scholarly journals Climate change impacts on migration of Pinus koraiensis during the Quaternary using species distribution models

Plant Ecology ◽  
2021 ◽  
Author(s):  
Takuto Shitara ◽  
Shunsuke Fukui ◽  
Tetsuya Matsui ◽  
Arata Momohara ◽  
Ikutaro Tsuyama ◽  
...  
Keyword(s):  
Species Distribution ◽  
Species Distribution Model ◽  
Eastern China ◽  
Disjunct Distribution ◽  
Distribution Model ◽  
Pinus Koraiensis ◽  
Japanese Archipelago ◽  
Bioclimatic Variables ◽  
The Japanese Archipelago ◽  
Suitable Habitats

AbstractClarifying the influences of paleoclimate changes on the disjunct distribution formation of plants allows a historical and mechanical understanding of current vegetation and biodiversity. This study investigated the influences of paleoclimate changes on the present disjunct distribution formation of Pinus koraiensis (Korean pine) using species distribution modeling. A species distribution model (SDM) was built using maximum entropy principle algorithms (MaxEnt), data from 152 occurrences of the species, and four bioclimatic variables at 2.5 arcminute (approximately 5 km) spatial resolution. The simulation revealed the excellent fit of the MaxEnt model performance, with an area under the curve (AUC) value of 0.922 and continuous Boyce index (BCI) value of 0.925 with fivefold cross-validation. The most important climatic factor was the minimum temperature of the coldest month. Suitable habitats for the species ranged between − 30.1 and − 4.1 °C. Projected suitable habitats under the Last Glacial Maximum (approximately 22,000 years ago [ka BP]: LGM) period showed wide distributions in eastern China, the southern part of the Korean Peninsula, and the Japanese Archipelago. After the mid-Holocene (approximately 6 ka BP), the suitable habitats expanded northwards in continental regions and retreated from both north and southwest of Japan. This eventually formed disjunct suitable habitats in central Japan. An increase in temperature after the LGM period caused the migration of P. koraiensis toward new, suitable habitats in continental Northeast Asia, while species in the Japanese Archipelago retreated, forming the present disjunct distributions.

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 Is a species distribution model output reliable to test niche limitation at the northern range limit of a Pacific coastal dune plant?

2021 ◽  
Author(s):  
Jacqueline Grubel
Keyword(s):  
Species Distribution ◽  
Species Distribution Model ◽  
Range Limits ◽  
Distribution Model ◽  
Range Limit ◽  
Endemic Plant ◽  
Niche Limitation ◽  
Bioclimatic Variables ◽  
Northern Range Limit ◽  
Dune Plant

Jacqueline Grubel* and Christopher G. Eckert (Faculty Supporter) It is widely thought that the size, shape and location of a species’ geographical distribution are a spatial expression of its realized niche, and this assumption is central to evolutionary biology, biogeography and conservation. Yet, the hypothesis that geographical range limits are niche limits is not well supported by experimental translocations of species beyond their range limits. Beyond range populations often exhibit fitness high enough for self-replacement. In contrast, environmental niche models based on bioclimatic data often suggest a decline in habitat suitability beyond range limits, thereby supporting niche limitation. However very few studies have evaluated whether species distribution models (SDMs) accurately predict the viability of populations in nature, and scant results to date are not supportive. Long-term transplant with the short-lived, Pacific costal dune endemic plant Camissoniopsis cheiranthifolia (Onagraceae) suggest that populations are viable beyond the northern range limit over multiple generations. We constructed an SDM based on a large range-wide database of species records plus standard bioclimatic variables and substrate type. We also included sea surface temperature, which greatly modifies the climate of dune habitat. Preliminary results suggest that our SDM reliably predicts the fitness of experimental populations. However, both approaches indicate that something other than niche limitation enforces the northern range limit of this species. Results from this well-studied dune plant suggest that range limitation via constraints on dispersal may play an important role in limiting northern range expansion.

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.

Identifying suitable habitats and current conservation status of a rare and elusive reptile in Iran

2018 ◽  
Vol 39 (3) ◽  
pp. 355-362 ◽  
Author(s):  
Rosa M. Chefaoui ◽  
Mahboubeh Sadat Hosseinzadeh ◽  
Meysam Mashayekhi ◽  
Barbod Safaei-Mahroo ◽  
Seyed Mahdi Kazemi
Keyword(s):  
Protected Areas ◽  
Species Distribution ◽  
Species Distribution Model ◽  
Conservation Status ◽  
Distribution Model ◽  
Reliable Estimate ◽  
Current Conservation ◽  
Mountain Ranges ◽  
Maximum Entropy Modeling ◽  
Suitable Habitats

Abstract Knowledge gaps regarding species distribution and abundance are great in remote regions with political instability, and they might be even larger concerning elusive and rare species. We predict the potential distribution for Hierophis andreanus, a poorly known endemic snake in the Iranian Plateau, and assess its conservation status in relation to existing protected areas. We used a maximum entropy modeling tool and Mahalanobis distance to produce an ensemble species distribution model. The most suitable habitats where located mainly in mountain ranges and adjacent areas of Iran and Afghanistan. Mean temperature and slope were the most important predictors for our models. Furthermore, just five localities for H. andreanus were inside the Iranian protected areas. A 10 km expansion from existing boundaries of protected areas in all directions would double protected localities to 10, and a 20 km buffer would result in 13 protected localities. Our findings are particularly valuable to select locations to conduct new surveys and produce a more reliable estimate of current population size to improve conservation and management for this reptile in the Irano-Anatolian region.

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 SPECIES DISTRIBUTION MODELLING OF TWO SPECIES ENDEMIC TO THE PHILIPPINES TO SHOW THE APPLICABILITY OF MAXENT

2019 ◽  
Vol XLII-4/W19 ◽  
pp. 449-454
Author(s):  
M. Z. G. Untalan ◽  
D. F. M. Burgos ◽  
K. P. Martinez
Keyword(s):  
Species Distribution ◽  
Endemic Species ◽  
Species Distribution Model ◽  
Species Distribution Modelling ◽  
The Philippines ◽  
Distribution Model ◽  
Distribution Modelling ◽  
Machine Learning Model ◽  
Monitor Lizard ◽  
Bioclimatic Variables

Abstract. Maxent is a machine learning model used for species distribution modelling (SDM) that is rising in popularity. As with any species distribution model, it needs to be validated for certain species before being used to generate insights and trusted predictions. Using Maxent, SDM of two endemic species in the Philippines, Varanus palawanensis (Palawan monitor lizard) and Caprimulgus manillensis (Philippine nightjar), were created using presence-only data, with 14 V. palawanensis and 771 C. manillensis occurrences, and 19 bioclimatic variables from BIOCLIM. This study shows the consistency to historical facts of Maxent on two endemic species of the Philippines of varying nature. The applicability of Maxent on the two very different species show that Maxent has high likelihood to give good results for other species. Showing that Maxent is applicable to the species of the Philippines gives additional tools for ecologists and national administrators to lead the development of the Philippines in the direction that conserves the biodiversity of the Philippines and that increases the productivity and quality of life in the Philippines.

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.

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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