Climate, soil or both? Which variables are better predictors of the distributions of Australian shrub species?

BackgroundShrubs play a key role in biogeochemical cycles, prevent soil and water erosion, provide forage for livestock, and are a source of food, wood and non-wood products. However, despite their ecological and societal importance, the influence of different environmental variables on shrub distributions remains unclear. We evaluated the influence of climate and soil characteristics, and whether including soil variables improved the performance of a species distribution model (SDM), Maxent.MethodsThis study assessed variation in predictions of environmental suitability for 29 Australian shrub species (representing dominant members of six shrubland classes) due to the use of alternative sets of predictor variables. Models were calibrated with (1) climate variables only, (2) climate and soil variables, and (3) soil variables only.ResultsThe predictive power of SDMs differed substantially across species, but generally models calibrated with both climate and soil data performed better than those calibrated only with climate variables. Models calibrated solely with soil variables were the least accurate. We found regional differences in potential shrub species richness across Australia due to the use of different sets of variables.ConclusionsOur study provides evidence that predicted patterns of species richness may be sensitive to the choice of predictor set when multiple, plausible alternatives exist, and demonstrates the importance of considering soil properties when modeling availability of habitat for plants.

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Mapping the Potential Distribution of Oak Wilt (Bretziella fagacearum) in East Central and Southeast Minnesota Using Maxent

Journal of Forestry ◽

10.1093/jofore/fvz053 ◽

2019 ◽

Vol 117 (6) ◽

pp. 579-591 ◽

Cited By ~ 1

Author(s):

Melissa Gearman ◽

Mikhail S Blinnikov

Keyword(s):

Land Cover ◽

Species Distribution Model ◽

Distribution Model ◽

Oak Wilt ◽

East Central ◽

Jackknife Test ◽

Forest Pathogen ◽

Sensing Technology ◽

Forest Diseases ◽

Better Than

Abstract With the advancement of spatial analysis and remote sensing technology, potentially devastating forest pathogens can be managed through spatial modeling. This study used Maxent, a presence-only species-distribution model, to map the potential probability distribution of the invasive forest pathogen oak wilt (Bretziella fagacearum) in eastern and southeastern Minnesota. The model related oak wilt occurrence data to environmental variables including climate, topography, land cover, soil, and population density. Results showed that areas with the highest probability of oak wilt occur within and surrounding the Minneapolis/St. Paul metropolitan area. The jackknife test of variable importance indicated land cover and soil type as important variables contributing to the prediction of the distribution. Multiple methods of analysis showed that the model performed better than random at predicting the occurrence of oak wilt. This study shows Maxent’s potential as an accurate tool in the early detection and management of forest diseases.

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Evaluating the influence of spatially varying catchability on multispecies distribution modelling

ICES Journal of Marine Science ◽

10.1093/icesjms/fsaa068 ◽

2020 ◽

Vol 77 (5) ◽

pp. 1841-1853

Author(s):

Chongliang Zhang ◽

Yong Chen ◽

Binduo Xu ◽

Ying Xue ◽

Yiping Ren

Keyword(s):

Species Distribution ◽

Predictive Power ◽

Species Distribution Model ◽

Distribution Model ◽

Distribution Models ◽

Distribution Modelling ◽

Species Response ◽

Challenges And Opportunities ◽

Empirical Tests ◽

Spatially Varying

Abstract Varying catchability is a common feature in fisheries and has great impacts on fisheries assessments and species distribution models. However, spatial variations in catchability have been rarely evaluated, especially in the multispecies context. We advocate that the need for multispecies models stands for both challenges and opportunities to handle spatial catchability. This study evaluated the influence of spatially varying catchability on the performance of a novel joint species distribution model, namely Hierarchical Modelling of Species Communities (HMSC). We implemented the model under nine simulation scenarios to account for diverse spatial patterns of catchability and conducted empirical tests using survey data from Yellow Sea, China. Our results showed that ignoring variability in catchability could lead to substantial errors in the inferences of species response to environment. Meanwhile, the models’ predictive power was less impacted, yielding proper predictions of relative abundance. Incorporating a spatially autocorrelated structure substantially improved the predictability of HMSC in both simulation and empirical tests. Nevertheless, combined sources of spatial catchabilities could largely diminish the advantage of HMSC in inference and prediction. We highlight situations where catchability needs to be explicitly accounted for in modelling fish distributions, and suggest directions for future applications and development of JSDMs.

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Shrub richness is primarily driven by climate conditions in Southwestern European woodlands

Annals of Forest Science ◽

10.1007/s13595-021-01117-5 ◽

2021 ◽

Vol 78 (4) ◽

Author(s):

Daniel Moreno-Fernández ◽

Isabel Cañellas ◽

Iciar Alberdi

Keyword(s):

Species Richness ◽

Mediterranean Basin ◽

Boosted Regression Trees ◽

Climatic Variables ◽

Relative Importance ◽

Forest Types ◽

Shrub Species ◽

Soil Variables ◽

The Mediterranean ◽

The Mediterranean Basin

Abstract • Key message The shrub species richness in Spanish forests is mainly linked to climatic variables and the importance of the groups of variables scarcely differs among forest types. Forest surrounding the Mediterranean Basin exhibit the highest levels of shrub richness. • Context Shrub species account for a high proportion of the plant diversity in Spanish forests and are a determinant factor in forest dynamics and ecosystem functionality. • Aims To investigate the relative importance of climatic, forest stand features, soil and topographic variables in explaining shrub richness in Spanish forests and if the relative importance of these four groups of variables reflects variations among forest types. • Methods We used the Spanish National Forest Inventory and a boosted regression trees approach to identify which climatic, soil, stand and topographic variables (N = 19 variables) are related to the richness of shrub species in Spanish woodlands. • Results The shrub species richness is mainly related to climatic variables followed by soil variables whereas stand and topographic variables play a minor role. The importance of the groups of variables scarcely differs among forest types although forests located around the Mediterranean Sea display the highest levels of shrub richness. • Conclusion Shrub richness in Spain is primarily driven by climatic and soil variables, both at country and forest-type scales. Forests surrounding the Mediterranean Basin account for the highest richness of shrub species but are also those most threatened by global change. Therefore, special attention must be paid to the monitoring and assessment of these forest ecosystems.

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A triad of kicknet sampling, eDNA metabarcoding, and predictive modeling to assess aquatic macroinvertebrate biodiversity

10.1101/2022.01.03.474789 ◽

2022 ◽

Author(s):

François Keck ◽

Samuel Hürlemann ◽

Nadine Locher ◽

Christian Stamm ◽

Kristy Deiner ◽

...

Keyword(s):

Species Richness ◽

Community Composition ◽

Species Distribution ◽

Species Distribution Model ◽

Traditional Approach ◽

Indirect Evidence ◽

Distribution Model ◽

Freshwater Biodiversity ◽

Distribution Models ◽

Aquatic Macroinvertebrate

Monitoring freshwater biodiversity is essential to understand the impacts of human activities and for effective management of ecosystems. Thereby, biodiversity can be assessed through direct collection of targeted organisms, through indirect evidence of their presence (e.g. signs, environmental DNA, camera trap, etc.), or through extrapolations from species distribution models (SDM). Differences in approaches used in biodiversity assessment, however, may come with individual challenges and hinder cross-study comparability. In the context of rapidly developing techniques, we compared a triad of approaches in order to understand assessment of aquatic macroinvertebrate biodiversity. Specifically, we compared the community composition and species richness of three orders of aquatic macroinvertebrates (mayflies, stoneflies, and caddisflies, hereafter EPT) obtained via eDNA metabarcoding and via traditional in situ kicknet sampling to catchment-level based predictions of a species distribution model. We used kicknet data from 24 sites in Switzerland and compared taxonomic lists to those obtained using eDNA amplified with two different primer sets. Richness detected by these methods was compared to the independent predictions made by a statistical species distribution model using landscape-level features to estimate EPT diversity. Despite the ability of eDNA to consistently detect some EPT species found by traditional sampling, we found important discrepancies in community composition between the two approaches, particularly at local scale. Overall, the more specific set of primers, namely fwhF2/EPTDr2n, was most efficient for the detection of target species and for characterizing the diversity of EPT. Moreover, we found that the species richness measured by eDNA was poorly correlated to the richness measured by kicknet sampling and that the richness estimated by eDNA and kicknet were poorly correlated with the prediction of the statistical model. Overall, however, neither eDNA nor the traditional approach had strong links to the predictive models, indicating inherent limitations in upscaling species richness estimates. Future challenges include improving the accuracy and sensitivity of each approach individually yet also acknowledge their respective limitations, in order to best meet stakeholder demands addressing the biodiversity crisis we are facing.

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A Robust Prediction Model for Species Distribution Using Bagging Ensembles with Deep Neural Networks

Remote Sensing ◽

10.3390/rs13081495 ◽

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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Improving performance of species distribution model in mountainous areas with complex topography

Ecological Research ◽

10.1111/1440-1703.12227 ◽

2021 ◽

Author(s):

Chi‐Cheng Liao ◽

Yi‐Huey Chen

Keyword(s):

Species Distribution ◽

Species Distribution Model ◽

Distribution Model ◽

Complex Topography ◽

Mountainous Areas

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A Bioclimate-Based Maximum Entropy Model for Comperiella calauanica Barrion, Almarinez and Amalin (Hymenoptera: Encyrtidae) in the Philippines

Insects ◽

10.3390/insects12010026 ◽

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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