Disentangling uncertainties from niche modeling in freshwater ecosystems

Parreira, Micael Rosa; Nabout, João Carlos; Tessarolo, Geiziane; Lima‐Ribeiro, Matheus S.; Teresa, Fabrício Barreto

doi:10.1016/j.ecolmodel.2018.10.024

Cited by 14 publications

(28 citation statements)

References 43 publications

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“…Therefore, we obtained a digital elevation model (DEM) from USGeological-Survey (GT30H1KSA: http://www.usgs.gov). Altitude is a variable of great importance to characterize the topography of a landscape (Domisch et al, 2015;Parreira et al, 2019). However, it is an incomplete variable since it does not allow for the definition of the angular configuration of its spatial variations.…”

Section: Preparation Of Environmental Predictorsmentioning

confidence: 99%

“…According to different authors, the predictions would be more robust with presence-absence or abundance data; however, presence-absence data introduces conflicting information: they indicate habitats that are not adequate or that are adequate but without the species being present, for example due to inaccessibility Fielding & Bell, 1997;Phillips et al, 2009). On the other hand, the presenceabsence data are also conditioned by the evolutionary and ecological history of the species since the ecosystems are not in equilibrium, which compromises the ability to detect the species at a certain time of the year Parreira, Nabout, Tessarolo, de Souza Lima-Ribeiro, & Teresa, 2019). However, the use of presence-only data is justified by the lack of systematically sampled data and by the need to know the predictive responses of the models for the species of interest.…”

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confidence: 99%

“…Therefore, the correct selection of environmental predictors is one of the most delicate operational tasks and has a significant effect on the final output, generating discrepancies in the models (Muscarella et al, 2014;Radosavljevic & Anderson, 2014). In fact, predicting species' ranges with dissimilar sets of environmental and physical variables may cause different predictions of the distribution of a species (Guisan, Graham, Elith, & Huettmann, 2007;Parreira et al, 2019). Peterson, 2001).…”

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confidence: 99%

“…Peterson, 2001). In this sense, for better spatial representation of the connections and the integration of the hydrographic networks, it is essential to incorporate other variables, for example, using accumulated flow as a predictor variable (Domisch et al, 2015;Parreira et al, 2019).…”

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confidence: 99%

“…Comparing species that have a preference for rivers versus species associated with streams allows us to evaluate how landscape, environmental and physical configurations may affect the spatial distribution patterns of the ichthyofauna (Castillo-Torres, Martínez-Meyer, Córdova-Tapia, & Zambrano, 2017;Domisch et al, 2015;Parreira et al, 2019;Poff, 1997). The wide spatial extent of the Amazonas-Tocantins basin facilitates the representation of climatic and environmental processes that could be considered continental.…”

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confidence: 99%

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Effects of different variable sets on the potential distribution of fish species in the Amazon Basin

Alvarez

Gerhard

Silva

et al. 2020

Ecology of Freshwater Fish

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Estimating species’ potential distribution is one of the main objectives of macroecology, especially when sampling biases can affect knowledge on how environmental variables affect species distribution. Ecological niche models estimate species’ environmental niches from different variables and their occurrences. Using the presence‐only data from eight Amazonian fish species, which inhabit rivers and streams, we aimed to (a) explore the effect of different sets variables on the spatial distributions of target species and (b) evaluate the predictive responses of MaxEnt to sets of variables with different degrees of complexity. MaxEnt has high flexibility in relation to the input data and its performance is influenced by a moderate number of adjustable parameters, allowing for high precision results when balancing underestimation and overestimation errors. We used environmental predictors in MaxEnt the principal components of climatic, topographic and edaphic variables as inputs. The combination of topographic and edaphic variables produced more precise and spatially restricted distribution ranges for all species when compared to those generated with climatic variables. All models reached high AUC values, especially for stream species. Modelled range sizes were broader for the river species, suggesting different tolerance thresholds and habitat preferences when compared to stream species. The complexity of the different variables sets did not affect MaxEnt's prediction capacity. However, for stream species, MaxEnt showed a greater predictive power. This work increases the knowledge with regards to the influence of different environmental predictors on the spatial patterns of the distribution of Amazonian fish.

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Section: Preparation Of Environmental Predictorsmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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confidence: 99%

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confidence: 99%

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Effects of different variable sets on the potential distribution of fish species in the Amazon Basin

Alvarez

Gerhard

Silva

et al. 2020

Ecology of Freshwater Fish

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The conservation of migratory fishes in the second largest river basin of South America depends on the creation of new protected areas

Bailly

Batista-Silva

Cassemiro

et al. 2021

Aquatic Conservation

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Freshwater ecosystems, providing valuable goods and services to humans, have been subjected to multiple human impacts, among which climate change plays a central role in threats to species. It is expected that protected areas, the cornerstone of biodiversity conservation efforts, will assume a decisive role in protecting freshwater species from the impacts of climate change. This study assessed the effects of climate change on migratory fish of the second largest neotropical river basin, evaluating the effectiveness of protected areas in safeguarding fish species, and hence the ecological functions that they perform and the ecosystem resources that they provide. The present range of 23 migratory fish of economic interest in the Paraná–Paraguay basin was estimated and the responses to future climatic shifts projected to the middle and end of the 21st century were examined, quantifying predictive uncertainties. Changes and losses of climatically suitable areas will trigger severe contractions in range, with the greatest impact on the most valuable species in commercial fishing, where range losses are likely to surpass 65% in the future. The main channel of the Upper Paraná River and tributaries of its left margin are projected to serve as climatic refuges for many species, and such regions are not affected by high predictive uncertainty. The results revealed that protected areas do not sufficiently protect migratory fish at present, and that they will continue to offer negligible protection in the face of climate change. This study alerts decision makers to the potential damage to inland fishery resources from climate change and provides useful information to guide conservation strategies spatially. We advocate that the creation of new protected areas and the redesign of the existing network to encompass regions that maximize current and future occupancy of migratory fish are crucial to conserve the valuable ecological, societal, and economic benefits that they provide.

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Modeling the climate change impact on the habitat suitability and potential distribution of an economically important hill stream fish, Neolissochilus hexagonolepis, in the Ganges–Brahmaputra basin of Eastern Himalayas

2021

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Species distribution modeling has been an expedient tool. The optimization of species distribution models for Eastern Himalayan hill stream fishes remains a substantial challenge. This study has constructed a potentially informative species distribution model for an economically important and Near Threatened hill stream fish, Neolissochilus hexagonolepis. The wild habitats of this species along the Ganges-Brahmaputra basin of the Eastern Himalayas are under enormous threat. Initially, 21 predictor variables were selected based on their ecological relevance to stream fish distribution. The maximum entropy (MaxEnt) algorithm was employed to predict the habitat suitability in current and ensemble two future scenarios considering different global circulation models of climate change. Our results suggested that seasonality in annual temperature, precipitation, terrain wetness index, and eco-regional attributes had a higher contribution to the model. The fish species seems to be distributed in freshwater rivers, streams, and headwaters of lower (elevation < 500 m) to medium (elevation < 1000 m) elevation in north-eastern states of India along with other countries of Eastern Himalayas. A potential loss of current-suitable habitats is predicted in future scenarios along the lower altitudes (500 m < elevation < 1000 m). Country-wide shifts appear significant in the analysis of variance. The maximum habitat loss is projected in the sub-Himalayan belt of India and Bangladesh. The results also indicate that this species would tend to disperse towards higher altitudes and northward. Overall, this study emphasizes preserving dispersal connectivity and identifying crucial corridors for this commercially essential cold-water fish species. The stakes of becoming climate change winners for such fish species are low, considering successful colonization to upstream habitats tracking their habitat suitability.

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Disentangling uncertainties from niche modeling in freshwater ecosystems

Cited by 14 publications

References 43 publications

Effects of different variable sets on the potential distribution of fish species in the Amazon Basin

Effects of different variable sets on the potential distribution of fish species in the Amazon Basin

The conservation of migratory fishes in the second largest river basin of South America depends on the creation of new protected areas

Modeling the climate change impact on the habitat suitability and potential distribution of an economically important hill stream fish, Neolissochilus hexagonolepis, in the Ganges–Brahmaputra basin of Eastern Himalayas

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