scholarly journals “Reconstructions of the past distribution of Testudo graeca mitochondrial lineages in the Middle East and Transcaucasia support multiple refugia since the Last Glacial Maximum”: A response to Turkozan et al. (2021)

2021 ◽  
pp. 201-203
Author(s):  
Flora Ihlow
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Distribution Models ◽  
Testudo Graeca ◽  
The Past ◽  
Environmental Similarity ◽  
Range Dynamics ◽  
Multiple Refugia ◽  
The Last Glacial Maximum ◽  
The Last Glacial

Species distribution models (SDMs) are frequently used to characterise current, past or future realised environmental niches. Two recent studies applied different approaches to infer range dynamics in eastern subspecies of the spur-thighed tortoise Testudo graeca Linnaeus, 1758. We discuss differences in the conclusions of the two papers and use multivariate environmental similarity surface (MESS) analyses to show that the results of the study by Turkozan et al. (2021), recently published in the Herpetological Journal, are compromised by extrapolation and therefore have to be interpreted with caution.

Reconstructions of the past distribution of Testudo graeca mitochondrial lineages in the Middle East and Transcaucasia support multiple refugia since the Last Glacial Maximum

2021 ◽  
pp. 10-17
Author(s):  
Oguz Turkozan
Keyword(s):  
Middle East ◽  
Last Glacial Maximum ◽  
Glacial Maximum ◽  
Testudo Graeca ◽  
Last Glacial ◽  
The Past ◽  
Precipitation Seasonality ◽  
Multiple Refugia ◽  
The Last Glacial Maximum ◽  
The Last Glacial

A cycle of glacial and interglacial periods in the Quaternary caused species’ ranges to expand and contract in response to climatic and environmental changes. During interglacial periods, many species expanded their distribution ranges from refugia into higher elevations and latitudes. In the present work, we projected the responses of the five lineages of Testudo graeca in the Middle East and Transcaucasia as the climate shifted from the Last Glacial Maximum (LGM, Mid – Holocene), to the present. Under the past LGM and Mid-Holocene bioclimatic conditions, models predicted relatively more suitable habitats for some of the lineages. The most significant bioclimatic variables in predicting the present and past potential distribution of clades are the precipitation of the warmest quarter for T. g. armeniaca (95.8 %), precipitation seasonality for T. g. buxtoni (85.0 %), minimum temperature of the coldest month for T. g. ibera (75.4 %), precipitation of the coldest quarter for T. g. terrestris (34.1 %), and the mean temperature of the driest quarter for T. g. zarudyni (88.8 %). Since the LGM, we hypothesise that the ranges of lineages have either expanded (T. g. ibera), contracted (T. g. zarudnyi) or remained stable (T. g. terrestris), and for other two taxa (T. g. armeniaca and T. g. buxtoni) the pattern remains unclear. Our analysis predicts multiple refugia for Testudo during the LGM and supports previous hypotheses about high lineage richness in Anatolia resulting from secondary contact.

scholarly journals Reconstructing distribution of the Eastern Rock Nuthatch during the Last Glacial Maximum and Last Interglacial

2019 ◽  
Vol 13 (1-2) ◽  
pp. 3-9
Author(s):  
Masoud Yousefi ◽  
Afshin Alizadeh Shabani ◽  
Hossein Azarnivand
Keyword(s):  
Last Glacial Maximum ◽  
Species Distribution ◽  
Current Distribution ◽  
Glacial Maximum ◽  
Zagros Mountains ◽  
Last Interglacial ◽  
Distribution Models ◽  
Last Glacial ◽  
The Last Glacial Maximum ◽  
The Last Glacial

Species distribution models have many applications in ecology, conservation, biogeography, and even paleoecology. In this study, we modeled the distribution of the Eastern Rock Nuthatch ( Sitta tephronota), a common rock dweller bird in Iranian Plateau, and determined most important climatic variables affecting the distribution of the species. We then projected the species distribution into the past, the Last Glacial Maximum (21,000 yr BP) and Last Interglacial (~120,000– 140,000 yr BP), to investigate how the species’ range would have changed through time. Results indicated that Zagros Mountains, Alborz Mountains and Kopet Dagh Mountains in the northeast of Iran are the most suitable habitats for the Eastern Rock Nuthatch. Annual mean temperature and annual precipitation identified as the most important variables in predicting the distribution of this species. During the Last Glacial Maximum, potential distribution of Eastern Rock Nuthatch was larger from its current distribution; however, the species’ climatic niche remains relatively stable since the Last Glacial Maximum. Our results also showed that during the Last Interglacial, distribution of the Eastern Rock Nuthatch was restricted to high elevations and was very different compared to its current distribution.

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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