Evaluating effects of rescaling and weighting data on habitat suitability modeling

Xue, Ying; Guan, Lisha; Tanaka, Kisei R.; Li, Zengguang; Chen, Yong; Ren, Yiping

doi:10.1016/j.fishres.2016.12.001

Cited by 39 publications

(20 citation statements)

References 39 publications

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“…; Xue et al. ). Using this method, weights were assigned to each environmental variable corresponding to its relative contribution (%) to the deviance explained in the BRT model (Xue et al.…”

Section: Methodsmentioning

confidence: 98%

“…Using this method, weights were assigned to each environmental variable corresponding to its relative contribution (%) to the deviance explained in the BRT model (Xue et al. ). The BRTs were developed with the “gbm.step” function within the R package “gbm” (Ridgeway ).…”

Section: Methodsmentioning

confidence: 99%

“…The SIs were then combined to form a composite HSI (also on a 0–1 scale) using an arithmetic mean model (Xue et al. ),

HSI = \frac{1}{\sum_{i = 1}^{n} w_{i}} \times \sum_{i = 1}^{n} {SI}_{i} w_{i},

where SI i represents an SI value associated with the i th environmental variable; w i represents the weight of variable i based on BRT results; and n represents the number of environmental variables included in the arithmetic mean model (AMM). In this study, only the AMM was used to generate HSI predictions, as previous studies have shown consistently better performance of this model over a geometric mean model (Tanaka and Chen , ).…”

Section: Methodsmentioning

confidence: 99%

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A spatiotemporal Evaluation of Atlantic Sea Scallop Placopecten magellanicus Habitat in the Gulf of Maine Using a Bioclimate Envelope Model

2018

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A bioclimate envelope model was developed to evaluate the impacts of climate variability on spatiotemporal availability of suitable habitat for the Atlantic sea scallop Placopecten magellanicus in the Gulf of Maine (GOM). Bioclimate envelopes were established through habitat suitability indices (HSIs) based on bottom temperature, bottom salinity, current velocity, depth, and bottom composition. The relationship between Atlantic sea scallop abundance and each environmental variable was quantified using suitability indices, which were generated based on standardized scallop abundance sampled over 10 years of dredge survey data. Boosted regression tree models were used to determine the relative importance of each environmental variable to scallop abundance, thereby establishing a weighting scheme within the HSI. A regional circulation model was coupled with the weighted HSI to hindcast spatiotemporal dynamics of suitable habitat for Atlantic sea scallop in coastal and offshore waters of the GOM from 1978 to 2013. Higher habitat suitability was found along inshore areas compared with offshore areas. Model predictions indicated an increasing trend in habitat suitability in inshore waters since 1978 and decreasing habitat suitability in offshore waters. This research provides a novel modeling framework with which to enhance research and management of commercially valuable Atlantic sea scallop stocks over broad spatiotemporal scales in the climatically altered GOM.

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Section: Methodsmentioning

confidence: 98%

Section: Methodsmentioning

confidence: 99%

“…The SIs were then combined to form a composite HSI (also on a 0–1 scale) using an arithmetic mean model (Xue et al. ),

HSI = \frac{1}{\sum_{i = 1}^{n} w_{i}} \times \sum_{i = 1}^{n} {SI}_{i} w_{i},

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A spatiotemporal Evaluation of Atlantic Sea Scallop Placopecten magellanicus Habitat in the Gulf of Maine Using a Bioclimate Envelope Model

2018

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“…Following the 2015 benchmark assessment, both spring and fall surveys were assumed to have a linear catchability relationship that remained constant during 2000-2014 (ASMFC 2015a). Therefore, lobster catch density in this study was used as a proxy for distribution and abundance, which assumed that lobster catches reflected the presence/absence and density of the species at a given location within the study area, and not confounded by bias associated D r a f t with sampling efficiency and environmental variability (Chang et al 2010;ASMFC 2015a;Tanaka and Chen 2016;Xue et al 2017).…”

Section: Datamentioning

confidence: 99%

Mesoscale climatic impacts on the distribution ofHomarus americanusin the US inshore Gulf of Maine

Tanaka

Chang

Xue

et al. 2019

Can. J. Fish. Aquat. Sci.

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American lobster (Homarus americanus) supports one of the most valuable fisheries in the United States. Spatial distributions of H. americanus are hypothesized to be influenced by climate-driven environmental factors, but such effects have not been quantified. We developed a Tweedie generalized additive model (GAM) to quantify environmental effects on season-, sex-, and size-specific distributions of H. americanus in the inshore Gulf of Maine. Tweedie GAMs were coupled with regional circulation model output to predict spatiotemporal changes in distribution of H. americanus due to mesoscale climate variability. GAM results indicated that bottom temperature and salinity impacts on H. americanus distribution were more pronounced during spring. The coupled climate–niche model predicted significantly higher H. americanus abundance under a warm climatology scenario. This study provides a predictive climate–niche modelling framework that may be useful for planning fishery investments and anticipating management challenges given ongoing climate-driven changes in the Northwest Atlantic.

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“…Habitat suitability index (HSI) modeling has become a broadly used and powerful tool to provide habitat preference information, which usually combines abundance index (AI) and oceanographic variables (such as temperature, salinity, CHLA, oxygen concentration, etc.) using a variety of different methods (Chen et al, 2009;Tian et al, 2009;Chang et al, 2013;Tanaka and Chen, 2015;Yu et al, 2016aYu et al, ,b, 2019Xue et al, 2017). The spatial distribution of cephalopods in the YS and the relationship between their AI and oceanographic variables, especially for non-target species, are rarely known.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Oceanographic Impacts on the Spatial Distribution of Common Cephalopods During Autumn in the Yellow Sea

Jin

Gorfine

et al. 2020

Front. Mar. Sci.

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Evaluating effects of rescaling and weighting data on habitat suitability modeling

Cited by 39 publications

References 39 publications

A spatiotemporal Evaluation of Atlantic Sea Scallop Placopecten magellanicus Habitat in the Gulf of Maine Using a Bioclimate Envelope Model

A spatiotemporal Evaluation of Atlantic Sea Scallop Placopecten magellanicus Habitat in the Gulf of Maine Using a Bioclimate Envelope Model

Mesoscale climatic impacts on the distribution ofHomarus americanusin the US inshore Gulf of Maine

Modeling the Oceanographic Impacts on the Spatial Distribution of Common Cephalopods During Autumn in the Yellow Sea

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