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.

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 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 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.

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.

scholarly journals Building realistic assemblages with a Joint Species Distribution Model

2014 ◽  
Author(s):  
David J. Harris
Keyword(s):  
Species Distribution ◽  
Environmental Variables ◽  
Species Distribution Model ◽  
Species Turnover ◽  
Alternative Methods ◽  
Distribution Model ◽  
Individual Species ◽  
Environmental Predictors ◽  
Species Occurrence ◽  
Distribution Models

1. Species distribution models (SDMs) can be used to predict how individual species---and whole assemblages of species---will respond to a changing environment. Until now, these models have either assumed (1) that species' occurrence probabilities are uncorrelated, or (2) that species respond linearly to preselected environmental variables. These two assumptions currently prevent ecologists from modeling assemblages with realistic co-occurrence and species richness properties. 2. This paper introduces a stochastic feedforward neural network, called mistnet, which makes neither assumption. Thus, unlike most SDMs, mistnet can account for non-independent co-occurrence patterns driven by unobserved environmental heterogeneity. And unlike recently proposed Joint SDMs, mistnet can also learn nonlinear functions relating species' occurrence probabilities to environmental predictors. 3. Mistnet makes more accurate predictions about the North American bird communities found along Breeding Bird Survey transects than several alternative methods tested. In particular, typical assemblages held out of sample for validation were nearly 50,000 times more likely under the mistnet model than under independent combinations of single-species models. 4. Apart from improved accuracy, mistnet shows two other important benefits for ecological research and management. First: by analyzing co-occurrence data, mistnet can identify unmeasured---and perhaps unanticipated---environmental variables that drive species turnover. For example, mistnet identified a strong grassland/forest gradient, even though only temperature and precipitation were given as model inputs. Second: mistnet is able to take advantage of incomplete data sets to guide its predictions towards more realistic assemblages. For example, mistnet automatically adjusts its expectations to include more forest-associated species in response to a stray observation of a forest-dwelling warbler.

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.

scholarly journals Joint species distribution models of Everglades wading birds to inform restoration planning

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245973
Author(s):  
Laura E. D’Acunto ◽  
Leonard Pearlstine ◽  
Stephanie S. Romañach
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Species Distribution Model ◽  
Site Occupancy ◽  
The United States ◽  
Decision Makers ◽  
Distribution Model ◽  
Distribution Models ◽  
Landscape Characteristics ◽  
Wading Bird

Restoration of the Florida Everglades, a substantial wetland ecosystem within the United States, is one of the largest ongoing restoration projects in the world. Decision-makers and managers within the Everglades ecosystem rely on ecological models forecasting indicator wildlife response to changes in the management of water flows within the system. One such indicator of ecosystem health, the presence of wading bird communities on the landscape, is currently assessed using three species distribution models that assume perfect detection and report output on different scales that are challenging to compare against one another. We sought to use current advancements in species distribution modeling to improve models of Everglades wading bird distribution. Using a joint species distribution model that accounted for imperfect detection, we modeled the presence of nine species of wading bird simultaneously in response to annual hydrologic conditions and landscape characteristics within the Everglades system. Our resulting model improved upon the previous model in three key ways: 1) the model predicts probability of occupancy for the nine species on a scale of 0–1, making the output more intuitive and easily comparable for managers and decision-makers that must consider the responses of several species simultaneously; 2) through joint species modeling, we were able to consider rarer species within the modeling that otherwise are detected in too few numbers to fit as individual models; and 3) the model explicitly allows detection probability of species to be less than 1 which can reduce bias in the site occupancy estimates. These improvements are essential as Everglades restoration continues and managers require models that consider the impacts of water management on key indicator wildlife such as the wading bird community.

scholarly journals Combining Deforestation and Species Distribution Models to Improve Measures of Chimpanzee Conservation Impacts of REDD: A Case Study from Ntakata Mountains, Western Tanzania

Forests ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 1195
Author(s):  
Rebecca Dickson ◽  
Marc Baker ◽  
Noémie Bonnin ◽  
David Shoch ◽  
Benjamin Rifkin ◽  
...  
Keyword(s):  
Biodiversity Conservation ◽  
Species Distribution ◽  
Carbon Emissions ◽  
Species Distribution Models ◽  
Cost Effective ◽  
Climate Mitigation ◽  
Suitable Habitat ◽  
Distribution Models ◽  
Western Tanzania

Projects to reduce emissions from deforestation and degradation (REDD) are designed to reduce carbon emissions through avoided deforestation and degradation, and in many cases, to produce additional community and biodiversity conservation co-benefits. While these co-benefits can be significant, quantifying conservation impacts has been challenging, and most projects use simple species presence to demonstrate positive biodiversity impact. Some of the same tools applied in the quantification of climate mitigation benefits have relevance and potential application to estimating co-benefits for biodiversity conservation. In western Tanzania, most chimpanzees live outside of national park boundaries, and thus face threats from human activity, including competition for suitable habitat. Through a case study of the Ntakata Mountains REDD project in western Tanzania, we demonstrate a combined application of deforestation modelling with species distribution models to assess forest conservation benefits in terms of avoided carbon emissions and improved chimpanzee habitat. The application of such tools is a novel approach that we argue permits the better design of future REDD projects for biodiversity co-benefits. This approach also enables project developers to produce the more manageable, accurate and cost-effective monitoring, reporting and verification of project impacts that are critical to verification under carbon standards.

scholarly journals Incorporating movement in species distribution models

2015 ◽  
Vol 39 (6) ◽  
pp. 837-849 ◽  
Author(s):  
Jennifer A Miller ◽  
Paul Holloway
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Suitable Habitat ◽  
Age At Maturity ◽  
Distribution Models ◽  
Migration Rates ◽  
Movement Ability ◽  
Landscape Factors ◽  
Patch Configuration ◽  
Realistic Information

Movement in the context of species distribution models (SDMs) generally refers to a species’ ability to access suitable habitat. Movement ability can be determined by some combination of dispersal constraints or migration rates, landscape factors such as patch configuration, disturbance, and barriers, and demographic factors related to age at maturity, mortality, and fecundity. Including movement ability can result in more precise projections that help to distinguish suitable habitat that is or can be potentially occupied, from suitable habitat that is inaccessible. While most SDM studies have ignored movement or conceptualized it in overly simplistic ways (e.g. no dispersal versus unlimited dispersal), it is increasingly important to incorporate realistic information on movement ability, particularly for studies that aim to project future distributions such as climate change forecasting and invasive species applications. This progress report addresses the increasingly complex ways in which movement has been incorporated in SDM and outlines directions for further study.

scholarly journals Assessing the effect of fish size on species distribution model performance in southern Chilean rivers

2018 ◽  
Author(s):  
Daniel Zamorano ◽  
Fabio Labra ◽  
Marcelo Villarroel ◽  
Luca Mao ◽  
Shaw Lucy ◽  
...  
Keyword(s):  
Body Size ◽  
Species Distribution ◽  
River Flow ◽  
Species Distribution Model ◽  
Anthropogenic Activities ◽  
Model Performance ◽  
Distribution Model ◽  
Distribution Models ◽  
Fish Size ◽  
River Fish

Despite its theoretical relationship, the effect of body size on the performance of species distribution models (SDM) has only been assessed in a few studies of terrestrial taxa. We aim to assess the effect of body size on the performance of SDM in river fish. We study seven Chilean freshwater fish, using models trained with three different sets of predictor variables: ecological (Eco), anthropogenic (Antr) and both (Eco+Antr). Our results indicate that the performance of the Eco+Antr models improves with fish size. These results highlight the importance of two novel predictive layers: the source of river flow and the overproduction of biotopes by anthropogenic activities. We compare our work with previous studies that modeled river fish, and observe a similar relationship in most cases. We discuss the current challenges of the modeling of riverine species, and how our work helps suggest possible solutions.

Sign in / Sign up

Export Citation Format

Share Document