When exotic introductions fail: updating invasion beliefs

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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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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Associations Between Habitat Quality And Body Size In The Carpathian Land Snail Vestia turgida: Species Distribution Model Selection And Assessment Of Performance

10.1101/2020.05.09.085746 ◽

2020 ◽

Author(s):

V. Tytar ◽

O. Baidashnikov

Keyword(s):

Body Size ◽

Habitat Quality ◽

Species Distribution ◽

Habitat Suitability ◽

Species Distribution Model ◽

Land Snail ◽

Distribution Model ◽

Distribution Models ◽

Data Set ◽

Additive Regression

Species distribution models (SDMs) are generally thought to be good indicators of habitat suitability, and thus of species’ performance, consequently SDMs can be validated by checking whether the areas projected to have the greatest habitat quality are occupied by individuals or populations with higher than average fitness. We hypothesized a positive and statistically significant relationship between observed in the field body size of the snail V. turgida and modelled habitat suitability, tested this relationship with linear mixed models, and found that indeed, larger individuals tend to occupy high-quality areas, as predicted by the SDMs. However, by testing several SDM algorithms, we found varied levels of performance in terms of expounding this relationship. Marginal R2, expressing the variance explained by the fixed terms in the regression models, was adopted as a measure of functional accuracy, and used to rank the SDMs accordingly. In this respect, the Bayesian additive regression trees (BART) algorithm (Carlson, 2020) gave the best result, despite the low AUC and TSS. By restricting our analysis to the BART algorithm only, a variety of sets of environmental variables commonly or less used in the construction of SDMs were explored and tested according to their functional accuracy. In this respect, the SDM produced using the ENVIREM data set (Title, Bemmels, 2018) gave the best result.

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Associations Between Habitat Quality and Body Size in the Carpathian-Podolian Land Snail Vestia turgida (Gastropoda, Clausiliidae): Species Distribution Model Selection and Assessment of Performance

Zoodiversity ◽

10.15407/zoo2021.01.025 ◽

2021 ◽

Vol 55 (1) ◽

pp. 25-40

Author(s):

V. Tytar

Keyword(s):

Body Size ◽

Habitat Quality ◽

Species Distribution ◽

Habitat Suitability ◽

Species Distribution Model ◽

Land Snail ◽

Distribution Model ◽

Distribution Models ◽

Data Set ◽

Additive Regression

Species distribution models (SDMs) are generally thought to be good indicators of habitat suitability, and thus of species’ performance. Consequently SDMs can be validated by checking whether the areas projected to have the greatest habitat quality are occupied by individuals or populations with higher than average fi tness. We hypothesized a positive and statistically signifi cant relationship between observed in the fi eld body size of the snail V. turgida (Rossmässler, 1836) and modelled habitat suitability, tested this relationship with linear mixed models, and found that indeed, larger individuals tend to occupy high-quality areas, as predicted by the SDMs. However, by testing several SDM algorithms, we found varied levels of performance in terms of expounding this relationship. Marginal R2 expressing the variance explained by the fi xed terms in the regression models, was adopted as a measure of functional accuracy, and used to rank the SDMs accordingly. In this respect, the Bayesian additive regression trees (BART) algorithm gave the best result, despite the low AUC and TSS. By restricting our analysis to the BART algorithm only, a variety of sets of environmental variables commonly or less used in the construction of SDMs were explored and tested according to their functional accuracy. In this respect, the SDM produced using the ENVIREM data set gave the best result.

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

PLoS ONE ◽

10.1371/journal.pone.0245973 ◽

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.

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A distribution model for Glossina brevipalpis and Glossina austeni in Southern Mozambique, Eswatini and South Africa for enhanced area-wide integrated pest management approaches

PLoS Neglected Tropical Diseases ◽

10.1371/journal.pntd.0009989 ◽

2021 ◽

Vol 15 (11) ◽

pp. e0009989

Author(s):

Chantel J. de Beer ◽

Ahmadou H. Dicko ◽

Jerome Ntshangase ◽

Percy Moyaba ◽

Moeti O. Taioe ◽

...

Keyword(s):

South Africa ◽

Protected Areas ◽

Species Distribution ◽

Habitat Suitability ◽

Species Distribution Model ◽

Distribution Model ◽

Distribution Models ◽

Glossina Austeni ◽

Suitability Maps ◽

Glossina Brevipalpis

Background Glossina austeni and Glossina brevipalpis (Diptera: Glossinidae) are the sole cyclical vectors of African trypanosomes in South Africa, Eswatini and southern Mozambique. These populations represent the southernmost distribution of tsetse flies on the African continent. Accurate knowledge of infested areas is a prerequisite to develop and implement efficient and cost-effective control strategies, and distribution models may reduce large-scale, extensive entomological surveys that are time consuming and expensive. The objective was to develop a MaxEnt species distribution model and habitat suitability maps for the southern tsetse belt of South Africa, Eswatini and southern Mozambique. Methodology/Principal findings The present study used existing entomological survey data of G. austeni and G. brevipalpis to develop a MaxEnt species distribution model and habitat suitability maps. Distribution models and a checkerboard analysis indicated an overlapping presence of the two species and the most suitable habitat for both species were protected areas and the coastal strip in KwaZulu-Natal Province, South Africa and Maputo Province, Mozambique. The predicted presence extents, to a small degree, into communal farming areas adjacent to the protected areas and coastline, especially in the Matutuíne District of Mozambique. The quality of the MaxEnt model was assessed using an independent data set and indicated good performance with high predictive power (AUC > 0.80 for both species). Conclusions/Significance The models indicated that cattle density, land surface temperature and protected areas, in relation with vegetation are the main factors contributing to the distribution of the two tsetse species in the area. Changes in the climate, agricultural practices and land-use have had a significant and rapid impact on tsetse abundance in the area. The model predicted low habitat suitability in the Gaza and Inhambane Provinces of Mozambique, i.e., the area north of the Matutuíne District. This might indicate that the southern tsetse population is isolated from the main tsetse belt in the north of Mozambique. The updated distribution models will be useful for planning tsetse and trypanosomosis interventions in the area.

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Optimization of the Fuzzy Matter Element Method for Predicting Species Suitability Distribution Based on Environmental Data

Sustainability ◽

10.3390/su10103444 ◽

2018 ◽

Vol 10 (10) ◽

pp. 3444 ◽

Cited By ~ 5

Author(s):

Quanzhong Zhang ◽

Haiyan Wei ◽

Zefang Zhao ◽

Jing Liu ◽

Qiao Ran ◽

...

Keyword(s):

Species Distribution ◽

Habitat Suitability ◽

Species Distribution Model ◽

Environmental Data ◽

Distribution Model ◽

New Approach ◽

Suitability Index ◽

Biological Laws ◽

Experimental Process ◽

Key Variables

Over the years, with the efforts of many researchers, the field of species distribution model (SDM) has been well explored. The model of fuzzy matter elements (FME), which, combined with GIS to predict species distribution, has received extensive attention since its emergence. Based on previous studies, this paper improved FME, extended the scope of the membership degree and habitat suitability index, and explored the unsuitable areas of species. We have enhanced the limitation effect of key variables on species habitats, making the operation of FME more consistent with biological laws. By optimizing the FME, it could avoid the accumulation of predicted errors with multi-variables, and make the predicted results more reasonable. In this study, Gynostemma pentaphyllum (Thunb.) Makino was used as an example. The experimental process used several major environmental variables (climate, soil, and terrain variables) to predict the habitat suitability distribution of G. pentaphyllum in China for its current and future period, which includes the period of 2050s (average for 2041–2060) and 2070s (average for 2061–2080) under representative concentration pathways 4.5 (RCP4.5). The results of the analysis showed that the model performed well with a high accuracy by reducing the redundancy of the environmental data. The study could relieve the reliance on a large database of environmental information and propose a new approach for protecting the G. pentaphyllum in unsuitable areas under climate change.

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