scholarly journals Improving the assessment and reporting on rare and endangered species through species distribution models

2014 ◽  
Vol 2 ◽  
pp. 226-237 ◽  
Author(s):  
Rita Sousa-Silva ◽  
Paulo Alves ◽  
João Honrado ◽  
Angela Lomba
Keyword(s):  
Endangered Species ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Distribution Models ◽  
Rare And Endangered Species ◽  
Rare And Endangered

scholarly journals Species distribution models for critically endangered liverworts (Bryophyta) from the Czech Republic: a guide to future survey expeditions.

2017 ◽  
Vol 66 (2) ◽  
pp. 101-110
Author(s):  
Lukáš Číhal ◽  
Oto Kaláb
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Critically Endangered ◽  
Distribution Models ◽  
Final Model ◽  
The Czech Republic ◽  
Rare And Endangered Species ◽  
Bioclimatic Variables ◽  
Rare And Endangered ◽  
Maximum Entropy Modelling

Abstract Using 35 presence-only data samples and five uncorrelated bioclimatic variables, we made species distribution models (SDMs) for 4 species of critically endangered (CR) liverworts from genus Jungermanniales and Marchantiales (Cephaloziella elegans, Leiocolea heterocolpos, Lophozia wenzelii and Riccia papillosa) using the maximum entropy modelling method (MaxEnt). Since we were modelling CR species, only one model proved to be strong enough to be used in the field. However, SDMs can serve as effective and fast tools for acceleration of the discovery of the rare and endangered species. The final model presented in this study can serve as a guide to future survey expeditions, the conservation of the target species and also to help understand their ecology.

scholarly journals How much is enough? Influence of number of presence observations on the performance of species distribution models

Sommerfeltia ◽  
2019 ◽  
Vol 39 (1) ◽  
pp. 1-28 ◽  
Author(s):  
Bente Støa ◽  
Rune Halvorsen ◽  
Jogeir N. Stokland ◽  
Vladimir I. Gusarov
Keyword(s):  
Species Distribution ◽  
Reference Model ◽  
Null Model ◽  
Species Distributions ◽  
Reference Distribution ◽  
Distribution Models ◽  
Rare And Endangered Species ◽  
Full Dataset ◽  
Presence Data ◽  
Rare And Endangered

Abstract Species distribution modeling (SDM) can be useful for many applied purposes, e.g., mapping and monitoring of rare and endangered species. Sparse presence data are a recurrent, major obstacle to precise modeling of species distributions. Thus, knowing the minimum number of presences required to obtain reliable distribution models is of fundamental importance for applied use of SDM. This study uses a novel approach to assess the critical sample size (CSS) sufficient for an accurate prediction of species distributions with Maximum Entropy Modeling (MaxEnt). Large presence datasets for thirty insect species, ranging from generalists to specialists regarding their responses to main bioclimatic gradients, were used to produce reference distribution models. Models based on replicated subsamples of different size drawn randomly from the full dataset were compared to the reference model using the index of vector similarity distribution models. Models based on replicated subsamples of different size drawn randomly from the full dataset were compared to the reference model using the index of vector similarity (IVS). Two thresholds for IVS were determined based on comparison of nine reference models to random null models. The threshold values correspond to 0.95 and 0.99 probability that a model outperforms a random null model in terms of similarity to the reference dataset. For 90% of the species, clearly nonrandom models were obtained with less than 10 presence observations, and for 97% of the species with less than 15 presence observations. We conclude that the number of presence observations required to produce nonrandom models is generally low and, accordingly, that even sparse datasets may be useful for distribution modelling.

scholarly journals A Model and Simulation of the Influence of Temperature and Amplicon Length on Environmental DNA Degradation Rates: A Meta-Analysis Approach

2021 ◽  
Vol 9 ◽  
Author(s):  
Tatsuya Saito ◽  
Hideyuki Doi
Keyword(s):  
Endangered Species ◽  
Water Temperature ◽  
Species Distribution ◽  
Degradation Rate ◽  
Meta Analysis ◽  
Environmental Dna ◽  
Rare And Endangered Species ◽  
Amplicon Length ◽  
Degradation Rates ◽  
Rare And Endangered

Environmental DNA (eDNA) analysis can detect aquatic organisms, including rare and endangered species, in a variety of habitats. Degradation can influence eDNA persistence, impacting eDNA-based species distribution and occurrence results. Previous studies have investigated degradation rates and associated contributing factors. It is important to integrate data from across these studies to better understand and synthesize eDNA degradation in various environments. We complied the eDNA degradation rates and related factors, especially water temperature and amplicon lengths of the measured DNA from 28 studies, and subjected the data to a meta-analysis. In agreement with previous studies, our results suggest that water temperature and amplicon length are significantly related to the eDNA degradation rate. From the 95% quantile model simulation, we predicted the maximum eDNA degradation rate in various combinations of water temperature and amplicon length. Predicting eDNA degradation could be important for evaluating species distribution and inducing innovation (e.g., sampling, extraction, and analysis) of eDNA methods, especially for rare and endangered species with small population size.

Genetic diversity and genetic structure of the rare and endangered species, Primula ranunculoides

2014 ◽  
Vol 21 (5) ◽  
pp. 601-609
Author(s):  
Wang Deyun ◽  
Peng Jie ◽  
Chen Yajing ◽  
Lü Guosheng ◽  
Zhang Xiaoping ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Genetic Structure ◽  
Endangered Species ◽  
Rare And Endangered Species ◽  
Rare And Endangered

Geoecological analysis of the Russian Federation regions representation in the state program “Environmental Protection”

2020 ◽  
Vol 957 (3) ◽  
pp. 47-53
Author(s):  
E.A. Kravets
Keyword(s):  
Endangered Species ◽  
Environmental Protection ◽  
Russian Federation ◽  
The State ◽  
Volga River ◽  
Unequal Distribution ◽  
State Program ◽  
Rare And Endangered Species ◽  
Rare And Endangered ◽  
The Russian Federation

The author offers mapping and geoecological analysis of the Russian Federation regions presence in the state program “Environmental Protection”. The unequal distribution of the program’s targets and activities in different regions is revealed. A considerable number of relevant environmental problems for several mentioned regions have not been reflected in the program. It is important to increase the area of specially protected natural areas for a significant number of subjects of the Russian Federation. The status “part of the territory occupied by specially protected natural territories of Federal value in the total area of the subject of the Russian Federation” is recommended to be assigned all regions of Russia. Identification and elimination of objects of accumulated environmental damage that threat to the Volga river is relevant, at least for all the regions in which the Volga flows. Not all regions with a high level of air pollution and/or large masses of air pollutants have the indicator “reduction of total emissions for the reporting year”. It is necessary to increase the Program of measures for the protection of rare and endangered species of plants and to expand the list of regions in which it is planned to protect rare and endangered species of animals.

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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