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

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.

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

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.

scholarly journals 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 ◽  
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.

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 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 Lowland tapir distribution and habitat loss in South America

2015 ◽  
Author(s):  
Jose Luis Passos Cordeiro ◽  
José MV Fragoso ◽  
Danielle Crawshaw ◽  
Luiz Flamarion B Oliveira
Keyword(s):  
South America ◽  
Protected Areas ◽  
Habitat Loss ◽  
Habitat Suitability ◽  
Distribution Patterns ◽  
Distribution Models ◽  
Potential Habitat ◽  
Geographical Regions ◽  
Suitability Maps ◽  
Complex Relationships

The development of species distribution models (SDMs) can help conservation efforts by generating potential distributions and identifying areas of high environmental suitability for protection. Our study presents a rigorously derived distribution and habitat map for lowland tapir in South America. We also describe the potential habitat suitability of various geographical regions and habitat loss, inside and outside of protected areas network. Two different SDM approaches, MAXENT and ENFA, produced relative different Habitat Suitability Maps for the lowland tapir. While MAXENT was efficient at identifying areas as suitable or unsuitable, it was less efficient (when compared to the results by ENFA) at identifying the gradient of habitat suitability. MAXENT is a more multifaceted technique that establishes more complex relationships between dependent and independent variables. Our results demonstrate that for at least one species, the lowland tapir, the use of a simple consensual approach (average of ENFA and MAXENT models outputs) better reflected its current distribution patterns. The Brazilian ecoregions have the highest habitat loss for the tapir. Cerrado and Atlantic Forest account for nearly half (48.19%) of the total area lost. The Amazon region contains the largest area under protection, and the most extensive remaining habitat for the tapir, but also showed high levels of habitat loss outside protected areas, which increases the importance of support for proper management.

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

2018 ◽  
Vol 10 (10) ◽  
pp. 3444 ◽  
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.

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.

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 Evaluation of species distribution models for estimating animal dark diversity

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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