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

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.

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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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Community Confounding In Joint Species Distribution Models

10.21203/rs.3.rs-1111644/v1 ◽

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.

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

Advances in Geospatial Technologies - Handbook of Research on Geographic Information Systems Applications and Advancements ◽

10.4018/978-1-5225-0937-0.ch017 ◽

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.

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

Climate ◽

10.3390/cli7030037 ◽

2019 ◽

Vol 7 (3) ◽

pp. 37 ◽

Cited By ~ 3

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.

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

Environmental Information Systems ◽

10.4018/978-1-5225-7033-2.ch023 ◽

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

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When exotic introductions fail: updating invasion beliefs

Biological Invasions ◽

10.1007/s10530-019-02163-x ◽

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.

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Integrated species distribution models: combining presence‐background data and site‐occupancy data with imperfect detection

Methods in Ecology and Evolution ◽

10.1111/2041-210x.12738 ◽

2017 ◽

Vol 8 (4) ◽

pp. 420-430 ◽

Cited By ~ 27

Author(s):

Vira Koshkina ◽

Yan Wang ◽

Ascelin Gordon ◽

Robert M. Dorazio ◽

Matt White ◽

...

Keyword(s):

Species Distribution ◽

Species Distribution Models ◽

Site Occupancy ◽

Distribution Models ◽

Imperfect Detection ◽

Background Data

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

10.7287/peerj.preprints.26874 ◽

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.

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Evaluation of species distribution models for estimating animal dark diversity

10.21203/rs.3.rs-1100842/v1 ◽

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.

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

PeerJ ◽

10.7717/peerj.4095 ◽

2017 ◽

Vol 5 ◽

pp. e4095 ◽

Cited By ~ 93

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.

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