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

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7771
Author(s):  
Daniel Zamorano ◽  
Fabio A. Labra ◽  
Marcelo Villarroel ◽  
Shaw Lacy ◽  
Luca Mao ◽  
...  
Keyword(s):  
Body Size ◽  
Species Distribution ◽  
Riparian Vegetation ◽  
Species Distribution Model ◽  
Performance Metrics ◽  
Conservation Status ◽  
Management Tool ◽  
Distribution Model ◽  
Neural Network Modeling ◽  
Fish Size

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, and to date, the evidence shows unclear results. In this context, Chilean fishes provide an ideal case to evaluate this relationship due to their short size (fishes between 5 cm and 40 cm) and conservation status, providing evidence for species at the lower end of the worldwide fish size distribution and representing a relevant management tool for species conservation. We assessed the effect of body size on the performance of SDM in nine Chilean river fishes, considering the number of records, performance metrics, and predictor importance. The study was developed in the Bueno and Valdivia basins of southern Chile. We used a neural network modeling algorithm, training models with a cross-validation scheme. The effect of fish size on selected metrics was assessed using linear models and beta regressions. While no relationship between fish size and the number of presences was found, our results indicate that the model specificity increases with fish size. Additionally, the predictive importance of Riparian Vegetation and Within-Channel Structures variables decreases for larger species. Our results suggest that the relationship between the grain of the dataset and the home range of the species could bias SDM, leading in our case, to overprediction of absences. We also suggest that evolutionary adaptation to low slopes among Chilean fishes increases the relevance of riparian vegetation in the SDMs of smaller species. This study provides evidence on how species size may bias SDM, which could potentially be corrected by adjusting the model grain.

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.

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