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Maryland Baseline Studies: Analysis
Maryland Baseline Studies: Analysis
We use several different approaches for presenting results in this study, ranging from simple maps depicting raw observation points, to more complex analyses that use these raw data in addition to remotely sensed habitat characteristics to model predicted abundances of wildlife. Understanding each analytical method and its limitations is essential to appropriately interpret maps, figures, and other analyses.

Download Wildlife Studies Offshore of Maryland. This 8-page summary publication represents an overview of results from the final technical report for the Maryland-focused study, and features survey results and case studies on marine mammals, sea turtles, and wintering seabirds. The Executive Summary for the technical report is also available here.

Additional results and case studies can be found in the 32-page synthesis report for the mid-Atlantic regional study, Mid-Atlantic Wildlife Studies: Distribution and Abundance of Wildlife along the Eastern Seaboard, 2012-2014.

 

Raw Observation Data

<p>Example A</p>
<p>In rare instances, we simply map locations of wildlife observations without additional analyses. This is straightforward, but has limitations. There are known biases in raw survey data that make it difficult to reliably compare values across space and time or between species. Because of these limitations, we only present raw survey data when there were insufficient observations to support approaches that address these sources of error. This map shows a subset of data for the Northern Gannet.</p>

Example A

In rare instances, we simply map locations of wildlife observations without additional analyses. This is straightforward, but has limitations. There are known biases in raw survey data that make it difficult to reliably compare values across space and time or between species. Because of these limitations, we only present raw survey data when there were insufficient observations to support approaches that address these sources of error. This map shows a subset of data for the Northern Gannet.

 

Persistent Hotspots of Abundance

<p>Example B</p>
<p>Persistent hotspots are locations where animals were most often found in large numbers relative to their typical distribution patterns. These areas likely provide important habitat for activities such as foraging or roosting.</p>
<p>We grouped survey observations into grid cells (lease blocks defined by the Bureau of Ocean Energy Management for offshore development). Counts were standardized by survey effort in each lease block, and blocks with the largest values within each survey were labeled as survey-specific hotspots. Blocks that were repeatedly identified as hotspots during the two years of surveys were called “persistent” hotspots. We combined boat and aerial survey data for lease blocks that were surveyed by both methods, weighting each dataset to address differences between survey methods. Hotspot maps use gradation of colors to indicate increasing hotspot persistence. Several limitations should be noted. First, hotspot maps do not indicate the full range of species’ habitat use in the mid-Atlantic; hotspots may occur in areas that were not surveyed. Second, individual grid cell “persistence” values should be interpreted with caution, as this analysis was to identify regional patterns.</p>
<p>This map shows a subset of data for the Northern Gannet.</p>

Example B

Persistent hotspots are locations where animals were most often found in large numbers relative to their typical distribution patterns. These areas likely provide important habitat for activities such as foraging or roosting.

We grouped survey observations into grid cells (lease blocks defined by the Bureau of Ocean Energy Management for offshore development). Counts were standardized by survey effort in each lease block, and blocks with the largest values within each survey were labeled as survey-specific hotspots. Blocks that were repeatedly identified as hotspots during the two years of surveys were called “persistent” hotspots. We combined boat and aerial survey data for lease blocks that were surveyed by both methods, weighting each dataset to address differences between survey methods. Hotspot maps use gradation of colors to indicate increasing hotspot persistence. Several limitations should be noted. First, hotspot maps do not indicate the full range of species’ habitat use in the mid-Atlantic; hotspots may occur in areas that were not surveyed. Second, individual grid cell “persistence” values should be interpreted with caution, as this analysis was to identify regional patterns.

This map shows a subset of data for the Northern Gannet.

 

Predictive Models

<p>Example C</p>
<p>Several statistical modeling approaches are used in this study, including generalized linear models (GLMs) and generalized additive models (GAMs). These modeling frameworks are frequently used in ecological research, and can incorporate environmental data (covariates), effort corrections, and observation biases into their structure. These approaches are used to make predictions about where animals occur and what environmental factors influence their distribution or abundance.</p>

<p>This map shows a subset of data for the Northern Gannet.</p>

Example C

Several statistical modeling approaches are used in this study, including generalized linear models (GLMs) and generalized additive models (GAMs). These modeling frameworks are frequently used in ecological research, and can incorporate environmental data (covariates), effort corrections, and observation biases into their structure. These approaches are used to make predictions about where animals occur and what environmental factors influence their distribution or abundance.

This map shows a subset of data for the Northern Gannet.

 

Temporal Variation in Relative Abundance

Bar charts were developed to summarize temporal patterns of relative abundance for species or taxonomic groups in the study area. For each survey method, we summed effort-corrected total counts of individuals by two-month time periods, so each period included data from two to four surveys. Boat and aerial bars were placed side by side to illustrate differences in detection and/or identification between the two survey methods. Larger bars represent higher effort-corrected counts of a species or group. Relative bar sizes are comparable among individual species, but not between species and broader taxonomic groups, as species and group percentiles were calculated separately.
Bar charts were developed to summarize temporal patterns of relative abundance for species or taxonomic groups in the study area. For each survey method, we summed effort-corrected total counts of individuals by two-month time periods, so each period included data from two to four surveys. Boat and aerial bars were placed side by side to illustrate differences in detection and/or identification between the two survey methods. Larger bars represent higher effort-corrected counts of a species or group. Relative bar sizes are comparable among individual species, but not between species and broader taxonomic groups, as species and group percentiles were calculated separately.
 

For More Information

For more information, see Chapters 1, 9, 11, 12, 13, and 14 in the technical report.
 

References

  • Burnham KP, Anderson DR (1984) The need for distance data in transect counts. Journal of Wildlife Management 48:1248–1254
  • Gardner B, Sullivan PJ, Epperly S, Morreale SJ (2008) Hierarchical modeling of bycatch rates of sea turtles in the western North Atlantic. Endangered Species Research 5:279–289. doi: 10.3354/esr00105
  • Guisan A, Edwards Jr TC, Hastie T (2002) Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecological Modelling 157:89–100
  • Santora JA, Veit RR (2013) Spatio-temporal persistence of top predator hotspots near the Antarctic Peninsula. Marine Ecology Progress Series 487:287–304. doi:10.3354/meps10350
  • Spear LB, Ainley DG, Hardesty BD, Howell SNG, Webb SW (2004) Reducing biases affecting at-sea surveys of seabirds: Use of multiple observer teams. Marine Ornithology 32:147–157
  • Zipkin EF, Gardner B, Gilbert AT, O’Connell AF, Royle JA, Silverman ED (2010) Distribution patterns of wintering sea ducks in relation to the North Atlantic Oscillation and local environmental characteristics. Oecologia 163:893–902
 
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