Evaluating spatio‐temporal variability in the habitat quality of Atlantic cod (<i>Gadus morhua</i>) in the Gulf of Maine

Guan, Lisha; Chen, Yong; Wilson, James A.

doi:10.1111/fog.12188

Cited by 16 publications

(7 citation statements)

References 39 publications

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“…Habitat suitability models were used to assign habitat suitability scores and to quantify the extent of suitable habitat for forage fishes throughout the Chesapeake Bay and its tributaries from 2000 to 2016 for each season. Habitat suitability models were estimated with the non-parametric histogram approach because this approach makes no assumption about the nature of the relationship between environmental features and fish abundance (Guan et al, 2016). Briefly, thresholds of environmental conditions that resulted in a gradient of suitability from least suitable (0) to most suitable (1) were identified for each influential habitat covariate for each species.…”

Section: Habitat Suitability Modelsmentioning

confidence: 99%

“…We applied a data-driven approach, boosted regression tree analysis (Elith et al, 2008), to select a subset of habitat covariates that were most influential in explaining fish relative abundance. Non-parametric suitability models using the histogram approach (Tanaka and Chen, 2015;Guan et al, 2016) were then constructed using the selected influential environmental covariates. Higher suitability is ascribed to conditions in which greater abundances of organisms are observed, and as such, habitat suitability models are processbased models.…”

Section: Introductionmentioning

confidence: 99%

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The Extent of Seasonally Suitable Habitats May Limit Forage Fish Production in a Temperate Estuary

Fabrizio

Tuckey

Bever³

et al. 2021

Front. Mar. Sci.

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The sustained production of sufficient forage is critical to advancing ecosystem-based management, yet factors that affect local abundances and habitat conditions necessary to support aggregate forage production remain largely unexplored. We quantified suitable habitat in the Chesapeake Bay and its tidal tributaries for four key forage fishes: juvenile spotted hake Urophycis regia, juvenile spot Leiostomus xanthurus, juvenile weakfish Cynoscion regalis, and bay anchovy Anchoa mitchilli. We used information from monthly fisheries surveys from 2000 to 2016 coupled with hindcasts from a spatially interpolated model of dissolved oxygen and a 3-D hydrodynamic model of the Chesapeake Bay to identify influential covariates and construct habitat suitability models for each species. Suitable habitat conditions resulted from a complex interplay between water quality and geophysical properties of the environment and varied among species. Habitat suitability indices ranging between 0 (poor) and 1 (superior) were used to estimate seasonal and annual extents of suitable habitats. Seasonal variations in suitable habitat extents in Chesapeake Bay, which were more pronounced than annual variations during 2000–2016, reflected the phenology of estuarine use by these species. Areas near shorelines served as suitable habitats in spring for juvenile spot and in summer for juvenile weakfish, indicating the importance of these shallow areas for production. Tributaries were more suitable for bay anchovy in spring than during other seasons. The relative baywide abundances of juvenile spot and bay anchovy were significantly related to the extent of suitable habitats in summer and winter, respectively, indicating that Chesapeake Bay habitats may be limiting for these species. In contrast, the relative baywide abundances of juvenile weakfish and juvenile spotted hake varied independently of the spatial extent of suitable habitats. In an ecosystem-based approach, areas that persistently provide suitable conditions for forage species such as shoreline and tributary habitats may be targeted for protection or restoration, thereby promoting sufficient production of forage for predators. Further, quantitative habitat targets or spatial thresholds may be developed for habitat-limited species using estimates of the minimum habitat area required to produce a desired abundance or biomass; such targets or thresholds may serve as spatial reference points for management.

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Section: Habitat Suitability Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Extent of Seasonally Suitable Habitats May Limit Forage Fish Production in a Temperate Estuary

Fabrizio

Tuckey

Bever³

et al. 2021

Front. Mar. Sci.

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“…HSI models are typically used for understanding a species' response to changes in habitat (Terrell and Carpenter 1997). Many HSI models assume that an organism's habitat preference does not change on an annual basis; distribution might, but the underlying relationship does not (Chen et al 2011;Tanaka and Chen 2015;Guan et al 2017). For the HSI models in this study to provide such insight, annual predictions would need to be made.…”

Section: Model Limitationsmentioning

confidence: 99%

“…The HSI can be projected spatially and temporally, providing valuable representation of changes in habitat quality over space and time and potential locations of a species' critical habitat (Chen et al 2009). Such information is critical for implementing spatially explicit conservation management (Brooks 1997), evaluating shifts in habitat quality over time (Guan et al 2017;Tanaka and Chen 2016), or understanding the impact of climate variability on suitable habitat distribution (Yu et al 2016). Recent efforts have been made to improve the predictive capabilities of spatially projected HSIs (Tanaka and Chen 2015;Tanaka and Chen 2016).…”

Section: R a F T 1 Introductionmentioning

confidence: 99%

Habitat suitability modeling based on a spatiotemporal model: an example for cusk in the Gulf of Maine

Runnebaum

Guan

Cao

et al. 2018

Can. J. Fish. Aquat. Sci.

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24Habitat use and distribution is a critical aspect in the management and conservation of a species, shortcomings of conventional HSIs: 1) the abundance indices from survey catch data typically 58 incorporated in these models do not account for changes in catchability over a time series; and 2) 59 the commonly used abundance indices, and therefore HSIs, are unable to incorporate surveys 60 from multiple gear types which sample different segments of the population and likely cover 61 different types of habitat. These issues need to be addressed in order to produce an unbiased 62 evaluation of spatio-temporal changes in habitat quality for a species over its distributional 63 range. Conventional HSIs use available data from sampled locations, hereinafter referred to as sample- 78based HSIs, which are often restricted to the locations of occurrence and typically processed to 79 assume that the samples are representative (i.e., the species is effectively sampled) and are 80 comparable through time (i.e., no changes in sampling distribution and efficiency). Therefore, 81 the sample-based HSIs might not be appropriate in at least the following two situations: 1) the 82 survey misses a significant portion or type of the species' habitat; and 2) sampling efficiency 83 (i.e., catchability) changes over space and/or through time due to density-dependent processes. 84Density-dependent habitat selection is a likely process for species in decline (MacCall 1990). 85When a species population is high, individuals move into previously marginal habitat because 86 high quality habitat is saturated; thus, the overall suitability of all occupied habitat declines on 87 average (MacCall 1990 Cusk (Brosme brosme) in the Gulf of Maine is one species where assessment is difficult using 104 conventional HSIs. It is a data-limited species, with low abundance and low catchability. central GOM to better sample species that primarily reside in complex habitat (Hoey et al. 2013). 146Six survey strata were selected for the LLS from ten offshore and four inshore strata from the The study area was divided into 5,710 cells (0.05º x 0.05º) for predicting grid-based densities, The second stage of the model approximates positive catches (c): 199The probability density function Gamma (c, x, y) is evaluated at c given a gamma distribution, knots that are generated based on the proportional density of survey data over the defined 208 domain (i.e., the 0.05º x 0.05º grid; Thorson et al. 2015). The spatial (ω) and spato-temporal (ε) 209 random effects were used in both spring and fall density estimates. 211Encounter probability p and positive catch rates λ are approximated using linear predictors 212( Thorson et al. 2015): 215where ‫‬ and ߣ are the expected probabilities of an occupied habitat and positive catches given 216 occupied habitat for sample i at a given location; ݀ ் () is the average reference density 217(encounters/positive catch rates) in year ܶ () ; ܳ is catchability for each survey; w i is the area 218 swept for sample i;...

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“…Currently, several studies on coastal habitat quality and ecological risk have been conducted globally. Habitat quality and ecological risks have been assessed in different coastal regions worldwide (Yu et al, 2015;Guan et al, 2017;Abreu et al, 2021;Ding et al, 2021). The assessment of habitat quality placed major stress on single species (Meng et al, 2004) and biological communities (Landry and Golden, 2019).…”

Section: Introductionmentioning

confidence: 99%

Variations of Habitat Quality and Ecological Risk and Their Correlations With Landscape Metrics in a Robust Human Disturbed Coastal Region

Huiqing

Yang

2021

Preprint

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As impacted by strong intense human activity disturbance, the landscape pattern change significantly affects the habitat quality and ecological risk level in the coastal regions. Though numerous researches were conducted in coastal habitat quality and ecological risk, the correlations of habitat quality and ecological risk with landscape metrics in coastal regions have been rarely reported. A scientific question should be addressed is the scientific basis for the sustainable development of coastal regions. Accordingly, Xinggang town, a coastal region in southern China, was selected as a typical coastal region disturbed by robust human activities. Given high-precision remote sensing data, the temporal-spatial variations of habitat quality and ecological risk in the coastal region were analyzed by adopting the methods of InVEST model and ecological risk index, and the correlations of habitat quality and ecological risk with landscape metrics were examined. As indicated from the results, the deterioration of habitat quality and the increase in ecological risk in the coastal region under the disturbance of human activities exhibited obvious distance gradients. The gradient area close to the coastline was the area with the prominent variations of habitat quality and ecological risk. Most landscape metrics were positively correlated with habitat quality and ecological risk, and the correlations were different in a range of distance gradients. Under the rapid urbanization in the coastal region, the rapid expansion of built-up land and the decrease of natural landscapes significantly impacted the landscape pattern index and then altered the habitat quality and ecological risk level.

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Evaluating spatio‐temporal variability in the habitat quality of Atlantic cod (Gadus morhua) in the Gulf of Maine

Cited by 16 publications

References 39 publications

The Extent of Seasonally Suitable Habitats May Limit Forage Fish Production in a Temperate Estuary

The Extent of Seasonally Suitable Habitats May Limit Forage Fish Production in a Temperate Estuary

Habitat suitability modeling based on a spatiotemporal model: an example for cusk in the Gulf of Maine

Variations of Habitat Quality and Ecological Risk and Their Correlations With Landscape Metrics in a Robust Human Disturbed Coastal Region

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