Evaluating the ecological influence of hydraulic projects: A review of aquatic habitat suitability models

Yi, Yujun; Cheng, Xi; Zhang, Yang; Wieprecht, Silke; Zhang, Shanghong; Wu, Yingjie

doi:10.1016/j.rser.2016.09.138

Cited by 102 publications

(66 citation statements)

References 104 publications

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“…However its interpretability is limited when analysing species-environmental relationships, its parameter identification is not straightforward, and it is highly sensitive to extrapolation (Prasad et al, 2006). MARS method is mainly implemented for multispecies modelling and is considered as an alternative to other regression methods such as GLM and GAM (Barry & Elith, 2006;Xinhai Li & Wang, 2013;Yi et al, 2017).…”

Section: Other Methodsmentioning

confidence: 99%

“…Then the obtained membership values are combined for different variables using fuzzy if-then rules incorporating fuzzy set operations. Fuzzy logic models are generally trained employing optimization routines based on the Shannon-Waver entropy (tuning the fuzzy set parameters looking for entropies values greater than 0.85) or genetic algorithms to generate uniformly distributed fuzzy sets (Yi et al, 2017). Fuzzy set operations include union, intersection, and additive complement.…”

Section: Soft Computing Methodsmentioning

confidence: 99%

“…Outcomes from different fuzzy rules are then aggregated into a final fuzzy score. Then the nearest ascent hill-climbing algorithm can be used for optimizing the consequent part of each fuzzy rule (Yi et al, 2017). Following Adriaenssens, De Baets, , fuzzy if-then rules consist of antecedent and consequent parts, which mainly rely on expert knowledge.…”

Section: Soft Computing Methodsmentioning

confidence: 99%

“…Stream health models are developed for establishing reference conditions (Feio & Poquet, 2011;Hawkins, Norris, Hogue, & Feminella, 2000), predicting biological variables and indicators in unsampled locations (Merriam, Petty, Strager, Maxwell, & Ziemkiewicz, 2015;Waite et al, 2010), classifying streams by impairment condition Maloney, Weller, Russell, & Hothorn, 2009), and predicting biological variables and indicators given the implementation of conservation practices (Hall et al, 2017;Sowa et al, 2016) and changes in environmental and landscape stressors (Einheuser et al, 2012;Einheuser, Nejadhashemi, Wang, Sowa, & Woznicki, 2013;. Steel et al, 2010), species distribution models (SDMs; Elith & Graham, 2009;Li & Wang, 2013;Van Echelpoel et al, 2015), and habitat suitability models (Ahmadi-Nedushan et al, 2006;Yi et al, 2017). A.…”

Section: Introductionmentioning

confidence: 99%

“…These models have been enhanced by the advances in landscape methods for studying freshwater ecosystems (Johnson & Host, 2010;E. However, hierarchical approaches incorporating climate, hydrologic, hydraulic, water quality, and/or physical habitat models have been suggested to improve the current models' predictability, interpretability, and accuracy (Daneshvar, Nejadhashemi, Herman, & Abouali, 2017;Einheuser et al, 2012;Einheuser, Nejadhashemi, Wang, et al, 2013;Guse et al, 2015;Holguin-Gonzalez et al, 2014;Holguin-Gonzalez, Everaert, Boets, Galvis, & Goethals, 2013;Jähnig et al, 2012;Kail et al, 2015;Kennen, Kauffman, Ayers, Wolock, & Colarullo, 2008;Woznicki, Nejadhashemi, Tang, & Wang, 2016;Yi et al, 2017). Steel et al, 2010), species distribution models (SDMs; Elith & Graham, 2009;Li & Wang, 2013;Van Echelpoel et al, 2015), and habitat suitability models (Ahmadi-Nedushan et al, 2006;Yi et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A review of macroinvertebrate‐ and fish‐based stream health modelling techniques

Hernandez‐Suarez

Nejadhashemi

2018

Ecohydrology

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During the last three decades, explaining cause-effect relationships between natural and anthropogenic disturbances with measures of stream health have motivated the growing application of statistical, machine learning, and soft computing methods.The aim of this review is to provide insight into the most widely used methods for predicting biological variables based on macroinvertebrate and fish species in riverine ecosystems. Therefore, we describe several methods including multiple linear regression, generalized linear models, generalized additive models, boosted regression trees, random forests, artificial neural networks, fuzzy logic-based, and Bayesian belief networks along with recent applications of these. Moreover, issues regarding variable selection, model interpretability, ensemble modelling, and model evaluation and overfitting are discussed. Recent advances have suggested the need for integrated modelling systems to enhance the predictive ability and improve interpretability.However, trade-offs between model complexity and accuracy demand research efforts in uncertainty quantification/propagation in model ensembles. Additionally, models should be perceived as complementary tools that require further validation with field measurements. Therefore, a consensus regarding monitoring and modelling practices for stream health applications is recommended.

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Section: Other Methodsmentioning

confidence: 99%

Section: Soft Computing Methodsmentioning

confidence: 99%

Section: Soft Computing Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A review of macroinvertebrate‐ and fish‐based stream health modelling techniques

Hernandez‐Suarez

Nejadhashemi

2018

Ecohydrology

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El Niño–Southern Oscillation impacts on jumbo squid habitat: Implication for fisheries management

Wen

Chen

et al. 2021

Aquatic Conservation

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1. Dosidicus gigas is an ecologically and economically important squid species extensively distributed in the eastern Pacific Ocean. Its habitat is extremely sensitive to climatic and environmental variability.2. The relationship between habitat pattern of D. gigas and El Niño-Southern Oscillation (ENSO, divided into the El Niño, ENSO-neutral, and La Niña events) was assessed from 1950 to 2015, using a habitat suitability index (HSI) modelling approach including two crucial environmental variables: sea surface temperature (SST) and sea surface height anomaly (SSHA).3. On the basis of cross-correlation analysis, it showed that both SST anomaly and SSHA were significantly positively related to the ENSO index. Moreover, a significantly negative association was found between the HSI values and the ENSO index.4. Due to the El Niño events, SST off Peru became higher and sea level rose, resulting in contracted areas of suitable SST and SSHA; consequently, suitable habitats for D. gigas dramatically decreased. In contrast, during the ENSO-neutral and La Niña years, the extent of suitable SST and SSHA increased due to the colder water and lower sea level, and suitable habitat for D. gigas expanded. 5. Moreover, the latitudinal gravity centre of HSI was significantly positively associated with the ENSO index. Relative to the ENSO-neutral and La Niña years, a southward movement of the monthly preferred SST isotherm for D. gigas during the El Niño years could explain the occurrence of more suitable habitats in southern waters off Peru.6. These findings suggested that the ENSO event plays an important role in regulating environmental conditions off Peru and further affected the spatio-temporal distribution of D. gigas habitat.

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Effects of climate change on the distribution of wild Akebia trifoliata

Zhang

Peng

Song

et al. 2022

Ecology and Evolution

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Understanding the impacts and constraints of climate change on the geographical distribution of wild Akebia trifoliata is crucial for its sustainable management and economic development as a medicinal material or fruit. In this study, according to the first‐hand information obtained from field investigation, the distribution and response to climate change of A. trifoliata were studied by the MaxEnt model and ArcGIS. The genetic diversity and population structure of 21 natural populations of A. trifoliata were studied by simple sequence repeat (SSR) markers. The results showed that the most important bioclimatic variable limiting the distribution of A. trifoliata was the Mean Temperature of Coldest Quarter (bio11). Under the scenarios SSP1‐2.6 and SSP2‐4.5, the suitable area of A. trifoliata in the world will remain stable, and the suitable area will increase significantly under the scenarios of SSP3‐7.0 and SSP5‐8.5. Under the current climate scenario, the suitable growth regions of A. trifoliata in China were 79.9–122.7°E and 21.5–37.5°N. Under the four emission scenarios in the future, the geometric center of the suitable distribution regions of Akebia trifoliata in China will move to the north. The clustering results of 21 populations of A. trifoliata analyzed by SSR markers showed that they had a trend of evolution from south to north.

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Evaluating the ecological influence of hydraulic projects: A review of aquatic habitat suitability models

Cited by 102 publications

References 104 publications

A review of macroinvertebrate‐ and fish‐based stream health modelling techniques

A review of macroinvertebrate‐ and fish‐based stream health modelling techniques

El Niño–Southern Oscillation impacts on jumbo squid habitat: Implication for fisheries management

Effects of climate change on the distribution of wild Akebia trifoliata

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