scholarly journals Increasing reality of species distribution models of consumers by including its food resources

2021 ◽  
Vol 16 (3) ◽  
pp. 411-425
Author(s):  
Gabriel Preuss ◽  
André Andrian Padial
Keyword(s):  
South America ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Food Resource ◽  
Distribution Model ◽  
Distribution Models ◽  
Commission Errors ◽  
Main Variable ◽  
Suitability Maps ◽  
Syagrus Romanzoffiana

Species distribution models are not usually calibrated with biotic predictors. Our study question is: does the use of biotic predictors matter in predicting species distribution? We aim to assess the importance of biotic predictors in the output of distribution models of the Brazilian squirrel (Sciurus aestuans) throughout South America based on fruits of Syagrus romanzoffiana – the most consumed food resource. We hypothesized that the distribution model of S. aestuans using its main food resource as a biotic predictor will be more accurate in comparison with the output of the model without the biotic predictor. We built three different distribution models: (i) distribution of S. romanzoffiana; (ii) distribution of S. aestuans without biotic predictor; and (iii) distribution of S. aestuans with biotic predictor. We evaluated performance scores, number of presence pixels and concordance between suitability maps. We found that performance scores may not vary between models with different predictors, but the output map changed significantly. We also found that models with biotic predictors seem to vary less in presence pixels. Furthermore, the main variable in the distribution model was the biotic variable. We conclude that the knowledge of a species’ biology and ecology can make better predictions of species distribution models mainly by avoiding commission errors.

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 Exploring snake occurrence records: Spatial biases and marginal gains from accessible social media

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e8059 ◽  
Author(s):  
Benjamin M. Marshall ◽  
Colin T. Strine
Keyword(s):  
Social Media ◽  
North America ◽  
South America ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Conservation Status ◽  
Model Performance ◽  
Distribution Models ◽  
Occurrence Data ◽  
Occurrence Records

A species’ distribution provides fundamental information on: climatic niche, biogeography, and conservation status. Species distribution models often use occurrence records from biodiversity databases, subject to spatial and taxonomic biases. Deficiencies in occurrence data can lead to incomplete species distribution estimates. We can incorporate other data sources to supplement occurrence datasets. The general public is creating (via GPS-enabled cameras to photograph wildlife) incidental occurrence records that may present an opportunity to improve species distribution models. We investigated (1) occurrence data of a cryptic group of animals: non-marine snakes, in a biodiversity database (Global Biodiversity Information Facility (GBIF)) and determined (2) whether incidental occurrence records extracted from geo-tagged social media images (Flickr) could improve distribution models for 18 tropical snake species. We provide R code to search for and extract data from images using Flickr’s API. We show the biodiversity database’s 302,386 records disproportionately originate from North America, Europe and Oceania (250,063, 82.7%), with substantial gaps in tropical areas that host the highest snake diversity. North America, Europe and Oceania averaged several hundred records per species; whereas Asia, Africa and South America averaged less than 35 per species. Occurrence density showed similar patterns; Asia, Africa and South America have roughly ten-fold fewer records per 100 km2than other regions. Social media provided 44,687 potential records. However, including them in distribution models only marginally impacted niche estimations; niche overlap indices were consistently over 0.9. Similarly, we show negligible differences in Maxent model performance between models trained using GBIF-only and Flickr-supplemented datasets. Model performance appeared dependent on species, rather than number of occurrences or training dataset. We suggest that for tropical snakes, accessible social media currently fails to deliver appreciable benefits for estimating species distributions; but due to the variation between species and the rapid growth in social media data, may still be worth considering in future contexts.

scholarly journals Community Confounding In Joint Species Distribution Models

2021 ◽  
Author(s):  
Justin J. Van Ee ◽  
Jacob S. Ivan ◽  
Mevin B. Hooten
Keyword(s):  
Bark Beetle ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Species Distribution Model ◽  
Management Strategies ◽  
Single Species ◽  
Distribution Model ◽  
Subalpine Forest ◽  
Community Effects ◽  
Distribution Models

Abstract Joint species distribution models have become ubiquitous for studying species-habitat relationships and dependence among species. Accounting for community structure often improves predictive power, but can also alter inference on species-habitat relationships. Modulated species-habitat relationships are indicative of community confounding: The situation in which interspecies dependence and habitat effects compete to explain species distributions. We discuss community confounding in a case study of mammalian responses to the Colorado bark beetle epidemic in the subalpine forest by comparing the inference from independent single species distribution models and a joint species distribution model. We present a method for measuring community confounding and develop a restricted version of our hierarchical model that orthogonalizes the habitat and species random effects. Our results indicate that variables associated with the severity and duration of the bark beetle epidemic suffer from community confounding. This implies that mammalian responses to the bark beetle epidemic are governed by interconnected habitat and community effects. Disentangling habitat and community effects can improve our understanding of the ecological system and possible management strategies. We evaluate restricted regression as a method for alleviating community confounding and distinguish it from other inferential methods for confounded models.

Species Distribution Models (SDM) – A Strategic Tool for Predicting Suitable Habitats for Conserving the Target Species

2017 ◽  
pp. 427-440
Author(s):  
Balaguru Balakrishnan ◽  
Nagamurugan Nandakumar ◽  
Soosairaj Sebastin ◽  
Khaleel Ahamed Abdul Kareem
Keyword(s):  
Species Distribution ◽  
Spatial Data ◽  
Environmental Variables ◽  
Species Distribution Models ◽  
Species Distribution Model ◽  
Distribution Model ◽  
Suitable Habitat ◽  
Target Species ◽  
Distribution Models ◽  
Suitable Habitats

Conservation of the species in their native landscapes required understanding patterns of spatial distribution of species and their ecological connectivity through Species Distribution Models (SDM) by generation and integration of spatial data from different sources using Geographical Information System (GIS) tools. SDM is an ecological/spatial model which combines datasets and maps of occurrence of target species and their geographical and environmental variables by linking various algorithms together, that has been applied to either identify or predict the regions fulfilling the set conditions. This article is focused on comprehensive review of spatial data requirements, statistical algorithms and softwares used to generate the SDMs. This chapter also includes a case study predicting the suitable habitat distribution of Gnetum ula, an endemic and vulnerable plant species using maximum entropy (MaxEnt) species distribution model for species occurrences with inputs from environmental variables such as bioclimate and elevation.

scholarly journals Modelling Beach Litter Accumulation on Mediterranean Coastal Landscapes: An Integrative Framework Using Species Distribution Models

Land ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 54
Author(s):  
Mirko Di Febbraro ◽  
Ludovico Frate ◽  
Maria Carla de Francesco ◽  
Angela Stanisci ◽  
Francesco Pio Tozzi ◽  
...  
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Abiotic Factors ◽  
Sand Dunes ◽  
Anthropogenic Sources ◽  
Distribution Model ◽  
Distribution Models ◽  
Litter Accumulation ◽  
Material Type ◽  
Beach Litter

Beach litter accumulation patterns are influenced by biotic and abiotic factors, as well as by the distribution of anthropogenic sources. Although the importance of comprehensive approaches to deal with anthropogenic litter pollution is acknowledged, integrated studies including geomorphologic, biotic, and anthropic factors in relation to beach debris accumulation are still needed. In this perspective, Species Distribution Models (SDMs) might represent an appropriate tool to predict litter accumulation probability in relation to environmental conditions. In this context, we explored the applicability of a SDM–type modelling approach (a Litter Distribution Model; LDM) to map litter accumulation in coastal sand dunes. Starting from 180 litter sampling plots combined with fine–resolution variables, we calibrated LDMs from litter items classified either by their material type or origin. We also mapped litter accumulation hotspots. LDMs achieved fair-to-good predictive performance, with LDMs for litter classified by material type performing significantly better than models for litter classified by origin. Accumulation hotspots were mostly localized along the beach, by beach accesses, and at river mouths. In light of the promising results achieved by LDMs in this study, we conclude that this tool can be successfully applied within a coastal litter management context.

scholarly journals Evaluating performance of four species distribution models using Blue-tailed Green Darner Anax guttatus (Insecta: Odonata) as model organism from the Gangetic riparian zone

2020 ◽  
Vol 12 (14) ◽  
pp. 16962-16970
Author(s):  
Kritish De ◽  
S. Zeeshan Ali ◽  
Niladri Dasgpta ◽  
Virendra Prasad Uniyal ◽  
Jeyaraj Antony Johnson ◽  
...  
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Riparian Zone ◽  
Distribution Model ◽  
Support Vector ◽  
Commission Error ◽  
Distribution Models ◽  
Area Of Interest ◽  
Mean Temperature ◽  
Precipitation Seasonality

In this paper we evaluated the performance of four species distribution models: generalized linear (GLM), maximum entropy (MAXENT), random forest (RF) and support vector machines (SVM) model, using the distribution of the dragonfly Blue-tailed Green Darner Anax guttatus in the Gangetic riparian zone between Bijnor and Kanpur barrage, Uttar Pradesh, India.  We used forest cover type, land use, land cover and five bioclimatic variable layers: annual mean temperature, isothermality, temperature seasonality, mean temperature of driest quarter, and precipitation seasonality to build the models.  We found that the GLM generated the highest values for AUC, Kappa statistic, TSS, specificity and sensitivity, and the lowest values for omission error and commission error, while the MAXENT model generated the lowest variance in variable importance. We suggest that researchers should not rely on any single algorithm, instead, they should test performance of all available models for their species and area of interest, and choose the best one to build a species distribution model.  

scholarly journals Not so Normal Normals: Species Distribution Model Results are Sensitive to Choice of Climate Normals and Model Type

Climate ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 37 ◽  
Author(s):  
Catherine Jarnevich ◽  
Nicholas Young
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Species Distribution Model ◽  
Distribution Model ◽  
Model Parameters ◽  
Climate Data ◽  
Distribution Models ◽  
Model Type ◽  
Time Periods ◽  
The Impact

Species distribution models have many applications in conservation and ecology, and climate data are frequently a key driver of these models. Often, correlative modeling approaches are developed with readily available climate data; however, the impacts of the choice of climate normals is rarely considered. Here, we produced species distribution models for five disparate species using four different modeling algorithms and compared results between two different, but overlapping, climate normals time periods. Although the correlation structure among climate predictors did not change between the time periods, model results were sensitive to both baseline climate period and model method, even with model parameters specifically tuned to a species. Each species and each model type had at least one difference in variable retention or relative ranking with the change in climate time period. Pairwise comparisons of spatial predictions were also different, ranging from a low of 1.6% for climate period differences to a high of 25% for algorithm differences. While uncertainty from model algorithm selection is recognized as an important source of uncertainty, the impact of climate period is not commonly assessed. These uncertainties may affect conservation decisions, especially when projecting to future climates, and should be evaluated during model development.

Species Distribution Models (SDM) – A Strategic Tool for Predicting Suitable Habitats for Conserving the Target Species

2019 ◽  
pp. 555-568
Author(s):  
Balaguru Balakrishnan ◽  
Nagamurugan Nandakumar ◽  
Soosairaj Sebastin ◽  
Khaleel Ahamed Abdul Kareem
Keyword(s):  
Species Distribution ◽  
Spatial Data ◽  
Environmental Variables ◽  
Species Distribution Models ◽  
Species Distribution Model ◽  
Distribution Model ◽  
Suitable Habitat ◽  
Target Species ◽  
Distribution Models ◽  
Suitable Habitats

Conservation of the species in their native landscapes required understanding patterns of spatial distribution of species and their ecological connectivity through Species Distribution Models (SDM) by generation and integration of spatial data from different sources using Geographical Information System (GIS) tools. SDM is an ecological/spatial model which combines datasets and maps of occurrence of target species and their geographical and environmental variables by linking various algorithms together, that has been applied to either identify or predict the regions fulfilling the set conditions. This article is focused on comprehensive review of spatial data requirements, statistical algorithms and softwares used to generate the SDMs. This chapter also includes a case study predicting the suitable habitat distribution of Gnetum ula, an endemic and vulnerable plant species using maximum entropy (MaxEnt) species distribution model for species occurrences with inputs from environmental variables such as bioclimate and elevation.

scholarly journals When exotic introductions fail: updating invasion beliefs

2019 ◽  
Vol 22 (3) ◽  
pp. 1097-1107
Author(s):  
Daniel K. Heersink ◽  
Peter Caley ◽  
Dean Paini ◽  
Simon C. Barry
Keyword(s):  
Species Distribution ◽  
Habitat Suitability ◽  
Species Distribution Models ◽  
Species Distribution Model ◽  
Region Of Interest ◽  
Distribution Model ◽  
Perna Viridis ◽  
Prior Belief ◽  
Distribution Models ◽  
Establishment Probability

AbstractDecisions regarding invasive risk of exotic species are often based on species distribution models projected onto the recipient region of interest. Such projections are essentially a measure of prior belief in the ability of an organism to invade. Whilst many decisions are made on the basis of such projections, it is less clear how such prior belief may be empirically modified on the basis of data, in particular introduction events that haven’t led to establishment. Here, using the Asian green mussel (Perna viridis) as an example, we illustrate how information on failed introduction attempts may be used to continually update our beliefs in the ability of an organism to invade per introduction, and the underlying habitat suitability for establishment. Our results show that the establishment probability of P. viridis per fouled ship visit in the supposedly favourable northern Australian waters are much lower than initially though, and are continuing to decline. A Bayesian interpretation of our results notes the dramatic reduction in our belief of the ability of P. viridis to invade in the light of what we estimate to be 100’s of fouled vessels per year visiting ports without any persistent populations establishing. Under a hypothetico-deductive approach we would reject the null (prior) species distribution model as being useful, and seek to find a better one that can withstand the challenge of data.

Sign in / Sign up

Export Citation Format

Share Document