scholarly journals A quantitative assessment of site-level factors in influencing Chukar (Alectoris chukar) introduction outcomes

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11280
Author(s):  
Austin M. Smith ◽  
Wendell P. Cropper, Jr. ◽  
Michael P. Moulton
Keyword(s):  
Species Distribution Models ◽  
Prediction Performance ◽  
Distribution Map ◽  
Distribution Models ◽  
Alectoris Chukar ◽  
Propagule Size ◽  
Game Birds ◽  
Species Establishment ◽  
Learning Ensembles ◽  
The Relationship

Chukar partridges (Alectoris chukar) are popular game birds that have been introduced throughout the world. Propagules of varying magnitudes have been used to try and establish populations into novel locations, though the relationship between propagule size and species establishment remains speculative. Previous qualitative studies argue that site-level factors are of importance when determining where to release Chukar. We utilized machine learning ensembles to evaluate bioclimatic and topographic data from native and naturalized regions to produce predictive species distribution models (SDMs) and evaluate the relationship between establishment and site-level factors for the conterminous United States. Predictions were then compared to a distribution map based on recorded occurrences to determine model prediction performance. SDM predictions scored an average of 88% accuracy and suitability favored states where Chukars were successfully introduced and are present. Our study shows that the use of quantitative models in evaluating environmental variables and that site-level factors are strong indicators of habitat suitability and species establishment.

The relationship between scale and predictor variables in species distribution models applied to conservation

2021 ◽  
Author(s):  
M. R. Oliveira ◽  
W. M. Tomas ◽  
N. M. R. Guedes ◽  
A.T. Peterson ◽  
J. K. Szabo ◽  
...  
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Predictor Variables ◽  
Distribution Models ◽  
The Relationship

scholarly journals Modelling the distribution of rare invertebrates by correcting class imbalance and spatial bias

2022 ◽  
Author(s):  
Willson B Gaul ◽  
Dinara Sadykova ◽  
Hannah J White ◽  
Lupe León-Sánchez ◽  
Paul Caplat ◽  
...  
Keyword(s):  
Species Distribution ◽  
Rare Species ◽  
Species Distribution Models ◽  
Class Imbalance ◽  
Prediction Performance ◽  
Good Prediction ◽  
Soil Arthropods ◽  
Spatial Bias ◽  
Distribution Models ◽  
Under Sampling

Aim: Soil arthropods are important decomposers and nutrient cyclers, but are poorly represented on national and international conservation Red Lists. Opportunistic biological records for soil invertebrates are often sparse, and contain few observations of rare species but a relatively large number of non-detection observations (a problem known as class imbalance). Robinson et al. (2018) proposed a method for sub-sampling non-detection data using a spatial grid to improve class balance and spatial bias in bird data. For taxa that are less intensively sampled, datasets are smaller, which poses a challenge because under-sampling data removes information. We tested whether spatial under-sampling improved prediction performance of species distribution models for millipedes, for which large datasets are not available. We also tested whether using environmental predictor variables provided additional information beyond what is captured by spatial position for predicting species distributions. Location: Island of Ireland. Methods: We tested the spatial under-sampling method of Robinson et al. (2018) by using biological records to train species distribution models of rare millipedes. Results: Using spatially under-sampled training data improved species distribution model sensitivity (true positive rate) but decreased model specificity (true negative rate). The decrease in specificity was minimal for rarer species and was accompanied by substantial increases in sensitivity. For common species, specificity decreased more, and sensitivity increased less, making spatial under-sampling most useful for rare species. Geographic coordinates were as good as or better than environmental variables for predicting distributions of two out of six species. Main Conclusions: Spatial under-sampling improved prediction performance of species distribution models for rare soil arthropod species. Spatial under-sampling was most effective for rarer species. The good prediction performance of models using geographic coordinates is promising for modeling distributions of poorly studied species for which little is known about ecological or physiological determinants of occurrence.

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.

Can species distribution models and molecular tools help unravel disjunct distribution of Rhododendron arboreum?

2021 ◽  
Vol 100 (1) ◽  
Author(s):  
Avik Ray ◽  
Rajasri Ray ◽  
Rani M. Saggere ◽  
G. Ravikanth
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Disjunct Distribution ◽  
Molecular Tools ◽  
Distribution Models ◽  
Rhododendron Arboreum

scholarly journals Continent-wide tree fecundity driven by indirect climate effects

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
James S. Clark ◽  
Robert Andrus ◽  
Melaine Aubry-Kientz ◽  
Yves Bergeron ◽  
Michal Bogdziewicz ◽  
...  
Keyword(s):  
Climate Change ◽  
Species Distribution Models ◽  
Climate Condition ◽  
Distribution Models ◽  
The West ◽  
Climate Effects ◽  
Continental Scale ◽  
Demographic Rates ◽  
Species Abundances ◽  
Meta Analyses

AbstractIndirect climate effects on tree fecundity that come through variation in size and growth (climate-condition interactions) are not currently part of models used to predict future forests. Trends in species abundances predicted from meta-analyses and species distribution models will be misleading if they depend on the conditions of individuals. Here we find from a synthesis of tree species in North America that climate-condition interactions dominate responses through two pathways, i) effects of growth that depend on climate, and ii) effects of climate that depend on tree size. Because tree fecundity first increases and then declines with size, climate change that stimulates growth promotes a shift of small trees to more fecund sizes, but the opposite can be true for large sizes. Change the depresses growth also affects fecundity. We find a biogeographic divide, with these interactions reducing fecundity in the West and increasing it in the East. Continental-scale responses of these forests are thus driven largely by indirect effects, recommending management for climate change that considers multiple demographic rates.

scholarly journals Vulnerability to climate change of a microendemic lizard species from the central Andes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
A. Laspiur ◽  
J. C. Santos ◽  
S. M. Medina ◽  
J. E. Pizarro ◽  
E. A. Sanabria ◽  
...  
Keyword(s):  
Climate Change ◽  
Species Distribution Models ◽  
Activity Patterns ◽  
Geographic Range ◽  
Behavioral Thermoregulation ◽  
Rapid Loss ◽  
Distribution Models ◽  
Lizard Species ◽  
Vulnerability To Climate Change

AbstractGiven the rapid loss of biodiversity as consequence of climate change, greater knowledge of ecophysiological and natural history traits are crucial to determine which environmental factors induce stress and drive the decline of threatened species. Liolaemus montanezi (Liolaemidae), a xeric-adapted lizard occurring only in a small geographic range in west-central Argentina, constitutes an excellent model for studies on the threats of climate change on such microendemic species. We describe field data on activity patterns, use of microhabitat, behavioral thermoregulation, and physiology to produce species distribution models (SDMs) based on climate and ecophysiological data. Liolaemus montanezi inhabits a thermally harsh environment which remarkably impacts their activity and thermoregulation. The species shows a daily bimodal pattern of activity and mostly occupies shaded microenvironments. Although the individuals thermoregulate at body temperatures below their thermal preference they avoid high-temperature microenvironments probably to avoid overheating. The population currently persists because of the important role of the habitat physiognomy and not because of niche tracking, seemingly prevented by major rivers that form boundaries of their geographic range. We found evidence of habitat opportunities in the current range and adjacent areas that will likely remain suitable to the year 2070, reinforcing the relevance of the river floodplain for the species’ avoidance of extinction.

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