scholarly journals Using Unsupervised Artificial Neural Networks to Detect Sibling Species: A case in Myxomycetes

2019 ◽  
Vol 3 ◽  
Author(s):  
Francisco Pando ◽  
Ignacio Heredia ◽  
Lara Lloret
Keyword(s):  
Sibling Species ◽  
Species Distribution ◽  
Environmental Data ◽  
Future Climate Change ◽  
Climate Change Scenarios ◽  
Distribution Models ◽  
Global Biodiversity Information Facility ◽  
Environmental Feature ◽  
Feature Vectors ◽  
Ecological Preferences

Species distribution modelling (SDM) --i.e. the prediction of species potential geographic distributions based on correlations between known presence records and the environmental conditions at occurrence localities-- is one of the most freqently cited developments in recent years in the realm of biodiversity studies (Web of Science 2019). The reasons for the explosion in SDM studies reside in: a) the pressure to know species distributions (under both present and future climate change scenarios) with precision to satisfy scientific as well as societal objectives; b) the impossibility of knowing every occurrence of all but the most conspicuous and/or special interest species; c) the availability of primary occurrence and environmental data in unprecedented amounts, thanks to initiatives like the Global Biodiversity Information Facility (GBIF); and d) the development of algorithms and software, along with the computing power, which allow inference of species distribution models from the available data. The standard methods to produce such models are based on environmental feature vectors, and some well-established algorithms such as distance-based machine learning, regression or a combination of these (Tsoar et al. 2007). In this presentation, we explore deep learning techniques (LeCun et al. 2015), particularly how those developed in recent years could contribute to the study of species distribution (see also Botella et al. 2018; Deneu et al. 2018). In this contribution we aim to identify sibling species on the basis of their ecological preferences. In this exercise, we prepare an image-based environmental representation space using an unsupervised classification approach, instead of a set of environmental feature vectors. For our case study, we chose a well-known cosmopolitan myxomycete (i.e., mycetozoan) species: Hemitrichia serpula (Scop.) Rostaf., whose ecological preferences --involving several biomes-- may suggest that it could comprise several sibling species.

scholarly journals Using species distribution models to infer potential climate change-induced range shifts of freshwater fish in south-eastern Australia

2011 ◽  
Vol 62 (9) ◽  
pp. 1043 ◽  
Author(s):  
Nick Bond ◽  
Jim Thomson ◽  
Paul Reich ◽  
Janet Stein
Keyword(s):  
Climate Change ◽  
Freshwater Fish ◽  
Species Distribution ◽  
Climate Change Scenario ◽  
Species Distribution Models ◽  
Range Shifts ◽  
Future Climate Change ◽  
Change Scenario ◽  
Climate Change Scenarios ◽  
Distribution Models

There are few quantitative predictions for the impacts of climate change on freshwater fish in Australia. We developed species distribution models (SDMs) linking historical fish distributions for 43 species from Victorian streams to a suite of hydro-climatic and catchment predictors, and applied these models to explore predicted range shifts under future climate-change scenarios. Here, we present summary results for the 43 species, together with a more detailed analysis for a subset of species with distinct distributions in relation to temperature and hydrology. Range shifts increased from the lower to upper climate-change scenarios, with most species predicted to undergo some degree of range shift. Changes in total occupancy ranged from –38% to +63% under the lower climate-change scenario to –47% to +182% under the upper climate-change scenario. We do, however, caution that range expansions are more putative than range contractions, because the effects of barriers, limited dispersal and potential life-history factors are likely to exclude some areas from being colonised. As well as potentially informing more mechanistic modelling approaches, quantitative predictions such as these should be seen as representing hypotheses to be tested and discussed, and should be valuable for informing long-term strategies to protect aquatic biota.

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 Modelling Maritime Pine (Pinus pinaster Aiton) Spatial Distribution and Productivity in Portugal: Tools for Forest Management

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 368
Author(s):  
Cristina Alegria ◽  
Natália Roque ◽  
Teresa Albuquerque ◽  
Paulo Fernandez ◽  
Maria Margarida Ribeiro
Keyword(s):  
Spatial Distribution ◽  
Forest Management ◽  
Species Distribution ◽  
Pinus Pinaster ◽  
Landscape Planning ◽  
Environmental Data ◽  
Maritime Pine ◽  
Future Climate Change ◽  
Sequential Simulation ◽  
Model Species

Research Highlights: Modelling species’ distribution and productivity is key to support integrated landscape planning, species’ afforestation, and sustainable forest management. Background and Objectives: Maritime pine (Pinus pinaster Aiton) forests in Portugal were lately affected by wildfires and measures to overcome this situation are needed. The aims of this study were: (1) to model species’ spatial distribution and productivity using a machine learning (ML) regression approach to produce current species’ distribution and productivity maps; (2) to model the species’ spatial productivity using a stochastic sequential simulation approach to produce the species’ current productivity map; (3) to produce the species’ potential distribution map, by using a ML classification approach to define species’ ecological envelope thresholds; and (4) to identify present and future key factors for the species’ afforestation and management. Materials and Methods: Spatial land cover/land use data, inventory, and environmental data (climate, topography, and soil) were used in a coupled ML regression and stochastic sequential simulation approaches to model species’ current and potential distributions and productivity. Results: Maritime pine spatial distribution modelling by the ML approach provided 69% fitting efficiency, while species productivity modelling achieved only 43%. The species’ potential area covered 60% of the country’s area, where 78% of the species’ forest inventory plots (1995) were found. The change in the Maritime pine stands’ age structure observed in the last decades is causing the species’ recovery by natural regeneration to be at risk. Conclusions: The maps produced allow for best site identification for species afforestation, wood production regulation support, landscape planning considering species’ diversity, and fire hazard mitigation. These maps were obtained by modelling using environmental covariates, such as climate attributes, so their projection in future climate change scenarios can be performed.

Very high resolution environmental predictors in species distribution models

2013 ◽  
Vol 38 (1) ◽  
pp. 79-96 ◽  
Author(s):  
Jean-Nicolas Pradervand ◽  
Anne Dubuis ◽  
Loïc Pellissier ◽  
Antoine Guisan ◽  
Christophe Randin
Keyword(s):  
High Resolution ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Environmental Data ◽  
Swiss Alps ◽  
Fine Scale ◽  
Distribution Models ◽  
Model Quality ◽  
Vegetation Height ◽  
Very High

Recent advances in remote sensing technologies have facilitated the generation of very high resolution (VHR) environmental data. Exploratory studies suggested that, if used in species distribution models (SDMs), these data should enable modelling species’ micro-habitats and allow improving predictions for fine-scale biodiversity management. In the present study, we tested the influence, in SDMs, of predictors derived from a VHR digital elevation model (DEM) by comparing the predictive power of models for 239 plant species and their assemblages fitted at six different resolutions in the Swiss Alps. We also tested whether changes of the model quality for a species is related to its functional and ecological characteristics. Refining the resolution only contributed to slight improvement of the models for more than half of the examined species, with the best results obtained at 5 m, but no significant improvement was observed, on average, across all species. Contrary to our expectations, we could not consistently correlate the changes in model performance with species characteristics such as vegetation height. Temperature, the most important variable in the SDMs across the different resolutions, did not contribute any substantial improvement. Our results suggest that improving resolution of topographic data only is not sufficient to improve SDM predictions – and therefore local management – compared to previously used resolutions (here 25 and 100 m). More effort should be dedicated now to conduct finer-scale in-situ environmental measurements (e.g. for temperature, moisture, snow) to obtain improved environmental measurements for fine-scale species mapping and management.

Examining Local Transferability of Predictive Species Distribution Models for Invasive Plants: An Example with Cogongrass (Imperata cylindrica)

2011 ◽  
Vol 4 (4) ◽  
pp. 390-401 ◽  
Author(s):  
Gary N. Ervin ◽  
D. Christopher Holly
Keyword(s):  
Invasive Species ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Imperata Cylindrica ◽  
Environmental Data ◽  
Training Data ◽  
Distribution Data ◽  
Strongly Correlated ◽  
Distribution Models ◽  
Tree Canopy Cover

AbstractSpecies distribution modeling is a tool that is gaining widespread use in the projection of future distributions of invasive species and has important potential as a tool for monitoring invasive species spread. However, the transferability of models from one area to another has been inadequately investigated. This study aimed to determine the degree to which species distribution models (SDMs) for cogongrass, developed with distribution data from Mississippi (USA), could be applied to a similar area in neighboring Alabama. Cogongrass distribution data collected in Mississippi were used to train an SDM that was then tested for accuracy and transferability with cogongrass distribution data collected by a forest management company in Alabama. Analyses indicated the SDM had a relatively high predictive ability within the region of the training data but had poor transferability to the Alabama data. Analysis of the Alabama data, via independent SDM development, indicated that predicted cogongrass distribution in Alabama was more strongly correlated with soil variables than was the case in Mississippi, where the SDM was most strongly correlated with tree canopy cover. Results suggest that model transferability is influenced strongly by (1) data collection methods, (2) landscape context of the survey data, and (3) variations in qualitative aspects of environmental data used in model development.

scholarly journals SDMtoolbox 2.0: the next generation Python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e4095 ◽  
Author(s):  
Jason L. Brown ◽  
Joseph R. Bennett ◽  
Connor M. French
Keyword(s):  
Species Distribution ◽  
Species Distribution Model ◽  
Species Distribution Modeling ◽  
Environmental Data ◽  
Distribution Model ◽  
Data Types ◽  
Distribution Models ◽  
Distribution Modeling ◽  
Modeling Software ◽  
Cost Paths

SDMtoolbox 2.0 is a software package for spatial studies of ecology, evolution, and genetics. The release of SDMtoolbox 2.0 allows researchers to use the most current ArcGIS software and MaxEnt software, and reduces the amount of time that would be spent developing common solutions. The central aim of this software is to automate complicated and repetitive spatial analyses in an intuitive graphical user interface. One core tenant facilitates careful parameterization of species distribution models (SDMs) to maximize each model’s discriminatory ability and minimize overfitting. This includes carefully processing of occurrence data, environmental data, and model parameterization. This program directly interfaces with MaxEnt, one of the most powerful and widely used species distribution modeling software programs, although SDMtoolbox 2.0 is not limited to species distribution modeling or restricted to modeling in MaxEnt. Many of the SDM pre- and post-processing tools have ‘universal’ analogs for use with any modeling software. The current version contains a total of 79 scripts that harness the power of ArcGIS for macroecology, landscape genetics, and evolutionary studies. For example, these tools allow for biodiversity quantification (such as species richness or corrected weighted endemism), generation of least-cost paths and corridors among shared haplotypes, assessment of the significance of spatial randomizations, and enforcement of dispersal limitations of SDMs projected into future climates—to only name a few functions contained in SDMtoolbox 2.0. Lastly, dozens of generalized tools exists for batch processing and conversion of GIS data types or formats, which are broadly useful to any ArcMap user.

Methods and uncertainties in bioclimatic envelope modelling under climate change

2006 ◽  
Vol 30 (6) ◽  
pp. 751-777 ◽  
Author(s):  
Risto K. Heikkinen ◽  
Miska Luoto ◽  
Miguel B. Araújo ◽  
Raimo Virkkala ◽  
Wilfried Thuiller ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climatic Factors ◽  
Future Climate Change ◽  
Future Research ◽  
Climate Change Scenarios ◽  
Distribution Models ◽  
Wide Range ◽  
Bioclimatic Modelling ◽  
Bioclimatic Envelope

Potential impacts of projected climate change on biodiversity are often assessed using single-species bioclimatic ‘envelope’models. Such models are a special case of species distribution models in which the current geographical distribution of species is related to climatic variables so to enable projections of distributions under future climate change scenarios. This work reviews a number of critical methodological issues that may lead to uncertainty in predictions from bioclimatic modelling. Particular attention is paid to recent developments of bioclimatic modelling that address some of these issues as well as to the topics where more progress needs to be made. Developing and applying bioclimatic models in a informative way requires good understanding of a wide range of methodologies, including the choice of modelling technique, model validation, collinearity, autocorrelation, biased sampling of explanatory variables, scaling and impacts of non-climatic factors. A key challenge for future research is integrating factors such as land cover, direct CO2 effects, biotic interactions and dispersal mechanisms into species-climate models. We conclude that, although bioclimatic envelope models have a number of important advantages, they need to be applied only when users of models have a thorough understanding of their limitations and uncertainties.

scholarly journals Species versus within-species niches: a multi-modelling approach to assess range size of a spring-dwelling amphibian

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Forough Goudarzi ◽  
Mahmoud-Reza Hemami ◽  
Mansoureh Malekian ◽  
Sima Fakheran ◽  
Fernando Martínez-Freiría
Keyword(s):  
Species Distribution ◽  
Conservation Status ◽  
Species Conservation ◽  
Landscape Scale ◽  
Species Level ◽  
Environmental Data ◽  
Similar Species ◽  
Fine Scale ◽  
Distribution Models ◽  
Area Of Occupancy

AbstractSpecies Distribution Models (SDMs) can be used to estimate potential geographic ranges and derive indices to assess species conservation status. However, habitat-specialist species require fine-scale range estimates that reflect resource dependency. Furthermore, local adaptation of intraspecific lineages to distinct environmental conditions across ranges have frequently been neglected in SDMs. Here, we propose a multi-stage SDM approach to estimate the distributional range and potential area of occupancy (pAOO) of Neurergus kaiseri, a spring-dwelling amphibian with two climatically-divergent evolutionary lineages. We integrate both broad-scale climatic variables and fine-resolution environmental data to predict the species distribution while examining the performance of lineage-level versus species-level modelling on the estimated pAOO. Predictions of habitat suitability at the landscape scale differed considerably between evolutionary level models. At the landscape scale, spatial predictions derived from lineage-level models showed low overlap and recognised a larger amount of suitable habitats than species-level model. The variable dependency of lineages was different at the landscape scale, but similar at the local scale. Our results highlight the importance of considering fine-scale resolution approaches, as well as intraspecific genetic structure of taxa to estimate pAOO. The flexible procedure presented here can be used as a guideline for estimating pAOO of other similar species.

scholarly journals On climate change and subterranean spiders

2018 ◽  
Vol 1 ◽  
Author(s):  
Stefano Mammola ◽  
Elena Piano ◽  
Alexandra Jones ◽  
Andrea Dejanaz ◽  
Marco Isaia
Keyword(s):  
Climate Change ◽  
Species Distribution ◽  
Thermal Tolerance ◽  
Species Distribution Models ◽  
Genetic Data ◽  
Model Organisms ◽  
Climate Change Scenarios ◽  
Model Species ◽  
Distribution Models ◽  
Physiological Tests

Subterranean ecosystems offer intriguing opportunities to study mechanisms underlying responses to changes in climate because species within them are often adapted to largely constant temperatures. However, responses of specialized subterranean species to anthropogenic climate warming are still largely undiscussed. We combined physiological tests, species distribution models and genetic data to investigate the potential effect of raising temperatures on subterranean coenosis. We used spiders of the genus Troglohyphantes Joseph, 1881 (Araneae: Linyphiidae) as model organisms, focusing on a coherent biogeographic area of the Western Alps in which the distribution of these spiders has been well documented. Thermal tolerance experiments in climatic chambers pointed at a reduced physiological tolerance to temperature fluctuations at increasing levels of troglomorphism. This result suggests that, during their subterranean evolution, spiders have progressively fine-tuned thermal tolerance to the constant and narrow temperature ranges of their habitats. Further evidence of the sensitivity of our model species to temperature increase derives from species distribution models projected onto different climate change scenarios. Model projections point toward a future decline in habitat suitability for subterranean spiders. Moreover, genetic data at the population/species interface are suggestive of limited gene flow between subterranean populations, testifying reduced dispersal capacity and habitat connectivity. In light of these results, we predict the potential extinction of the most restricted endemic species. Our findings therefore emphasize the importance of considering subterranean organisms as model species for ecological studies dealing with climatic changes, and to extend such investigations to other subterranean systems worldwide.

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