Bird Restoration Monitoring

WHY RESTORE BIRDS?

Birds are conspicuous organisms that often generate support for multi-scale restoration efforts. The seasonally and geographically diverse birdlife (see Box II.1) across the Gulf of Mexico is highly valued by the public, both for consumptive (e.g., hunting-related activities) and non-consumptive (e.g., wildlife viewing and photography activities) uses (NSRE, 2010). Wildlife-watchers in the five Gulf coast states, including birders, numbered over 16.4 million and spent more than $22.5 billion on various activities in the region (U.S. Fish and Wildlife Service and U.S. Census Bureau, 2006). Furthermore, birds provide several other ecosystem services including several functions potentially relevant to the Gulf of Mexico ecosystem (Sekercioglu et al., 2004; Sekercioglu, 2006; Green and Elmberg, 2014). For example, many songbirds serve as important seed dispersers in the region, seabirds and wading birds could serve as nutrient depositors, and insectivorous and raptor species could limit insect and rodent damage, respectively, to Gulf coast habitats, crops, and homes. Petroleum oils threaten Gulf coast birds (and the services they provide) primarily through coating of feathers, ingestion, and egg shell contamination (Leighton, 1993). As a result, “[the] quantified mortalities were estimated to range from 51,600 to 84,500 individual birds” (DWH NRDA Trustees, 2016, p. 4-483) and when combined with the loss of productivity, estimate range as high as 56,100 to 102,400. However, other earlier estimates were significantly higher (Haney et al., 2014a,b; Sackmann and Becker, 2015).

RESTORATION OBJECTIVES

The objective of restoring habitat upon which birds depend is widely considered useful and a frequently necessary element of effective bird restoration. Beyond this overarching objective, other (often related) restoration objectives can include reducing threats to birds, enhancing other supporting species, promoting ecosystem services, understanding cause-and-effect relationships that drive observed annual variation in bird populations, and understanding the impacts of management and restoration actions on birds. For example, a restoration project focused on creating emergent marsh habitat may not necessarily focus on marsh birds as a restoration objective, per se. Thus, one potential restoration target might be a desire to “produce [X] number of breeding clapper rails and [X] number of breeding seaside sparrows,” an objective based on population status. For a project using multiple techniques to restore barrier island beach habitat, an objective might be “to provide wintering habitat for [X] number of wintering piping plovers.” Alternatively, one might ask “how many nesting pairs of Wilson’s plovers are produced using beach nourishment technique [Y] versus technique [Z]?” Many of these examples of potential objectives are being evaluated by and

can be drawn from the Gulf of Mexico Avian Monitoring Network (GoMAMN).¹

Depending upon the chosen restoration objectives, it is generally good practice to consider issues such as the state of other habitats visited/used by target bird species, species-dependent responses of birds to restoration activities, the extent to which restoration is supplemented with management activities that may affect targeted populations, and the spatial scales (site-specific to regional) at which restoration objectives will be evaluated (George and Zack, 2001; Marzluff and Ewing, 2001; Smallwood, 2001; Morrison, 2002; Ausden, 2004; Sutherland et al., 2004; Ortega-Alvarez and Lindig-Cisneros, 2012; Hutto et al., 2014). Given that many of the Gulf bird populations migrate across borders, monitoring would also benefit from including Mexican coastal sites as well and Caribbean Islands.

Why Monitor Bird Restoration?

Birds are perhaps the most studied and well-known wildlife worldwide. They have repeatedly been used as focal species because they can generally be surveyed at relatively low cost over large geographic areas, demographic parameters can be relatively easily assessed, and ecological requirements and habitat associations fairly well known for many species with diverse life history traits across a wide range of habitats (Gardali et al., 2006; Majer, 2009). Bird taxa often exhibit strong site fidelity and predictable diets (Novak et al. 2006; Rush et al. 2009); occupy high positions in trophic food webs, are important components of ecosystem energy flows, can move in response to ecological conditions (Ogden et al., 2014); may provide an indication of habitat integrity (DeLuca et al. 2004, 2008; Novak et al. 2006); and often respond quickly to restoration efforts (Bergeron-Burns et al., 2014). Also, many species using the Gulf are transients, or migrate through the region, so have the potential to serve as short-term, localized indicator species (Henkel et al., 2014). Further, birds regularly engage the public in citizen science activities (Ortega-Alvarez and Lindig-Cisneros, 2012).

DECISION-CRITICAL UNCERTAINTIES

Conceptual models are particularly useful early in a project (Ogden et al., 2005, 2014), when there is a need to focus on the important drivers, stressors, and attributes at the exclusion of a detailed ecological modeling approach (Gawlik, 2006). Although widely acknowledged as important, relatively few examples of bird-specific conceptual models are presented in the peer-reviewed literature (but see Figure II.4). Rather, birds are typically incorporated into multi-component ecosystem-based models. For example, Gentile et al. (2001; see Figure 7) used conceptual models to better understand the potential influence of ecosystem management on the sustainability of the Everglades and South Florida ecosystems.

Many uncertainties exist around bird response to various restoration techniques. For colonial waterbirds and solitary beach nesting shorebirds, the importance of sculpting or shaping the beach morphology of renourished islands to facilitate rapid colonization is poorly known and has been identified as a critical information gap (Guilfoyle et al., 2006; Grippo et al., 2007). Despite the use of a rigorous and robust before-after-control-impact paired study design (see Chapter 3), Grippo et al. (2007) found no significant changes in mean waterbird or shorebird abundance after replenishment, and consequently strongly recommend the use of multiple control sites and scheduling surveys to reduce potential sources of variability. The utility of dredged material as a management tool for colonial nesting habitat for terns and skimmers is a second important area of uncertainty in bird restoration. Although poorly understood outside the southeast Atlantic Coast, guidance on how to create and manage dredged-materials islands as early successional bird habitat is available as a starting point for Gulf of Mexico projects (Golder et al., 2008).

There is also significant uncertainty around the response of marsh birds to various approaches to marsh restoration (such as those listed in the tidal wetland section of Part II) and many uncertainties are specific to individual species. Additional uncertainty is due to our lack of empirical data linking sea-level rise to effects on marsh bird populations and reproductive success. In addition to sea level rise, climate-related drivers that could affect Gulf marsh birds include precipitation patterns, hydrologic and fire regimes, increased temperature, and hurricanes (Woodrey et al., 2012). One tidal marsh restoration technique that has promise is

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¹ The Gulf of Mexico Avian Monitoring Network (GoMAMN): https://globalchange.ncsu.edu/secsc/wpcontent/uploads/GoMAMN-2-Pager-final.pdf.

hydrologic, or tidal flow, restoration, although there is a high degree of uncertainty with regards to marsh bird response and thus a critical need to monitor in an adaptive management framework (Mitchell et al., 2006; Shriver and Greenberg, 2012). Regardless of the marsh restoration objective, the initial steps of creating an engineered, or sculpted, marsh involve careful attention to create the appropriate hydrology, elevation, and spoil characteristics (Palmer et al., 1997; Turner and Streever, 2002; Armitage et al., 2014). Fortunately, specific, testable hypotheses for estuarine systems can provide a framework for reducing uncertainties if used under an adaptive management framework (Conroy et al., 2010).

Long-legged wading birds such as herons, egrets, ibises, and storks are generally both highly mobile and responsive to landscape-scale changes in foraging and nesting habitat. These birds respond to prey availability, which is influenced by both prey density and water depth (Gawlik, 2002). Areas of concentrated prey resources are generally created in salt marshes by having shallow areas (at least at low tide) and pools that trap prey in the high marsh. Maintaining these features within a restored wetland complex would be key to maintaining these critical marsh bird resources. In addition, freshwater flow regime can strongly affect both vegetative structure of foraging habitat and prey density through changes in salinity (Bildstein et al., 1990; Battaglia et al., 2012; Lorenz, 2014). Therefore, a key uncertainty for bird response to restoration is whether restoration projects will retain critical freshwater flows, and what those regimes will be.

Another key uncertainty is whether long-legged wading birds will respond to positive changes that are intended to make habitats more suitable (e.g., increased food resources, appropriate hydrology) by being attracted to this habitat if it occurs. However, at a larger scale, these birds may simply concentrate at more suitable distant sites. The ability to say that wading birds definitely are not responding to restoration is highly dependent on making the argument that they are not somewhere else, which argues strongly for regional monitoring. This argument is clear in the Everglades, which is large enough to constitute a region (Frederick and

Ogden, 2003). A third key uncertainty may have to do with nest predators. Although nesting is probably driven mostly by food availability (Frederick and Spalding, 1994; Beerens et al., 2015), protection from mammalian nest predators is a pre-condition for any reproductive success. Therefore, in order to use wading bird nesting as a metric of foraging habitat suitability, the assumption of predator-free nesting space needs to be demonstrated. If predator-free, then birds will nest on almost any substrate, and will often nest in close proximity to human activity (Nell and Frederick, 2015; Nell et al., 2016; Tsai et al., 2016).

PROJECT-LEVEL MONITORING AND ASSESSMENT PLAN CONSIDERATIONS

Information Needs Based on Monitoring Purpose and Project Objectives

As defined in Part I of this report, the three primary purposes of restoration monitoring include (1) assuring projects are built and are initially functioning as designed (construction monitoring); (2) assessing whether restoration goals and objectives have been or are being met (performance monitoring); and (3) informing restoration management, improving design of future restoration efforts, and increasing ecosystem understanding (monitoring for adaptive management). Bird-focused restoration projects, like all objective-driven restoration projects, will require various forms of monitoring (Hutto and Belote, 2013), with projects involving the placement and sculpting of sediment requiring a broader array of monitoring purposes.

Construction Monitoring

Although construction monitoring may seem tangential to bird-focused restoration projects, assessing the creation of habitat for colonial nesting waterbirds, solitary nesting shorebirds, and secretive marsh birds is critical. Stringent oversight during the construction phase is necessary (1) to ascertain specific quantitative recommendations available in the literature with regards to substrate composition, presence/absence of vegetation, vegetation species composition, vegetation height, elevation, slope, etc. (Golder et al., 2008); and (2) to ensure that physical construction requirements are met.

Performance Monitoring

Measuring restoration effectiveness on bird populations can be difficult for several reasons. Primarily, the population is responding at a scale much larger than the project and responding to factors outside the restoration project. Furthermore, detecting change in faunal populations is difficult, especially for relatively small marsh restoration projects. Vegetation changes over a project’s lifetime can all contribute to increased variation in estimates of bird use for a given project. This increased variation can make statistical comparisons to evaluate restoration success problematic and any efforts to reduce this variation need to be employed. Shriver and Greenberg (2012) provide three recommendations to help reduce variance in bird use estimates: surveys need to (1) adhere to standardized protocols, including guild definitions, such as proposed by Konisky et al. (2006); (2) conduct post-restoration monitoring for longer time periods (up to 15 years); and (3) integrate newly developed analytical techniques such as occupancy modelling (MacKenzie et al., 2006).

Performance monitoring for bird-focused restoration projects need to collect information that is linked to the restoration project objectives. Monitoring efforts to assess progress towards these objectives focus on three broad categories of information needs: (1) abundance, (2) community composition, and/or (3) reproductive rates. For example, a project objective of restoring [X] acres of tidal marsh could measure the relative abundance (i.e., the number of individuals of a particular type as a percentage of the overall number in a given area) of breeding clapper rails and seaside sparrows (see Table II.4). If one is less interested in targets for specific species and more interested in ecological function, an appropriate objective might be “producing [X] acres of functioning sand beach habitat for wintering shorebirds.” This objective links to metrics such as species richness, avian community diversity, and/or a community level integrity index, as compared to some appropriate reference data set (see Table II.4). Yet another project might focus on achieving a minimum viable population. Such projects may measure fledglings per nest in addition to population size for beach nesting Wilson’s plovers. As the combination of specific metrics from and across these categories is potentially overwhelming, clearly worded objective(s) are critical to successful performance monitoring.

Monitoring for Adaptive Management

Management of natural resources is characterized by uncertainty, including in our ability to predict bird response to restoration or other management activities (Runge et al., 2011). Thus, framing site-level restoration projects or a group of similar restoration projects across a region within an adaptive management framework provides feedback for learning, or reducing uncertainty, about the system (Lyons et al., 2008). Nichols and Williams (2006) refer to this approach as “targeted” (or focused) monitoring, which they associate with “integration into conservation practice, with monitoring design and implementation based on a priori hypotheses and associated models of system responses to management.”

Understanding existing uncertainties (as well as which type(s) of uncertainty are being considered) and prioritizing reduction of uncertainty components is not an easy task (Williams, 2011). Although a few examples of relevant uncertainties are briefly outlined above, there are myriad possibilities and many approaches for making sense of these uncertainties (e.g.,Williams, 1997, 2001, 2011; Gregory and Keeney, 2002; Runge et al., 2011; Moore and Runge, 2012). Also, the GoMAMN group has initiated an effort to identify and prioritize critical avian-related uncertainties from both a management action and ecological processes perspective (Wilson, 2015). Thus, much of the bird-related background work is being done by a group of avian experts and is available to restoration practitioners along the Gulf of Mexico to help define restoration objectives and associated avian monitoring efforts to evaluate project success.

Choose Suitable Metrics

In the case of bird restoration monitoring, suitable metrics will include individual species or species groups. For the former, GoMAMN has compiled a list of priority birds useful for Northern Gulf of Mexico bird monitoring objectives and priorities. The list consists of approximately 100 species broadly grouped into guilds, including land birds, marsh birds, pelagic seabirds, raptors, seabirds, shorebirds, wading birds, and waterfowl (Wilson, 2015).² Although other species may be of interest at the projectlevel or local geography, it is good practice to include priority species from this list (at a minimum) as metrics for Gulf of Mexico restoration projects with bird-focused objectives and design (Sanderlin et al., 2014). A variety of metrics are available for evaluating avian response to restoration (see Table II.4). Practitioners might be tempted to choose the least rigorous of the approaches outlined below as a cost-saving measure, but to assess restoration performance, the metric(s) chosen should be directly linked to the objective(s) of the restoration project.

Abundance

Birds are often easily detectable because of their conspicuousness and their habit of singing. However, simple bird lists with counts of individuals by species are not always the most useful metric when monitoring bird response to restoration. Conversely, estimates of absolute abundance (i.e., the number of individuals of a given species per unit area), which account for imperfect detection, are not always a panacea. Highlighted below are several possible abundance metrics that need to be considered when measuring bird response to restoration efforts. Note these are generally arranged from the least expensive, least intensive, and easiest metrics to implement with low rigor to the more expensive and extensive metrics resulting in higher rigor (Elphick, 1996, Table 1; Wiens et al., 2008, Figure 8).

Birding checklists The advent of eBird,³ part of the growing field of human computation, is a web-based program that allows birders to enter their field observations into a web-platform database, and renders checklist-based observations more widely useful to enhance understanding of bird biology and conservation (Wood et al., 2011). The eBird database focus of analyses typically addresses bird distribution and abundance across broad spatial scales. Frequent, repeated site visits by birdwatchers, spread across seasons, provides

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² This list is a compilation of a wide variety of priority bird conservation lists found in State Wildlife Actions Plans, Joint Venture plans, Landscape Conservation Cooperative documents, U.S. Fish and Wildlife Service Birds of Conservation Concern lists, the Audubon Watch List, the North American Wetlands Conservation Act, the International Union for Conservation of Nature, true Northern Gulf of Mexico pelagic birds, Gulf of Mexico endemic species, and the ten most oiled bird species recovered during the Deepwater Horizon Oil spill.

³ eBird information: http://ebird.org/content/ebird.

basic species inventory data and a general sense of relative abundance. Diversity metrics corrected for effort indicate that eBird data can substitute for standardized surveys at the projectlevel (Callaghan and Gawlik, 2015).

Practitioners wishing to use eBirders to help assess the outcome of their restoration project(s) must consider a few critical issues relating to spatial scale and inference. First, the balance between data quality and quantity is critical for using citizen science data such as eBird (Sullivan et al., 2009), so surveyors must adhere to the broad guidelines set forth on the eBird website. Second, inferences can only be drawn for the site within areas of high frequency sampling, which eliminates bias based on a non-random selection of sampling locations (Dickinson et al., 2010). Third, based on the scant research to date, only projects using diversity as a metric can safely rely on eBird data. Projects where abundance is the metric of inference ought to follow more standardized survey methodologies conducted by trained individuals until eBird data can be rigorously evaluated with regards to using this as a measure of restoration success.

Indices of abundance Indices, or relative abundance measures, have long been used in avian ecology, management, and conservation (Verner, 1985; Gregory et al., 2004; Johnson, 2008; Stephens et al., 2015). For this discussion, an index is defined to be a variable that correlates strongly with abundance or density of a species in an area (Caughley, 1977; Bart and Earnst, 2002; Bart et al., 2004; Johnson, 2008). Indices potentially suffer from bias due to imperfect detection in the field and novel quantitative methods have recently been developed to reduce this bias. Johnson (2008) argues that while these advances are valuable to understanding the detection process, “their practical application may well be limited, likely to intensive studies focusing on a small number of species.” If the objective is to increase population size by a given number or proportional amount, monitoring with repeated measurements of a population index over a number of years would adequately address an objective focused on increasing population. Frequently, estimating a population index is much less resource-intensive than estimating population size, and a obtaining a reliable index value is often preferred over poorly estimating size (Gregory et al., 2004). Ultimately, abundance indices may be suitable to address most ecological or conservation objectives requiring population estimation (Caughley, 1977; Verner, 1985; Gregory et al., 2004; Johnson, 2008).

Occupancy Oftentimes focusing on individual-level metrics can be costly, yet species-level metrics may often be more appropriate or feasible (e.g., MacKenzie and Nichols, 2005; Williams et al., 2002; Pacifici et al., 2012). For example, instead of spending the time and money necessary to estimate absolute abundance (i.e., the number of individuals per unit area; see Bart [2005]) of a species, one might choose to determine the presence or absence of a species at locations within the same area (Rhodes et al., 2006). This approach is generally referred to as species occurrence or occupancy (MacKenzie and Nichols, 2004; MacKenzie, 2012). In more technical terms, occupancy is defined as “the probability that a randomly selected site or sampling unit in an area of interest is occupied by a species” (i.e., the site contains at least one individual of the species) (MacKenzie et al., 2006). Estimating occupancy rates requires an estimate of detection probability, so efforts focused on this metric will require more effort to estimate than an index of abundance. However, traditional methods of distance sampling (Buckland and Anderson, 2004; Buckland and Rexstad, 2015) can be easily modified, thus reducing effort required, to allow for occupancy estimation (Shriver and Greenberg, 2012). Perhaps the simplest adaptation of traditional methods involves dividing a 20 minute fixed radius point count (recommended by Neckles et al. [2002] for monitoring marsh birds in New England marshes) into four separate 5 minute time intervals with all detected bird instances separated into discrete time blocks (Farnsworth et al., 2002; Rosenstock et al., 2002; Alldredge et al., 2007a,b). These data allow the application of occupancy modeling techniques that would not only estimate occupancy and abundance, but would simultaneously estimate detection probabilities (MacKenzie et al., 2006; Shriver and Greenberg, 2012).

Absolute abundance Recent and continuing criticisms of indices revolve around the issue of differential and/or imperfect detection of individuals (e.g., Thompson, 2002); reflecting only a proportion of individuals counted from a population (Johnson, 2008). Unfortunately, this proportion may not always be constant—birds farther from an observer are less likely to be detected or this proportion may vary across different habitats as a function of time and as a result of a differing habitat structure—yet many alternative approaches to address these issues have been developed. For example, distance sampling addresses the obvious differences in

detectability the farther a bird is away from an observer (Buckland et al., 2001). Additional methods involving mark-recapture, multiple-observer methods, and time-of-detection techniques, among others, have been developed to address this issue (see Gregory et al., 2004 and Johnson, 2008 for technique-specific citations).

Community Composition

Community composition is an umbrella category for many specific community metrics. The premise behind this approach is that avian community diversity is a function of habitat diversity (Rafe et al., 1985). It is typical for created habitats to initially lack important microhabitats and microhabitat features required by some bird species or groups, resulting in less diverse bird assemblages. Melvin and Webb (1998) found species richness and diversity to be greater in natural salt marshes; yet created salt marshes did provide bird habitat, though not necessarily for the same species assemblage as natural marshes. For restoration objectives with a focus on community-based outcomes, a few primary metrics are repeatedly noted in the literature, including relative abundance by species (e.g., Lewis and Casagrande, 1997; Grippo et al., 2007), relative abundance by bird groups (e.g., Melvin and Webb, 1998), abundance/density (e.g., Weller, 1995; Shriver and Greenberg, 2012), species richness (e.g., Weller, 1995; Melvin and Webb, 1998, Grippo et al., 2007; Shriver and Greenberg, 2012), species diversity (e.g., Melvin and Webb, 1998; Shriver and Greenberg, 2012), species evenness (e.g., Shriver and Greenberg, 2012), species similarity (e.g., Shriver and Greenberg, 2012), foraging guilds (e.g., Weller, 1995; Lewis and Casagrande, 1997, Shriver and Greenberg, 2012), and occurrence of obligate species (i.e., those restricted to an area for part of their life cycle) (e.g., Lewis and Casagrande, 1997).

Like all project evaluation criteria, community-focused metrics require some consideration before selection and use for evaluating restoration outcome. For particular habitats, including tidal marsh, the direct relationships between marsh bird communities and site characteristics are generally poorly understood (Brawley, 1995). Thus, working with an ornithologist with specific habitat and avian community experience in the system of interest will provide considerable insight in project design and monitoring approaches. The development of robust target habitat-specific avian community criteria is problematic for some habitats, including salt marshes. Community profiles can be heavily biased by selection of control site(s). To address this potential bias, standard profiles of avian salt marsh communities have been suggested, and by logical extension, would be good practice to develop for any habitat across the Gulf of Mexico by sampling large number of marshes or “pristine” locations of appropriate habitat across the region (Lewis and Casagrande, 1997).

In addition, Lewis and Casagrande (1997) also recommend further development of this method using relative foraging guild and species abundance data from habitats within biogeographic regions, in their case southern New England. Until these robust regional community profiles are developed, care should be taken that the chosen control site(s) represent a habitat that can be reasonably replicated by restoration. Despite these concerns, a community profile approach is appropriate for evaluating restoration of avian communities, because it captures a wide range of ecological functions quickly and inexpensively. Sole reliance on the presence of obligate species can be problematic, as well as conservative, for evaluating restoration success. Their presence can be highly variable in situations where a given species, such as secretive marshes birds, is difficult to detect (Conway, 2009, 2011; Johnson et al., 2009). The addition of facultative species for evaluation has been suggested given that many, particularly in wetlands, incur direct benefits from restoration (Lewis and Casagrande, 1997).

Birds are directly linked to ecosystem condition, such that changes in avian community composition needs to reflect changes in the ecological integrity of a site, and thus biological integrity indices ought to be a robust monitoring tool (e.g., Bryce et al., 2002; DeLuca et al., 2004; Shriver and Greenberg, 2012). Measurable avian restoration metrics (e.g., occupancy, abundance by species) can easily be integrated into a community-level integrity index to monitor tidal marsh condition (Deluca et al., 2004). This condition index approach, or similar ones that consider bird community assemblage, may allow more robust techniques to evaluate differences between tidal marsh restoration sites pre- and post-restoration or between reference and restored sites. A secondary benefit to using indices of marsh bird community integrity scores is that when combined with identification of land-use threshold(s), these indices are easy to interpret and may help communicate complex ecological data to natural resource managers and conservation planners (DeLuca et al., 2004; Frederick et al., 2009). Furthermore, given this

approach has been used with tidal marshes, there is no reason to believe similar approaches could not be applied to avian assemblages across submerged aquatic vegetation, oyster, and/or sand beach habitats.

Reproductive Rates

Few avian restoration monitoring efforts include demographic parameters as outcome metrics, likely because associated demographic data can be difficult to collect and more expensive relative to other avian monitoring metrics (Larison et al. 2001; Smallwood, 2001; Williams et al., 2002; Fletcher et al., 2003, 2006; Thomson et al., 2008; Lorenz et al., 2009). However, recent emphasis on the critical nature of these data for evaluating and understanding response to restoration efforts has been noted. Bjorndal et al. (2011) suggest that one of seven research plan elements be to “integrate demography with abundance trends for multiple life stages and determine environmental effects on those parameters.” They stress that both demographic and abundance data are essential to determine causes of population declines. Shriver and Greenberg (2012) support this strong statement, noting that few demographic studies of tidal marsh bird populations, regardless of a focus on restoration, have been published while even fewer have focused on different marsh management approaches. Estimates of occupancy or abundance by themselves are not adequate to determine population persistence, a critical parameter for tidal marsh endemic populations (Shriver and Greenberg, 2012).

The collection of parameters such as fledgling success, fecundity, and age-specific survival could provide a valuable opportunity to assess the effects of restoration efforts on target taxa through development of complete life cycle models. Through this individual-based model structure, such as presented by Mattson and Cooper (2007), restoration practitioners and research ecologists alike will be able to predict effects of future environmental changes, such as sea level rise, on coastal restoration project outcomes (Woodrey et al., 2012). Projects that incorporate sampling pre- and post-restoration at multiple sites across a region will certainly provide the most informative results, but may likely be the most expensive and logistically challenging. It will depend on decision-makers and what management question these monitoring efforts aim to inform (see chapter 3 for a detailed discussion).

See Table II.4 for monitoring metrics that are generally accepted to measure (“standardized”) for construction and performance monitoring of bird-related restoration activities, and monitoring metrics that are suggested by the committee to assess bird restoration efforts at a program, region, or Gulf-wide scale (note that example metrics to support monitoring for adaptive management are not included because of their inherent project/program-specificity).

Monitoring Planning Considerations

Gregory et al. (2004) and Lambert et al. (2009) provide excellent guidance for how to plan a bird monitoring effort, in which key elements include the following: (1) determine monitoring objective(s), (2) determine need for absolute density metric or an index, (3) determine monitoring project boundaries, (4) determine if objectives require conducting a census or collecting a sample, (5) determine a monitoring objective-appropriate sampling strategy, (6) determine the size and shape of the sampling unit(s) in which surveys will take place, (7) determine the field method(s) to be used to adequately assess progress toward monitoring objective(s), and (8) consider and address issues of accuracy, precision, statistical power, and bias (see Chapter 3) when designing a survey. Carefully thinking through each of these steps will maximize the likelihood of achieving progress toward objectives, resulting in scientifically sound data for evaluating efficacy of restoration projects using avian monitoring metrics.

Appropriate Baseline (Pre-Construction) Data

Many restoration practitioners and natural resource managers associate the collection of baseline data within the project area as a means of measuring changes that are expected to occur to the parameters of interest (e.g., Reiger et al. 2014), possibly including birds, once a project has been implemented. However, best practice for a robust sampling scheme is to ensure that baseline data are also collected for multiple control/reference site(s) as well. In addition, because bird population numbers can vary due to their high mobility, it is important to have multiple years of baseline data against which to measure changes in abundance associated with a restoration project.

Appropriate Control/Reference Sites

Selection of appropriate reference sites needs to include considerations of similarity in size, landscape context (i.e., habitat diversity, habitat fragmentation, and distance from human development), and vegetation community assemblages to restoration target(s), as well as locations with bird data available for calculating target metrics (Block et al., 2001; Morrison, 2009; Hutto et al., 2014). Restoration practitioners ought to choose multiple appropriate control and/or reference sites to reflect the desired endpoint(s) of a restoration project. In addition, appropriate control/reference site(s) need to be carefully selected to reflect the desired outcome of the restoration efforts. The avian monitoring survey design conducted at project site(s) should be repeated at the appropriate control/reference site(s) for the duration of the restoration project (Lewis and Casagrande, 1997). Finally, the ability to draw sound conclusions from a restoration projects is enhanced where multiple control sites are incorporated into the study design (Underwood, 1991).

Develop a Rigorous and Robust Sampling Design

Perhaps the most over-arching best practice for avian monitoring efforts is scientific rigor. It is one of three fundamental objectives identified and defined by GoMAMN as “Objective 2.0: Maximize Rigor of Monitoring Projects (increase emphasis on scientific rigor [study designs, sampling frameworks, power analysis, etc. – see Chapter 3] underpinning monitoring projects in the Gulf of Mexico).” The group further developed a set of performance metrics, or evaluation criteria, to determine required standards to reflect the rigor of a project design. In order to be considered a rigorous effort, a project’s monitoring plan needs to include (as discussed in Chapter 3) the following considerations:

• Clearly state project objectives and/or hypotheses – emphasis placed on a monitoring design that clearly states objectives and/or hypotheses along with supporting details;
• Maximize the monitoring design to achieve project objectives – emphasis placed on a survey⁴ and sampling design that is clearly appropriate to measure progress toward achieving objectives;
• Maximize appropriateness of target taxa – emphasis placed on a survey that uses the most appropriate target species for evaluating objectives and hypotheses;
• Maximize appropriateness of response variable(s) – emphasis placed on a survey that uses the most appropriate and best suited response variable(s) to address project objectives/hypotheses;
• Maximize appropriateness of the statistical analysis – emphasis placed on a survey that uses the most appropriate and best suited statistical technique(s) to address project objectives/hypotheses;
• Conduct an appropriate formal power analysis – emphasis placed on a survey that explicitly states a desired effect sizes for meaningful ecological and management decisions;
• Develop and articulate a data management plan – emphasis placed on a survey that includes a data management plan that addresses data acquisition, development, storage, and transfer;
• Maximize the inference based on survey design – emphasis placed on a survey that provides broadly applicable data with a broad range of inference;
• Maximize the budget – emphasis placed on a survey that has an appropriate, reasonable, and efficient budget to address objectives/hypotheses;
• Maximize the timeline for the project – emphasis placed on a survey that has an appropriate and reasonable timeline to address objectives/hypotheses; and
• Maximize the likelihood of meeting project objectives– emphasis placed on a survey in which aspects of the project are clearly aligned and the best way to achieve objectives/hypotheses (Wilson, 2015).

A focus on these criteria in the design of an avian monitoring effort will ensure both the scientific validity of the data collected, which is necessary to confidently support monitoring conclusions (see Table 3.1), as well contribute to progress towards meeting the project objectives.

Consider Cost Constraints

Cost constraints increase with the level of detail and specificity (e.g., going from simple

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⁴ Here the term survey includes a number of different survey techniques, methodologies, and metrics.

checklists reflecting relative abundance to estimating multiple demographic parameters) determined to be appropriate for a given bird monitoring program. However, a few relatively novel technologies exist that could help reduce monitoring costs associated with a particular project. For example, autonomous recording units are a cost-effective class of tools for monitoring singing or calling birds (where individuals are rarely observed or hard to access on a regular basis (Acevedo and Villanueva-Rivera, 2006; Alquezar and Machado, 2015). Using autonomous recording units saves personnel costs by requiring fewer field technicians, but does involve human labor in data review, identification, and quality control. Regardless, these units could be particularly useful for monitoring bird species that vocalize frequently or for species such as breeding marsh birds, especially if occupancy is a monitoring metric of interest.

Recent technological advances with wildlife cameras have led to an explosion in the use of camera trapping to survey and monitor wildlife in recent years (O’Connell et al., 2011; Burton et al., 2015). The use of trail cameras likely reduces costs for field technicians to conduct repeated field surveys, both in terms of time and logistics. However, the use of this technology does not preclude consideration of several lessons learned regarding quantitative assessment of animal populations. For instance, issues such as imperfect detection, effective sampling area, occupancy modeling assumptions, and multi-species inference are topics that need to be addressed in a monitoring context (Burton et al., 2015). For example, in the Big Bend Region of Florida, Frederick et al. (2015) used trail cameras to estimate usage of restored oyster reefs by aquatic birds. They report cameras were not always reliable and detection of target wading birds was likely biased low due to dark conditions or occlusion by precipitation or fog. Ultimately, the cameras collected useable bird data, although many individuals could not be identified to species level (Frederick et al., 2015).

Other Technology Advances

Many new technologies are being developed to track wide-ranging organisms. For example, a very promising bird-tracking technology is the nano-tag (e.g., Taylor et al., 2011), where digitally encoded micro-transmitters are attached to individual organisms to track their movements. The main advantage of these micro-transmitters over conventional radio-transmitters is their smaller size and significantly longer battery life. In addition, these tags can be read by receiver towers that constantly and passively monitor the surrounding area (up to 25 km away) for nano-tag signals. The potential for understanding species movements across and around the Gulf cannot be overstated. The broad-scale implementation of this technology across the Gulf region would provide invaluable information regarding long distance movements as well as local-scale movements and site fidelity to restoration projects. Further, the development of a Gulf-wide network of automated radio telemetry towers, similar to the Motus Wildlife Tracking System would facilitate monitoring from the project-scale to the Gulf-wide scale. When collaborative efforts, such as marking birds with nano-tag transmitters, are combined with a region-wide network of automated towers, this type of array could track birds across the widespread, diverse habitats of the Gulf of Mexico over thousands of kilometers.⁵

The utility of standard sampling frameworks and protocols cannot be overstated. In the case of secretive marsh birds in the northeastern United States, regional monitoring efforts exist that would not have been possible without an underlying unified sampling framework (Wiest et al., 2016). In this case, partners throughout the region were willing to collect different portions of these data because sampling locations had been selected using a rigorously designed sampling framework (Johnson et al., 2009). This approach allows local and regional information needs to be addressed simultaneously. Most importantly for restoration practitioners, sampling effort (i.e., number of points observed) can be increased to make inferences at the restoration project scale while preserving the statistical design elements for inference at broader spatial scales. Further, this framework allows integration of restoration efforts into adaptive management schemes that address questions regarding the effectiveness of management activities for these species (Conroy et al., 2010). See Box II.2 for a case study of successful regional bird monitoring.

Program-Level Monitoring and Assessment

Given the potential amount of tidal marsh restoration and geographic extent of emergent coastal wetland habitat across the Gulf of Mexico, the formation of a regional emergent marsh restoration team that includes restoration

___________________

⁵ See http://motus.org.

practitioners, monitoring experts, and wetland avian ecologists would be an extremely useful step in addressing protocols and standard procedures for the benefit for multiple projects and regional integration of their data (see Box II.2). Integrated and inter-disciplinary teams are especially valuable for their role in giving serious thought to the development of a regional marsh monitoring effort, including secretive marsh birds, that allows evaluation of restoration outcomes across the region, along with an understanding of the changes in marsh bird populations due to these directed efforts.

Building on the multitude of restoration efforts currently underway and proposed for the future across the Gulf of Mexico is critical to measuring avian response to Gulf NRDA, RESTORE, and NFWF activities (see Chapter 2). In order to generate robust and scientifically defensible measures of responses to restoration activities, taxa-specific regional sampling frameworks need to be designed and implemented. In addition, every attempt ought to be made to develop and implement standardized field sampling protocols that allow data compilation across various spatial scales. Although daunting to consider, this has been accomplished for secretive marsh birds in the Northeastern United States (Wiest et al., 2016; See Box II.2).

Also, restoration projects have focused much recent attention on restoring marsh birds, and one major development is the testing, vetting, and agreement on a standardized monitoring protocol – the Standardized North American Marsh Bird Monitoring Protocol (Conway, 2011). The luxury of having a standard procedure for counting marsh birds allows for the quick and rigorous implementation of monitoring efforts across multiple scales. This methodology provides very explicit instructions for counting marsh birds to address a variety of fundamental objectives including (1) documenting presence or distribution of marsh birds; (2) comparing densities among management units, wetlands, or regions; (3) estimating population trends; (4) evaluating the effects of management efforts on marsh bird species; and (5) documenting habitat associations or wetland conditions that drive marsh bird abundance or occupancy. Regardless of the bird taxa of interest, these five objectives should likely serve as the basis for any bird-focused restoration project across the Gulf.

Several integration performance metrics highlight the need for standardization of mapping and survey data collection, as well as maximizing the accessibility, utility, and standardization of monitoring data. To address such concerns and support an integrated, explicit monitoring effort throughout the Gulf, avian ecologists, conservation biologists, and natural resource managers, among others, are working together as part of GoMAMN. Practitioners are using a structured decision making framework (Gregory and Keeney, 2002) to develop a systematic, rigorous, and comprehensive avian monitoring

network (Wilson, 2015). The initial efforts of this group are focused on developing monitoring objectives to maximize the relevancy of bird monitoring data and ensure that projects are addressing current data needs irrespective of geographic scale.

Table II.4 Metrics Considered Good Practice to Monitor Bird Restoration Activities for Construction, Performance Toward Project Objectives, and Program-Level or Large-Scale Assessments of (A) Beach and Dune, (B) Emergent Marsh, and (C) Open Water Bird Restoration

SOURCES: Johnson et al., 2009; Conway, 2011; Wilson, 2015; Wuuest et al., 2016

NOTE: Since bird restoration efforts are usually more guild-specific than objectives-specific, symbols indicate metrics that are generally appropriate to sample for each guild for different purposes of monitoring.

^a Bird guilds include land birds (LB), marsh birds (MB), pelagic seabirds (PS), raptors (RA), sea birds (SE), shore birds (SB), wading birds (WB), and waterfowl (WF).

^b Seasons are defined as breeding (b; June-August), migration (m; March-May and September-November), and winter (w; December-February). Seasons are included for faunal monitoring attributes because birds show species-specific seasonal habitat use patterns along the Gulf of Mexico.

^c Small and discrete vs. large and extensive.

^d Based on Coastal Change Analysis Program habitat descriptions.

^e Natural or restored site.

^f Defined here as flood, wild fire, prescribed fire, hurricane, tornado, straight-line winds or other major disturbance that occurred in the “target area.”

^g Defined here as prescribed fire, drawdown, flooding, disking, mowing, grazing, herbicide application, beach renourishment, marsh restoration, fisheries management, freshwater management, barrier island creation/restoration, vegetation planting.

REFERENCES

evaluate the success of ecological restoration on wildlife. Restoration Ecology 9(3):293–303.

Bryce, S.A., M.R. Hughes, and P.R. Kaufman. 2002. Development of a bird integrity index using bird assemblages as indicators of riparian condition. Environmental Management 30:294-310.

Buckland, S.T., D.R. Anderson, K.P. Burnham, J.L. Lakke, and D.L. Borchers. 2001. Introduction to Distance Sampling: Estimating Abundance of Biological Populations. New York: Oxford University Press.

Buckland, S.T., and D.R. Anderson. 2004. Introduction to advanced distance sampling. In Advanced Distance Sampling. S.T. Buckland, D.R. Anderson, K.P. Burnham, J.L. Laake, D.L. Borchers, and L. Thomas, eds. Oxford, UK: Oxford University Press.

Buckland, S.T., E.A. Rexstad, T.A. Marques, and C.S. Oedekoven. 2015. Design-based estimation of animal density and abundance. Pp. 105-126 in Distance Sampling: Methods and Applications. Heidelberg: Springer International Publishing.

Burton, A.C., E. Neilson, D. Moreira, A. Ladle, R. Steenweg, J.T. Fisher, E. Bayne, and S. Boutin. 2015. Wildlife camera trapping: A review and recommendations for linking surveys to ecological processes. Journal of Applied Ecology 52:675-685.

Callaghan, C.T., and D.E. Gawlik. 2015. Efficacy of eBird data as an aid in conservation planning and monitoring. Journal of Field Ornithology 86:298-304.

Caughley, G. 1977. Analysis of Vertebrate Populations. London: John Wiley and Sons.

Conroy, M.J., R.J. Cooper, S.A. Rush, K.W. Stodola, B.L. Nuse, and M.S. Woodrey. 2010. Effective use of data from marshbird monitoring programs for conservation decision-making. Waterbirds 33:397-404.

Conway, C.J. 2011. Standardized North American marsh bird monitoring protocol. Waterbirds 34:319-346.

DeLuca, W.V., C.E. Studds, R.S. King, and P.P. Marra. 2008. Coastal urbanization and the integrity of estuarine bird communities: Threshold responses and the importance of scale. Biological Conservation 141:2669-2678.

DeLuca, W.V., C.E. Studds, L.L. Rockwood, and P.P. Marra. 2004. Influence of land use on the integrity of marsh bird communities of the Chesapeake Bay, USA. Wetlands 24:837-847.

Dickinson, J.L., B. Zuckerberg, and D.N. Bonter. 2010. Citizen Science as an ecological research tool: Challenges and benefits. Annual Review of Ecology, Evolution, and Systematics 41:149-172.

DWH NRDA Trustees (Deepwater Horizon Natural Resource Damage Assessment Trustees). 2016. Deepwater Horizon oil spill: Final Programmatic Damage Assessment and Restoration Plan and Final Programmatic Environmental Impact Statement. http://www.gulfspillrestoration.noaa.gov/restoration-planning/gulf-plan.

Elphick, C.S. 1996. Experimental approaches to shorebird habitat management. International Wader Studies 9:20-28.

Farnsworth, G.L., K.H. Pollock, J.D. Nichols, T.R. Simons, J.E. Hines, and J.R. Sauer. 2002. A removal model for estimating detection probabilities from point-count surveys. Auk 119(2):414-425.

Fletcher, R.J., Jr., and R.R. Koford. 2003. Changes in breeding bird populations with habitat restoration in northern Iowa. The American Midland Naturalist 150(1):83-94.

Fletcher, R.J., Jr., R.R. Koford, and D.A. Seaman. 2006. Critical demographic parameters for declining songbirds breeding in restored grasslands. Journal of Wildlife Management 70(1):145-157.

Frederick, P., D.E. Gawlik, J.C. Ogden, M.I. Cook, and M. Lusk. 2009. The White Ibis and Wood Stork as indicators for restoration of the Everglades ecosystem. Ecological Indicators 9:S83-S95.

Frederick, P.C., and J.C. Ogden. 2003. Monitoring wetland ecosystems using avian populations: Seventy years of surveys in the Everglades. Pp. 321-350 in Monitoring Ecosystems: Interdisciplinary Approaches for Evaluating Ecoregional Initiatives. D. Bush and J. Trexler, eds. Washington, DC: Island Press.

Frederick, P.C., and M.G. Spalding. 1994. Factors affecting reproductive success of wading birds (Ciconiiformes) in the Everglades ecosystem. Pp. 659-691 in Everglades: The Ecosystem and Its Restoration. S. Davis and J. C. Ogden, eds. Delray Beach, FL: St. Lucie Press.

Frederick, P., B. Pine, J. Seavey, and L. Sturmer. Restoring Resilient Oyster Reefs in Florida’s Big Bend. Final Report to the Nature Conservancy and the National Oceanic and Atmospheric Administration. http://shellfish.ifas.ufl.edu/wp-content/uploads/Final-report-Lone-Cabbage-2015-final-version2.pdf.

Gardali, T., A.L. Holmes, S. L. Small, N. Nur, G.R. Geupel, and G.H. Golet. 2006. Abundance patterns of landbirds in restored and remnant riparian forests on the

Sacramento River, California, U.S.A. Restoration Ecology 14:391-403

Gawlik DE. 2002. The effects of prey availability on the numerical response of wading birds. Ecological Monographs 72:329-346.

Gawlik, D.E. 2006. The role of wildlife science in wetland ecosystem restoration: Lessons from the Everglades. Ecological Engineering 26:70-83.

Gentile, J.H., M.A. Harwell, W. Cropper, Jr., C.C. Harwell, D. DeAngelis, S. Davis, J.C. Ogden, and D. Lirman. 2001. Ecological conceptual models: A framework and case study on ecosystem management for South Florida sustainability. The Science of the Total Environment 274:231-253.

George, T.L., and S. Zack. 2001. Spatial and temporal considerations in restoring habitat for wildlife. Restoration Ecology 9:272–279.

Golder, W., D. Allen, S. Cameron, and T. Wilder. 2008. Dredged material as a tool for management of tern and skimmer nesting habitats. DOER Technical Notes Collection (ERDC TN-DOER-E24), Vicksburg, MS: USACE Engineer Research and Development Center. http//el.erdc.usace.army.mil/dots/doer/doer.html.

Green, A. J. and J. Elmberg. 2014. Ecosystem services provided by waterbirds. Biological Reviews 89(1):105-122.

Greenberg, R., and J.E. Maldonado. 2006. Diversity and endemism in tidal-marsh vertebrates. Studies in Avian Biology 32:32–53.

Gregory, R.D., D.W. Gibbons, and P.F. Donald. 2004. Bird census and survey techniques. Pp. 17-55 in Bird Ecology and Conservation: A Handbook of Techniques. W.J. Sutherland, I. Newton, and R.E. Green, eds. New York: Oxford University Press.

Gregory, R.S., and R.L Keeney. 2002. Making smarter environmental management decisions. Journal of the American Water Resources Association 38:1601-1612.

Grippo, M.A., S. Cooper, and A.G. Massey. 2007. Effect of beach replenishment projects on waterbird and shorebird communities. Journal of Coastal Research. 23:1088-1096.

Guilfoyle, M.P., R.A. Fischer, D.N. Pashley, and C.A. Lott, eds. 2006. Summary of first regional workshop on dredging, beach nourishment, and birds on the south Atlantic coast. ERDC/EL TR-06-10, Vicksburg, MS: USACE Engineer Research and Development Center.

Haney, J.C., H.J. Geiger, and J.W. Short. 2014a. Bird Mortality from the Deepwater Horizon oil spill. I. Exposure probability in the offshore Gulf of Mexico. Marine Ecology Progress Series 513:225-237.

Haney, J.C., H.J. Geiger, and J.W. Short. 2014b. Bird Mortality from the Deepwater Horizon oil spill. II. Carcass sampling and exposure probability in the coastal Gulf of Mexico. Marine Ecology Progress Series 513:239-252.

Henkel, J.R., B.J. Sigel, and C.M. Taylor. 2014. Oiling rates and condition indices of shorebirds on the northern Gulf of Mexico following the Deepwater Horizon oil spill. Journal of Field Ornithology 85:408-420.

Holderby, Z., A. Hill, E. Palacios. M.C. Green, E. Amador, and C. de Dios. 2014. Comparisons of reddish egret (Egretta rufescens) diet during the breeding season across its geographic range. Waterbirds 37:136-143.

Hutto, R.L. and Belote, R.T. 2013. Distinguishing four types of monitoring based on the questions they address. Forest Ecology and Management 289:183-189.

Hutto, R.L., A.D. Flesch, and M.A. Fylling. 2014. A bird’s-eye view of forest restoration: Do changes reflect success? Forest Ecology and Management 327:1-9.

Johnson, D.H. 2008. In defense of indices: The case of bird surveys. Journal of Wildlife Management 72:857-868.

Johnson, D.H., J.P. Gibbs, M. Herzog, S. Lor, N.D. Niemuth, C.A. Ribic, M. Seamans, T.L. Shaffer, W.G. Shriver, S.V. Stehman, and W.L. Thompson. 2009. A sampling design framework for monitoring secretive marshbirds. Waterbirds 32:203-215.

Konisky, R.A., D.M. Burdick, M. Dionne, and H.A. Neckles. 2006. A regional assessment of salt marsh restoration and monitoring in the Gulf of Maine. Restoration Ecology 14:516-525,

Lambert, J.D., T.P. Hodgman, E.J. Laurent, G.L. Brewer, M.J. Iliff, and R. Dettmers. 2009. The Northeast Bird Monitoring Handbook. The Plains, VA: American Bird Conservancy.

Larison, B., Laymon, S.A., Williams, P.L. and Smith, T.B., 2001. Avian responses to restoration: Nest-site selection and reproductive success in song sparrows. Auk 118(2):432-442.

Leighton, F.A. 1993. The toxicity of petroleum oils to birds. Environmental Review 1:92-103.

Lewis, C. and D.G. Casagrande. 1997. Using avian communities to evaluate salt marsh restoration. Pp. 204-236 in Restoration of an Urban Salt Marsh. E.G. Casagrande, ed. New Haven, CT: Yale School of Forestry and Environmental Studies.

Lincoln, F.C., S.R. Peterson, and J.L. Zimmerman. 1998. Migration of birds. Circular 16.

Washington, DC: U.S. Department of the Interior, U.S. Fish and Wildlife Service.

Lorenz, J.J., B. Langan-Mulrooney, P.E. Frezza, R.G. Harvey, and F.J. Mazzotti. 2009. Roseate spoonbill reproduction as an indicator for restoration of the Everglades and the Everglades estuaries. Ecological Indicators 9(6):S96-S107.

Lorenz, J.L. 2014. The relationship between water level, prey availability and reproductive success in roseate spoonbills foraging in aseasonally-flooded wetland while nesting in Florida Bay. Wetlands 34:201-211. doi: 10.1007/s13157-012-0364-y.

Lyons, J.E., M.C. Runge, H.P. Laskowski, and W.L. Kendall. 2008. Monitoring in the context of structured decision-making and adaptive management. Journal of Wildlife Management 72:1683-1692.

MacKenzie, D.I. 2012. Study design and analysis options for demographic and species occurrence dynamics. Pp. 397-425 in Design and Analysis of Long-term Ecological Monitoring Studies R.A. Gitzen, J.J. Millspaugh, A.B. Cooper, and D.S. Licht, eds. Cambridge, UK: Cambridge University Press.

MacKenzie, D.I., and J.D. Nichols. 2004. Occupancy as a surrogate for abundance estimation. Animal Biodiversity and Conservation 27:461-467.

MacKenzie, D.I., J.D. Nichols, J.A. Royle, K.H. Pollock, L.L. Bailey, and J.E. Hines. 2006. Occupancy estimation and modeling: Inferring patterns and dynamics of species occurrence. Amsterdam: Elsevier.

Majer, J.D. 2009. Animals in the restoration process: Progressing the trends. Restoration Ecology 17:315-319.

Marzluff, J.M., and K. Ewing, K. 2001. Restoration of fragmented landscapes for the conservation of birds: A general framework and specific recommendations for urbanizing landscapes. Restoration Ecology 9(3):280–292.

Mattsson, B. J., and R. J. Cooper. 2007. Which life history components determine breeding productivity for individual songbirds? A case study of the Louisiana Waterthrush (Seiurus motacilla). Auk 124(4):1186-1200.

Melvin, S.L., and J.W. Webb, Jr. 1998. Differences in the avian communities of natural and created Spartina alterniflora salt marshes. Wetlands 18:59-69.

Michot, T.C. 2000. Comparison of wintering Redhead populations in four Gulf of Mexico seagrass beds. Pp. 243–260 in Limnology and Aquatic Birds: Monitoring, Modelling, and Management F.A. Comin, J.A. Herrara, and J. Ramimirez, eds. Merida, Mexico: Universidad Autonoma de Yucatan.

Mitchell, L.R., S. Gabrey, P.P. Marra, and R.M. Erwin. 2006. Impacts of marsh management on coastal-marsh bird habitats. Studies in Avian Biology 32:155-175.

Moore, F.R., M.S. Woodrey, J.J. Buler, S. Woltmann, and T.R. Simons. 2005. Understanding the stopover ecology of migratory birds: A scale dependent approach. Pp. 684-689 in Bird Conservation Implementation and Integration in the Americas: Proceedings of the Third International Partners in Flight Conference, March 2002; Asilomar, California. Ralph, C.J. and T.D. Rich, eds. General Technical Report PSW-GTR-191. Albany, California: Pacific Southwest Research Station, Forest Service, U.S. Department of Agriculture.

Moore, J.L., and M.C. Runge. 2012. Combining structured decision making and value-of-information analyses to identify robust management strategies. Conservation Biology 26:810-820.

Morrison, M. 2002. Searcher bias and scavenging rates in bird–wind energy studies. NREL/SR-500-30876. Golden, CO: National Renewable Energy Laboratory.

Morrison, M. 2009. Restoring Wildlife: Ecological Concepts and Practical Applications., Washington, DC: Island Press.

NSRE (National Survey on Recreation and the Environment). 2010. America’s participation in outdoor recreation: Results from NSRE (with weighted data) (versions 1 to 13). Washington, DC: Forest Service, U.S. Department of Agriculture. www.srs.fs.usda.gov/trends/Nsre/nsre2.html.

Neckles, H.A., M. Dionne, D.M. Burdick, C.T. Roman, R. Buchsbaum, and E. Hutchins. 2002. A monitoring protocol to assess tidal restoration of salt marshes on local and regional scales. Restoration Ecology 10:556-563.

Nell, L.A., and P.C. Frederick 2015. Fallen nestlings and regurgitant as mechanisms of nutrient transfer from nesting wading birds to crocodilians. Wetlands 35:723-732.

Nell, L., P.C. Frederick, F.J. Mazotti, K.A. Vliet and L.A. Brandt 2016. Presence of breeding birds improves body condition for a crocodilian nest protector. PLoS One. doi: 10.1371/journal.pone.0149572.

Nichols, J.D., and B.K. Williams. 2006. Monitoring for conservation. Trends in Ecology and Evolution 21:668-673.

Novak, J.M., K.F. Gaines, J.C. Cumbee Jr, G.L. Mills, A. Rodriguez-Novarro, and C.S.

Romanek. 2006. The clapper rail as an indicator species of estuarine-marsh health. Studies in Avian Biology 32:270–281.

O’Connell, A.F., J.D. Nichols, and K.U. Karanth. 2011. Camera Traps in Animal Ecology: Methods and Analyses. New York: SpringerVerlag.

Ogden, J.C., S.M. Davis, K.J. Jacobs, T. Barnes, and H.E. Fling. 2005. The use of conceptual ecological models to guide ecosystem restoration in South Florida. Wetlands 25(4):795-809.

Ogden, J.C., J.D. Baldwin, O.L. Bass, J.A. Browder, M.I. Cook, P.C. Frederick, P.E. Freeza, R.A. Galvez, A.B. Hodgson, K.D. Meyer, L.D. Oberhofer, A.F. Paul, P.J. Fletcher, S.M. Davis, and J.L. Lorenz. 2014. Waterbirds as indicators of ecosystem health in the coastal marine habitats of Southern Florida: 2. Conceptual ecological models. Ecological Indicators 44:128-147. doi: 10.1016/j.ecolind.2014.03.008.

Ortega-Alvarez, R., and R. Lindig-Cisneros. 2012. Feathering the scene: The effects of ecological restoration on birds and the role birds play in evaluating restoration outcomes. Ecological Restoration 30:116-127.

Pacifici, K., R.M. Dorazio, and M.J. Conroy. 2012. A two‐phase sampling design for increasing detections of rare species in occupancy surveys. Methods in Ecology and Evolution 3(4):721-730.

Palmer, M.A., R.F. Ambrose, and N.L. Poff. 1997. Ecological theory and community restoration ecology. Restoration Ecology 5:291-300.

Plissner, J.H., and S.M Haig 2000. Status of a broadly distributed endangered species: Results and Implication of the second International Piping Plover census. Canadian Journal of Zoology 78:128-139.

Rafe, R.W., M.B. Usher, and R.G. Jefferson. 1985. Birds on reserves: The influences of area and habitat on species richness. Journal of Applied Ecology 22:327-335.

Rappole, J.H., and M.A. Ramos. 1994. Factors affecting migratory bird routes over the Gulf of Mexico. Bird Conservation International 4(2-3):251-262.

Rieger, J., J. Stanley, and R. Traynor. 2014. Project Planning and Management for Ecological Restoration. Washington, DC: Island Press.

Rhodes, J.R., A.J. Tyre, N. Jonzén, C.A. McAlpine, and H.P. Possingham. 2006. Optimizing presence-absence surveys for detecting population trends. Journal of Wildlife Management 70(1):8-18.

Rosenstock, S.S., D.R. Anderson, K.M. Giesen, T. Leukering, and M.F. Carter. 2002. Landbird counting techniques: Current practices and an alternative. Auk 119(1):46-53.

Runge, M.C., S.J. Converse, and J.E. Lyons. 2011. Which Uncertainty? Using expert elicitation and expected value of information to design an adaptive program. Biological Conservation 144:1214-1223.

Rush, S.A., E.C. Soehren, M.S. Woodrey, C. L. Graydon, and R.J. Cooper. 2009. Occupancy of select marsh birds within northern Gulf of Mexico tidal Marsh: Current estimates and projected change. Wetlands 29:798-808.

Sackmann, B.S., and D.S. Becker. 2015. Bird mortality due to the Deepwater Horizon oil spill: Comment on Haney et al. (2014a,b). Marine Ecology Progress Series 534:273-277.

Sanderlin, J.S., W.M. Block, and J.L. Ganey. 2014. Optimizing study design for multi-species avian monitoring programmes. Journal of Applied Ecology 51(4):860-870.

Sekercioglu, C.H. 2006. Increasing awareness of avian ecological function. Trends in Ecology and Evolution 21:464-471.

Sekercioglu, C.H., Daily, G.C. and Ehrlich, P.R. 2004. Ecosystem consequences of bird declines. Proceedings of the National Academy of Sciences 101(52):18042-18047.

Shriver, W.G., and R. Greenberg. 2012. Avian community responses to tidal restoration along the North Atlantic coast of North America. Pp. 119-143 in Tidal Marsh Restoration: A Synthesis of Science and Practice. C.T. Roman, and D.M. Burdick, eds. Washington, DC: Island Press.

Smallwood, K.S. 2001. Linking habitat restoration to meaningful units of animal demography. Restoration Ecology 9(3):253-261.

Smith, L.M., R.L. Pederson, and R.M. Kaminski. 1989. Habitat management for migrating and wintering waterfowl in North America. Lubbock: Texas Tech University Press.

Stephens P.A., N. Pettorelli, J. Barlow, M.J. Whittingham, and M.W. Cadotte. 2015. Management by proxy? The use of indices in applied ecology. Journal of Applied Ecology 52:1-6.

Sullivan, B.L., C.L. Wood, M.J. Iliff, R.E. Bonney, D. Fink, and S. Kelling. 2009. eBird: A citizen-based bird observation network in the biological sciences. Biological Conservation 142(10):2282-2292.

Sutherland, W.J., I. Newton, and R. Green. 2004. Bird ecology and conservation: A handbook of techniques. Oxford, UK: Oxford University Press.

Taylor, P.D., S.A. Mackenzie, B.G. Thurber, A.M. Calvert, A.M. Mills, L.P. McGuire, and C.G. Guglielmo. 2011. Landscape movements of migratory birds and bats reveal an expanded scale of stopover. PLoS ONE 6(11):e27054. doi: 10.1371/journal.pone.0027054.

Thompson, W.L. 2002. Towards reliable bird surveys: Accounting for individuals present but not detected. Auk 119(1):18-25.

Thomson, D.L., E.G. Cooch, and M.J Conroy. 2008. Modeling demographic processes in marked populations. New York: Springer.

Tsai, J.R., B.E. Reichert, P.C. Frederick and K.D. Meyer. 2016. Breeding site longevity and site characteristics have intrinsic value for predicting persistence of colonies of an endangered bird. Wetlands 36(4):639–647.

Turner, R.E., and B. Streever. 2002. Approaches to Coastal Wetland Restoration: Northern Gulf of Mexico. Hague, Netherlands: SPB Academic Publishing.

Underwood, A.J. 1991. Beyond BACI: The detection of environmental impacts on populations in the real, but variable world. Journal of Experimental Marine Biology and Ecology 161:145-178.

Verner, J. 1985. Assessment of counting techniques. Current Ornithology 2:247-302.

Weller, M.W. 1995. Use of two waterbird guilds as evaluation tools for the Kissimmee River restoration. Restoration Ecology 3:211-224.

Wiens, J.A., G.D. Hayward, R.S. Holthausen, and M.J. Wisdom. 2008. Using surrogate species and groups for conservation planning and management. Bioscience 58:241-252.

Wiest, W.A., M.D. Correll, B.J. Olsen, C.S. Elphick, T.P. Hodgman, D.R. Curson, and W.G. Shriver. 2016. Population estimates for tidal marsh birds of high conservation concern in the northeastern USA from a design-based survey. Condor 118(2):274-288. doi: 10.1650/CONDOR-15-30.1.

Williams, B.K., 1997. Approaches to the management of waterfowl under uncertainty. Wildlife Society Bulletin (1973-2006) 25(3): 714-720.

Williams, B.K., 2001. Uncertainty, learning, and the optimal management of wildlife. Environmental and Ecological Statistics 8(3): 269-288.

Williams, B.K., 2011. Adaptive management of natural resources framework and issues. Journal of Environmental Management 92(5):1346-1353.

Williams, B.K., J.D. Nichols, and M.J. Conroy. 2002. Analysis and Management of Animal Populations. San Diego, CA: Academic Press.

Wilson, R., ed. 2015. Integrated Gulf of Mexico Bird Monitoring Framework: Structured Decision Making Prototype Version 3.0. Draft Interim Technical Report – October 2015. Jackson, MI: U.S. Fish and Wildlife Service, Migratory Bird Program.

Wood, C., B. Sullivan, M. Iliff, D. Fink, and S. Kelling. 2011. eBird: Engaging birders in science and conservation. PLoS Biology 9:1-5.

Woodrey, M.S., S.A. Rush, J.A. Cherry, B.L. Nuse, R.J. Cooper, and A.J.J. Lehmicke. 2012. Understanding the potential impacts of global climate change on marsh birds in the Gulf of Mexico region. Wetlands 32:35-49.

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