwest ◽  
Species Distribution ◽  
Species Distribution Model ◽  
Diet Breadth ◽  
Habitat Characteristics ◽  
Distribution Model ◽  
Late Archaic ◽  
Human Diet ◽  
The Pacific ◽  
Suitable Habitats

Camas (Camassia quamash) is well documented as a traditional native food source throughout the Northwestern United States and Canada. A better understanding of the historic distribution of camas in Idaho would help to distinguish root foraging in this region from the Pacific Northwest. Modern grazing, development, climate change, and other factors have decimated native camas in this region. This study uses a species distribution model (MaxEnt) to provide a well-informed geospatial projection of the historic distribution and habitat characteristics of camas in Southern Idaho. Understanding the most significant landscape and climate characteristics for camas allows us to estimate suitable habitats, and therefore the potential influence of camas on human diet breadth and mobility in the Late Archaic.

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.

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