Constructing a hybrid species distribution model from standard large-scale distribution data

2018 ◽  
Vol 373 ◽  
pp. 39-52 ◽  
Author(s):  
Alexander Singer ◽  
Oliver Schweiger ◽  
Ingolf Kühn ◽  
Karin Johst
Keyword(s):  
Species Distribution ◽  
Large Scale ◽  
Species Distribution Model ◽  
Distribution Model ◽  
Distribution Data ◽  
Hybrid Species

scholarly journals Evaluation of species distribution models for estimating animal dark diversity

2021 ◽  
Author(s):  
Camilo Matus-Olivares ◽  
Jaime Carrasco ◽  
José Luis Yela ◽  
Paula Meli ◽  
Andres Weintraub ◽  
...  
Keyword(s):  
Spatial Patterns ◽  
Species Distribution ◽  
Species Distribution Model ◽  
Predictive Performance ◽  
The Other ◽  
Distribution Model ◽  
Distribution Data ◽  
Ensemble Model ◽  
Distribution Models ◽  
Software Environment

Abstract Aim Applying wide and effective sampling of animal communities is rarely possible due to the associated costs and the use of techniques that are not always efficient. Thus, many areas have a faunistic hidden diversity we denote Animal Dark Diversity (ADD), defined as the diversity that is present but not yet detected plus the diversity defined by Pärtel et al. (2011) that is not (yet) present despite the area’s favourable habitat conditions. We evaluated different species distribution model types (SDM techniques) on the basis of three requirements for ADD estimate reliability: 1) estimated spatial patterns of ADD do not differ significantly from other SDM techniques; 2) good predictive performances; and 3) low overfitting. Location Iberian Peninsula. Taxon Chiroptera and Noctuoidea (Lepidoptera) Methods We used distribution data for 25 species of bats and 352 species of moths. We evaluated eleven SDM techniques using biomod2 package implemented in the R software environment. We fitted the various SDM techniques to the data for each species and compared the resulting ADD estimates for the two animal groups under three threshold types. Results The results demonstrated that estimated ADD spatial patterns vary significantly between SDM techniques and depend on the threshold type. They also showed that SDM techniques with overfitting tend to generate smaller ADD sizes, thus reducing the possible species presence estimates. Among the SDMs studied, the ensemble models delivered ADD geographic patterns more like the other techniques while also presenting a high predictive performance for both faunal groups. However, the Ensemble Model Committee Average (ECA) performed much better on the sensitivity metric than all other techniques under any of the thresholds tested. In addition, ECA stood out clearly from the other ensemble model techniques in displaying low-medium overfitting. Main conclusions SDM techniques should no differ among each other in their ADD estimations, have good predictive performances and exhibit low overfitting. Furthermore, to reduce estimate uncertainty it is suggested that the threshold type be one that transforms high values of presences probabilities into binary information and furthermore that the SDM technique have a sensitivity bias, as otherwise the estimates will perform better for species absence in cases where it is not in fact known whether a species is truly absent.

scholarly journals Quantification of land-sea nutrient fluxes supplied by allis shad across the species' range

2021 ◽  
Author(s):  
Camille Poulet ◽  
Betsy L. Barber-O'Malley ◽  
Géraldine Lassalle ◽  
Patrick Lambert
Keyword(s):  
Ecosystem Services ◽  
Species Distribution ◽  
Large Scale ◽  
Western Europe ◽  
Species Distribution Model ◽  
Distribution Model ◽  
Nutrient Loads ◽  
Allis Shad ◽  
Distribution Ranges ◽  
Nutrient Subsidies

Diadromous species act as nutrient vectors between their marine and freshwater habitats. Few valuations of this regulating service exist and none at the scale of species distribution ranges. This large-scale approach seems particularly relevant for species moving and exchanging individuals across borders and territories as these populations may strongly depend upon each other in terms of population viability and provision of ecosystem services. The development of a new nutrient routine within an existing mechanistic species distribution model provided estimates of the 'maximum potential' of the anadromous allis shad (Alosa alosa) to provide nitrogen and phosphorous subsidies throughout Western Europe. During their seasonal reproductive migration, shad provided low amounts of nutrient subsidies when compared to North-American anadromous species and annual riverine nutrient loads. However, these subsidies are delivered as pulses concentrated in space and time, suggesting that more work is needed to figure out the significance of these shad-derived nutrients in terms of riverine ecosystem functioning. The evidence of a substantial flow of strayers delivering nutrient subsidies in several rivers confirmed the need for large-scale management of migratory species to ensure a sustainable provision of ecosystem services.

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 The impact of data quality filtering of opportunistic citizen science data on species distribution model performance

2021 ◽  
Vol 444 ◽  
pp. 109453
Author(s):  
Camille Van Eupen ◽  
Dirk Maes ◽  
Marc Herremans ◽  
Kristijn R.R. Swinnen ◽  
Ben Somers ◽  
...  
Keyword(s):  
Data Quality ◽  
Citizen Science ◽  
Species Distribution ◽  
Species Distribution Model ◽  
Model Performance ◽  
Distribution Model ◽  
Science Data ◽  
Quality Filtering ◽  
The Impact

scholarly journals Application Natura 2000 Data For The Invasive Plants Spread Prediction

2015 ◽  
Vol 46 (4) ◽  
pp. 159-166 ◽  
Author(s):  
J. Pěknicová ◽  
D. Petrus ◽  
K. Berchová-Bímová
Keyword(s):  
Environmental Factors ◽  
Invasive Plants ◽  
Species Distribution ◽  
Natura 2000 ◽  
Distribution Model ◽  
Distribution Data ◽  
Habitat Types ◽  
Distribution Models ◽  
Occurrence Data ◽  
Heracleum Mantegazzianum

AbstractThe distribution of invasive plants depends on several environmental factors, e.g. on the distance from the vector of spreading, invaded community composition, land-use, etc. The species distribution models, a research tool for invasive plants spread prediction, involve the combination of environmental factors, occurrence data, and statistical approach. For the construction of the presented distribution model, the occurrence data on invasive plants (Solidagosp.,Fallopiasp.,Robinia pseudoaccacia,andHeracleum mantegazzianum) and Natura 2000 habitat types from the Protected Landscape Area Kokořínsko have been intersected in ArcGIS and statistically analyzed. The data analysis was focused on (1) verification of the accuracy of the Natura 2000 habitat map layer, and the accordance with the habitats occupied by invasive species and (2) identification of a suitable scale of intersection between the habitat and species distribution. Data suitability was evaluated for the construction of the model on local scale. Based on the data, the invaded habitat types were described and the optimal scale grid was evaluated. The results show the suitability of Natura 2000 habitat types for modelling, however more input data (e.g. on soil types, elevation) are needed.

Sign in / Sign up

Export Citation Format

Share Document