Biogeomorphic classification and images of shorebird nesting sites on the U.S. Atlantic coast

Sturdivant, Emily J.; Thieler, E. Robert; Zeigler, Sara L.; Winslow, Luke; Hines, Megan K.; Read, Jordan S.; Walker, Jordan

doi:10.5066/f70v89x3

Cited by 4 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…All supporting data can be found in previous publications (Lentz al., 2015b;Sturdivant et al, 2016Sturdivant et al, , 2019. We thank our federal, state, and private collaborators who supervised, participated in, and coordinated field-testing and data collection for the dataset used to train the Piping Plover Habitat BN.…”

Section: Data Availability Statementmentioning

confidence: 99%

“…This prior work used a dataset of landscape characteristics at piping plover nests and random points located in the species' New York-New Jersey recovery unit (Fire Island and the Rockaway Peninsula, New York; Long Beach and Pullen islands, New Jersey). This dataset contained 335 points comprised of 178 nest locations and 157 random points (Sturdivant et al, 2016;Thieler et al, 2016;Zeigler et al, 2017). Characteristics for the variables geomorphic setting, substrate type, vegetation type, and vegetation density were observed in-situ (detailed variable definitions in Zeigler et al, 2017).…”

Section: Piping Plover Habitat Bayesian Networkmentioning

confidence: 99%

See 1 more Smart Citation

Predicted Sea‐Level Rise‐Driven Biogeomorphological Changes on Fire Island, New York: Implications for People and Plovers

et al. 2022

Self Cite

View full text Add to dashboard Cite

Forecasting biogeomorphological conditions for barrier islands is critical for informing sea‐level rise (SLR) planning, including management of coastal development and ecosystems. We combined five probabilistic models to predict SLR‐driven changes and their implications on Fire Island, New York, by 2050. We predicted barrier island biogeomorphological conditions, dynamic landcover response, piping plover (Charadrius melodus) habitat availability, and probability of storm overwash under three scenarios of shoreline change (SLC) and compared results to observed 2014/2015 conditions. Scenarios assumed increasing rates of mean SLC from 0 to 4.71 m erosion per year. We observed uncertainty in several morphological predictions (e.g., beach width, dune height), suggesting decreasing confidence that Fire Island will evolve in response to SLR as it has in the past. Where most likely conditions could be determined, models predicted that Fire Island would become flatter, narrower, and more overwash‐prone with increasing rates of SLC. Beach ecosystems were predicted to respond dynamically to SLR and migrate with the shoreline, while marshes lost the most area of any landcover type compared to 2014/2015 conditions. Such morphological changes may lead to increased flooding or breaching with coastal storms. However—although modest declines in piping plover habitat were observed with SLC—the dynamic response of beaches, flatter topography, and increased likelihood of overwash suggest storms could promote suitable conditions for nesting piping plovers above what our geomorphology models predict. Therefore, Fire Island may offer a conservation opportunity for coastal species that rely on early successional beach environments if natural overwash processes are encouraged.

show abstract

Section: Data Availability Statementmentioning

confidence: 99%

Section: Piping Plover Habitat Bayesian Networkmentioning

confidence: 99%

Predicted Sea‐Level Rise‐Driven Biogeomorphological Changes on Fire Island, New York: Implications for People and Plovers

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

Piping plovers demonstrate regional differences in nesting habitat selection patterns along the U.S. Atlantic coast

et al. 2021

Self Cite

View full text Add to dashboard Cite

Habitat studies that encompass a large portion of a species' geographic distribution can explain characteristics that are either consistent or variable, further informing inference from more localized studies and improving management successes throughout the range. We identified landscape characteristics at Piping Plover nests at 21 sites distributed from Massachusetts to North Carolina and compared habitat selection patterns among the three designated U.S. recovery units (New England, New York-New Jersey, and Southern). Geomorphic setting, substrate type, and vegetation type and density were determined in situ at 928 Piping Plover nests (hereafter, used resource units) and 641 random points (available resource units). Elevation, beach width, Euclidean distance to ocean shoreline, and least-cost path distance to low-energy shorelines with moist substrates (commonly used as foraging habitat) were associated with used and available resource units using remotely sensed spatial data. We evaluated multivariate differences in habitat selection patterns by comparing recovery unit-specific Bayesian networks. We then further explored individual variables that drove disparities among Bayesian networks using resource selection ratios for categorical variables and Welch's unequal variances t-tests for continuous variables. We found that relationships among variables and their connections to habitat selection were similar among recovery units, as seen in commonalities in Bayesian network structures. Furthermore, nesting Piping Plovers consistently selected mixed sand and shell, gravel, or cobble substrates as well as areas with sparse or no vegetation, irrespective of recovery unit. However, we observed significant differences among recovery units in the elevations, distances to ocean, and distances to low-energy shorelines of used resource units. Birds also exhibited increased selectivity for overwash habitats and for areas with access to low-energy shorelines along a latitudinal gradient from north to south. These results have important implications for conservation and management, including assessment of shoreline stabilization and habitat restoration planning as well as forecasting effects of climate change.

show abstract

A pragmatic approach for comparing species distribution models to increasing confidence in managing piping plover habitat

Maslo

Zeigler

Drake

et al. 2019

Conservat Sci and Prac

Self Cite

View full text Add to dashboard Cite

Conservation management often requires decision-making without perfect knowledge of the at-risk species or ecosystem. Species distribution models (SDMs) are useful but largely under-utilized due to model uncertainty. We used an ensemble modeling approach of two independently derived SDMs to explicitly address common modeling impediments and directly inform conservation decision-making for piping plovers in a heavily populated mid-Atlantic (USA) coastal zone. We summarized previously published Bayesian network and maximum entropy models to highlight similarities and differences in structure, and we compared the relative importance of predictors used. Despite differences in analytical approach, relative importance of factors driving nest-site selection was consistent. Models demonstrated considerable agreement when comparing a binary (suitable/ unsuitable) measure of suitability. Instances of model consensus (i.e., overlapping areas of predicted piping plover nesting habitat between models) provide a stronger "signal" in model results, reducing uncertainty related to biases or errors associated with either model. We tested model accuracy using a common dataset of plover nests initiated within the focal areas between 2013 and 2015, and we examined congruency in model outputs. Nearly, 90% of all nests occurred in areas predicted suitable by at least one model, and at least 33% of the total nests were predicted in areas suitable by both. Because models predominantly agreed on what drives piping plover nest-site selection, areas predicted suitable by a single model should not be discounted. This case study demonstrates how models can effectively inform conservation planning by explicitly identifying the management objective, presenting robust evidence to allow managers to evaluate outcomes of alternative management decisions, and clearly communicating results that address real-world conservation problems. Our results can

show abstract

Biogeomorphic classification and images of shorebird nesting sites on the U.S. Atlantic coast

Cited by 4 publications

References 0 publications

Predicted Sea‐Level Rise‐Driven Biogeomorphological Changes on Fire Island, New York: Implications for People and Plovers

Predicted Sea‐Level Rise‐Driven Biogeomorphological Changes on Fire Island, New York: Implications for People and Plovers

Piping plovers demonstrate regional differences in nesting habitat selection patterns along the U.S. Atlantic coast

A pragmatic approach for comparing species distribution models to increasing confidence in managing piping plover habitat

Contact Info

Product

Resources

About