Green herons (Butorides virescens) in an urbanized landscape: does recreational disturbance affect foraging behavior?
The human population continues to grow, with trends towards an increasingly urbanized population. The U.S. is expected to increase by 44% from the current 319 million (U.S. Census Bureau, 2010) to 461 million (44%) by 2050 (U.S. Census Bureau, 2008). As the trend towards urbanization continues, many natural areas near urban locations will likely become highly modified with a concomitant increase in recreational use of these natural areas. In 2013, for example, nearly 50 percent of all Americans over the age of five participated in outdoor recreational activities (The Outdoor Foundation, 2013, 2014). Providing humans with access and recreational opportunities in natural areas is a key element in educating and influencing the public to support conservation efforts in those areas. As the number of people participating in outdoor recreation rises every year, it becomes increasingly important to monitor the potential impacts human recreational activities can have on wildlife and to be able to quantify these impacts in relation to the benefits of recreation.
Human recreational activities can cause disturbances that directly or indirectly impact avian species that inhabit aquatic systems (hereafter 'waterbirds'). There are various forms of human recreation that can cause disturbances that could directly or indirectly alter an animal's behavior and/or distribution (Fox and Madsen, 1997), including swimming, boating (Batten, 1977), automobiles (Stolen, 2003), and ecotourism (Klein et al, 1995). Because waterbirds use areas many ecotourists and outdoor recreationists find attractive (e.g., shorelines, rivers, and lakes), they are often subject to elevated levels of human disturbance (Rodgers and Schwikert, 2003).
In many cases the impacts of human disturbances on waterbirds can be unclear and not easily observable because they might act through reduced access to feeding, nesting and breeding sites if birds are avoiding certain areas due to human presence (Gill et al, 1996). Conversely, there are also cases where there is a direct connection between human disturbance and negative impacts on waterbirds, including disruption during breeding season (Safina and Burger, 1983), increased energetic demands (Ydenberg and Dill, 1986; Burger, 1991), increased vulnerability to predators (Safina and Burger, 1983), as well as a reduction in foraging efficiency, foraging rates, and nesting success (Burger, 1994; Stolen, 2003). For instance the amount of time Piping Plovers (Charadrius melodus) devote to running and crouching increases whereas time devoted to feeding decreases as the number of people near foraging plovers increase, perhaps accounting for overall decreased reproductive success (Burger, 1991, 1994). Another study by Burger and Gochfield (1998) looked at the effects of ecotourism on five species of waterbirds in the Florida Everglades. For all species, lime devoted to feeding and number of strikes or pecks at prey decreased while people were present. In addition the percentage of time spent foraging and the number of strikes decreased as anthropogenic noise increased. In contrast some studies found that human disturbances had no effect on birds, or birds developed a tolerance or even became habituated to a source of disturbance (Weller, 1999; Nisbet, 2000; Lord et al., 2001; Webb and Blumstein, 2005).
Waterbirds benefit from maximizing their foraging rate for a variety of reasons. Foraging success affects critical life history stages as well as reproductive success of waterbirds (Frederick and Spalding, 1994; Powell, 1983). Energetic requirements greatly increase for adults during nesting due to physiological demands of clutch production and provisioning of nestlings (Frederick and Powell, 1994; Ashkenazi and Yom-Tov, 1996). Outside of the breeding season, foraging efforts of adults are focused on energy gain for migration and over-wintering. Human disturbance during these particularly stressful periods might decrease foraging efficiency and negatively affect demography.
The Green Heron (Butorides virescens) is a relatively small, stocky ardeid (241 g; 41-46 cm long) (Niethammer and Kaiser, 1983). Male and female adults are morphologically very similar, but females tend to be smaller than males and have duller and lighter plumage. Foraging habitat includes marshes, riparian zones, as well as ditches, canals, lakes, and ponds. Green Herons will feed at any time of the day or night and main prey items taken are fish and invertebrates; however, they will opportunistically take reptiles, amphibians and rodents (Davis and Kushlan, 1994). Green Herons forage along the banks or from emergent vegetation in rivers, lakes, and wetlands throughout much of North America. Despite its widespread distribution in the U.S., the North American Breeding Bird Survey (BBS) annual abundance indices for Green Herons in both Texas and the United States indicate the species is in decline. The BBS Green Heron abundance trends show a -1.16% per y (95% credible interval [-1.84, -0.48]) across all monitored BBS routes during 2002-2012 (Sauer et al, 2014).
As a fairly abundant species that might be in decline, the Green Heron is an ideal species for examining the potential effects of human recreational disturbance. Kaiser and Fritzell (1984) found on three of four rivers surveyed in southeastern Missouri, the number of Green Herons seen on the main river channel was negatively related to the number of recreationist groups. The study also found increased human activity resulted in decreased foraging effort and reduced length of foraging bouts.
San Marcos, Texas, is the fastest growing city in the U.S. (cities over 50,000 people; U.S. Census Bureau Report, 2014). Polak et al. (2008) looked at the influence of human recreational activities on waterbird abundance at Spring Lake and along the San Marcos River, Texas. The study found the highest abundance of waterbirds, including Green Herons, occurred in areas with the greatest amount of human disturbance. In the wake of increasing urbanization and recreational use in this freshwater aquatic system, it is important to examine critical factors for effective waterbird conservation in this disturbance-prone habitat.
The primary objective of this study was to examine the effects of human recreational disturbance on foraging Green Herons through the use of focal observations at sites of varying disturbance. We expected to find the level of human disturbance would alter the frequency of occurrence of specific foraging behaviors at each site. Increased disturbance could lead to altered behavior as attention may be directed away from foraging to the perceived threat from human recreation. This change in behavior may be expressed in variation of behavior used (i.e., change in frequency) and increased or decreased foraging efficiency.
The study was conducted at three sites in San Marcos, Texas: one on the San Marcos River and two at the impounded headsprings known as Spring Lake (Fig. 1). We selected these three sites a priori, based on variation in known levels of human recreational disturbance (i.e., high, medium, and low), which result from variations in level of public access.
Spring Lake is owned by Texas State University and types of recreational activities permitted there are greatly limited. Potential disturbance to birds on the lake comes from occasional use of a wetland boardwalk, periodic tours of glass bottom-boats and kayaks, and an occasional research team. The low disturbance site (hereafter Cove) is in the area of Spring Lake referred to as the "cove" that lies towards the south side of the lake. The cove is used by foraging birds and does not sustain any noticeable human disturbance, as boats and kayaks do not normally enter this area. Average depth in this area is 2.6 m and average current velocity is 0.03 m/s (Behen, 2013). The foraging area in the cove encompasses roughly 0.95 ha.
The medium-disturbance site (hereafter Boardwalk) lies about 0.20 km northeast of the cove in an area that contains a wetland boardwalk. The boardwalk runs 0.16 km along an area of the lake referred to as the "basin" and also reaches into an area referred to as the "slough." Average depth in these areas is 1.4 m and current velocity is 0.01 m/s (Behen, 2013). The foraging area around the boardwalk encompasses roughly 1.05 ha.
The high-disturbance site (hereafter River) is located in the upper reaches of the San Marcos River (below Spring Lake Dam), about 0.35 km from the cove where there are two bends in the river creating an "s" shape. Average depth in this area is 1.5 m and average current velocity is 0.16 m/s (Behen, 2013). The foraging area encompasses roughly 0.72 ha and contained areas of eddies and relatively still water due to vegetative structure and woody debris as well as the bends the river. The river was chosen as the high-disturbance site because it is a popular spot for recreational activities such as tubing, swimming, and paddling. Data from rentals of flotation devices ("tubes") on the river reveals approximately 500-600 tubes are rented daily on weekends during Green Heron nesting season, and recreationists frequently float by and pass within a few meters of waterbirds foraging in the river (Polak, 2008).
[FIGURE 1 OMITTED]
Water turbidity and temperature levels are similar across the three sites (Groeger et aL, 1997; Behen, 2013). Each site was also similar in that it included a foraging area consisting of a dense mat of floating and emergent vegetation, as well as stationary emergent objects such as logs. While average water depth and velocity varied across study sites, reported measurements include open water where Green Herons do not forage. Environmental variables (i.e., water depth and light intensity) have been found to affect foraging success to a greater degree than foraging behavior in other wading birds (Rodgers, 1983; Bates and Ballard, 2014). Differences in characteristics of prey type may have a stronger influence on foraging behavior than environmental variables (Bates and Ballard, 2014). The lake contains 17 species of fish while the upper reaches of the river contains 25 species of fish (Behen, 2013) and the river contains twice as many macroinvertebrates as the lake (Diaz and Alexander, 2010); however, macroinvertebrate studies do not differentiate between upper and lower reaches of the river. Strong et al. (1997) found two species of wading birds procured similar prey from different habitat types; therefore, it is possible Green Herons may select similar prey types at each study site, especially given their close proximity.
We assessed the effects of human recreational disturbance on Green Herons through the use of focal observations. Observations were made several times per wk from April 2013 to September 2013, and from April 2014 to August 2014 at each of the three study sites. These dates were chosen to coincide with the Green Heron breeding season in central Texas.
During 20 min observational periods, we collected behavioral data partitioned into nine discrete categories, we thought to be important, using feeding behavior terminology modified from Kushlan (2011): standing, stalking, walking quickly, walking slowly, peering, alert posture, flight, preening, and prey handling. We also added "stalking," which denotes locomotion of <1 step/s. Observation periods of 20 min were chosen to help mitigate observer fatigue, given all focal observations were made through binoculars. We also recorded the number of conspecifics present, weather data, foraging efficiency (number of captures/number of total strikes), average flight distance (flights during foraging bouts), and interactions with conspecifics, humans, and predators. We documented human activity by counting the number of people that passed the focal observation points and also noting the nature (e.g., swimmers, canoe/kayakers, etc.) of their activity. Observations were made at various times throughout the day (between dawn and dusk) to ascertain periodicity of human activity and account for circadian rhythms in Green Heron foraging activity. Observations were made only once on any individual bird at a given location and day, and from a starting distance of at least 20 m (some birds would walk/fly closer to, or further from the observation point during an observation). As many as three individual focal birds were randomly selected and observed in one day. Young of the year (identified by plumage) were excluded. Focal birds were not distinguishable from one another. Only actively foraging birds were studied, and birds perching far away from foraging areas or only preening were not selected for focal observations.
For each 20 min observational period, we determined the proportion of time the bird spent in each activity. We calculated foraging efficiency (total number of captures/total number of strikes) for each observation and then arcsine-transformed these data to normalize the distribution prior to analysis; for convenience, untransformed data are presented in the tables. To evaluate differences in foraging behavior between the three study sites, we used analysis of variance (ANOVA). When an ANOVA revealed a significant difference, we used planned comparisons to test for a linear change in treatment means. We established the cutoff for statistical significance ([alpha] = 0.022) using false discovery rate (FDR) to correct for multiple comparisons (Benjamini and Hochberg, 1995). To further assess if differences in behavior were a result of human disturbance or due to habitat differences between the three study sites, we used linear regression models. Fifteen models were built to assess the potential influence of study site variation as well as the influence of human disturbance on each of the response variables. Study site and disturbance were modeled as categorical variables, with study site predictors coded to reflected habitat, and disturbance predictors coded as one for disturbed or zero otherwise. For all analyses the response variables considered were: standing, stalking, walk slowly, walk quickly, and foraging efficiency. We also tested a null model that assumed no effect of disturbance or study site.
The behaviors peering and alert posture (both <5% of total observations) were omitted from further analysis, as these behaviors were found to be difficult to separate and seen as too subjective. The amount of time spent flying during foraging bouts was not examined as flight was often the result of territorial interactions, nor was the amount of time spent handling prey as it is a function of the type of prey caught. We used an information-theoretic approach based on Akaike Information Criterion (AIC) to select the best model for each foraging behavior (Akaike, 1973). We computed delta ([DELTA]), Akaike weights ([w.sub.i]), and adjusted [r.sup.2] values to determine the strength of evidence for each model. Algorithms for all statistical analyses were coded in R (R Version 2.15.1, www.r-project.org, accessed 9 Sept. 2012).
Foraging behavior was recorded for a total of 2695 min during 154 observations. An incident of potential human disturbance occurred during 89% of the observations at the River (n = 46), 50% of the observations at the Boardwalk (n = 60), and 0% at the Cove (n = 48); our a priori prediction of disturbance at these three sites was supported. During focal observations, the mean number ([+ or -]S.E.) and density ([+ or -]S.E.) of conspecifics observed were 0.64 (0.15) and 0.89 (0.21)/ha (River site), 4.6 (0.37) and 4.38 (0.35)/ha (Boardwalk site), and 0.91(0.12) and 0.96 (0.13)/ha (Cove site). An ANOVA and Tukey's HSD (honest significant difference) multiple comparison of means procedure revealed significant differences in the number of conspecifics between River/Boardwalk (high/medium disturbance) and Boardwalk/Cove (medium/low disturbance) sites but not between River/Cove (high/low disturbance) sites, ([F.sub.2,134] = 77.94, P < 0.001).
Green Herons spent the majority of each observation standing (78.4 [+ or -] 1.2%) whereas stalking, walking slowly, and walking quickly took up only ~0.5 to 9.0% of focal observations (Table 1). Mean foraging efficiency varied from 0.70 to 0.84 captures per strike. The ANOVAs comparing variation in foraging behaviors and foraging efficiency between the three sites only yielded significant variation among the activity categories of stalking ([F.sub.2,149] = 8.094, P < 0.001) and walking slowly ([F.sub.2,151] = 4.938, P < 0.01). The planned comparison analysis for stalking behavior revealed a linear increase from low (Cove) to high (River) disturbance sites ([F.sub.1,149] = 9.5, P < 0.01), whereas the planned comparison for walking slowly was not statistically significant ([F.sub.1,151] = 4.34, P = 0.04) using the FDR calculated alpha of 0.022. Linear regression analysis with AIC model selection showed that differences in habitat provided the best explanation for the observed variation in four of the five response variables (Table 2: stalking, walking slowly, walking quickly, and foraging efficiency). Standing behavior was best suited to the disturbance model according to its Akaike weight; however, we selected the null model as the best fit for standing given the coefficient of determination was so low ([r.sup.2] = 0.019). Model parameter estimates are shown in Table 3. ANOVAs comparing only low (Cove) and medium (Boardwalk) disturbed sites found significant differences in stalking ([F.sub.1,104] = 15.52, P < 0.001), walking slowly ([F.sub.1,106] = 9.16, P < 0.01), and efficiency ([F.sub.1,94] = 6.99, P < 0.01), where Green Herons stalked and walked slowly more often and had lower efficiency rates at the medium (Boardwalk) disturbance site.
Kaiser and Fritzell (1984) showed use of rivers by Green Herons was affected by recreational activities, which apparently caused a decline in use by the birds; however, they collected no data on the effects of human disturbance on foraging behavior. In our study the only significant difference or linear trend (reflecting disturbance effects) in Green Heron foraging behavior between the three study sites was for stalking behavior. The birds appeared to be somewhat less active at the low (Cove) disturbance site as compared to medium (Boardwalk) and high (River) disturbance sites; however, variation in behavior that was significant appears to be more likely due to differences in habitat than differences in disturbance levels. Additionally, density of Green Herons at high (River) and low (Cove) disturbance sites was almost identical indicating disturbances are not likely causing differences in relative abundance among study sites.
Our subsequent analyses of the medium (Boardwalk) and low (Cove) disturbance sites yielded somewhat different results. Because there were no disturbances at the Cove, this analysis can be viewed as a comparison of disturbed to undisturbed sites while controlling for habitat as both of these sites were within the lake and has similar environmental characteristics (Behen, 2013). Green Herons showed significantly higher efficiency rates at the undisturbed Cove site as compared to the medium disturbed Boardwalk site, whereas in the prior analysis that included all three sites, there was no significant difference in efficiency between the three sites. This comparison suggests the potential effects of disturbance on behavior and foraging efficiency at sites of similar habitat even if we did not observe disturbance effects across all three sites.
It is inherently difficult to study adverse effects of human disturbance on waterbirds because there are a variety of other factors that can influence their behavior. A possible explanation for the detected variation in Green Heron foraging behavior is that the birds responded to abiotic, temporal (Burger, 1991) and environmental variables not directly evaluated in this study (i.e., light intensity, water flow, prey type and density, time of day, vegetation, etc.). Green Herons might modify their foraging technique to maximize foraging efficiency to suit their location. Studies of heron foraging success indicate differences in vegetative density affect striking efficiency (Kent, 1987; Campos and Lekuona, 2001). Kent (1987) found a significant association between certain foraging behaviors and specific habitats in Snowy Egrets (Egretta thula) and Tricolored Herons (Egretta tricolor); for instance, the behavior "disturb-and-chase" was associated with open water habitat for both species. Maurer and Whitmore (1981) examined the influence of habitat structure on foraging behavior of five species of passerine birds. That study found each bird species modified its foraging behavior to accommodate differences in vegetative structure that altered the distribution of resources.
Other factors such as competitor or intraspecific interactions, or locations of territories or nest sites in relation to foraging areas might have also influenced our results. The foraging behavior of solitary White Ibises was found to differ from that of ibises foraging in large flocks; birds in flocks spent less time vigilant and more time foraging (Petit and Bildstein, 1987). In our study there were between one and nine conspecifics foraging with the studied bird during 78% of the observations recorded at the boardwalk site, 58% at the lake site observations, and 39% at the river site observations. Furthermore, there were 14 observed (plus additional incidental) instances of interactions with conspecifics that caused a Green Heron to flush from a foraging bout, whereas there were only 11 instances of human disturbance flushing a bird. These incidental observations show intraspecific interactions between Green Herons might be more influential than human disturbance on foraging behavior at our study sites.
A possible explanation for lack of observable effect of human disturbance on Green Heron foraging behavior in our study might be that birds habituated to the level of disturbance at each locale and became more tolerant in disturbed areas (i.e., al River site). In areas where people commonly visit or are continually present, some species of birds appear to habituate to certain types of human activities (Weller, 1999; Lord el al., 2001). Habituation and tolerance to human disturbance has frequently been reported in colonial waterbirds (Vos et al., 1985; Nisbet, 2000; Stolen, 2003). Gill et al (2001) found no evidence human presence reduced the number of Black-tailed Godwits (Limosa limosa) in estuaries that have some of the highest levels of recreational use in Britain. In addition, levels of human activity did not influence distribution or habitat use in their study. Webb and Blumstein (2005) found Western Gulls (Larus occidentalis) on the Santa Monica Pier (a heavily visited tourist attraction in California, U.S.A.) showed a gradient of behavioral change and tolerance that reflected a gradient of human disturbance. In our study Green Herons may have learned to tolerate human disturbance, suggesting habituation, where it commonly (and historically) occurs (i.e., headwaters of San Marcos River), but are less habituated to disturbance in areas of reduced or infrequent human disturbance.
The risk of mortality associated with human presence is a major factor likely to influence whether or not species tolerate, or avoid, humans (Gill et al, 2001). For example species that are hunted as game might avoid humans more than species not hunted. One reason for Green Herons' lack of detectable response to human disturbances could be because this species is not hunted. The response to disturbance can be seen as a trade-off between food intake and the perceived risk of predation by human presence (Gill et al., 1996). However, it is still possible that disturbance effects actually do occur but were obscured by other factors mentioned above. Indirect effects like reduction in access to nesting and foraging sites were not identified in this study. There were occasions when observations were not possible for periods of time because Green Herons were not present at the river site. Presumably this could have been due to the number or distribution of people recreating on the river. Furthermore, under abnormal circumstances, human disturbance at these sites could present a significant conservation problem. For example, if severe environmental conditions cause birds to experience great stress (e.g., severe weather) any additional effects of human disturbance could exacerbate their situation. It should be noted, however, that the extent to which a bird is tolerant might depend on the availability of alternative resources and the physical state of the individual bird. Some birds under stress, for example, during cold weather when there is an increased risk of starvation, might be less easily disturbed by humans (Stillman and Goss-Custard, 2002) or predators (McGowan et al., 2002) than at other times. Nonetheless, it is important to point out that altered behavior is not necessarily negative if birds are still able to readily acquire adequate amounts of food perhaps if disturbance is sufficiently brief (Burger and Gochfeld, 1998).
Our results demonstrate the examined effects of human recreational disturbance on Green Heron foraging behavior and efficiency in an urbanized landscape are varied. Tolerance and habituation at sites with more regular and frequent human recreational activities may likely be occurring, whereas irregular and less frequent human disturbance at other sites may alter foraging behavior. Waterbirds are important resources for scientific research because they can function as biological indicators that might help understand and predict human impacts on an ecosystem level (Kushlan, 1993). Identifying a waterbird that is fairly tolerant of human disturbance might lessen the effects that investigator intrusions or activities might have on quality and reliability of data collected, as well as stress on the birds. Our findings are cautionary but encouraging about supporting the life history requirements of Green Herons in aquatic systems with varying levels of anthropogenic recreational activities.
Acknowledgments.--We thank The Texas Ornithological Society; Aaron Wallendorf and Taylor Heard at the Meadows Center for Water and the Environment for allowing us access to Spring Lake and for use of their kayaks; John K. Johnson at the Texas State University Outdoor Center for use of his facilities and gear; Michelle E. Curtis, Matthew B. Haverland and Rebekah J. Rylander for being excellent field assistants; Dr. Francis Rose for use of his canoe; Melanie Howard at the City of San Marcos for providing access to the restoration project areas; and Dr. Floyd "Butch" Weckerly for his statistical expertise and advice.
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AMANDA A. MOORE (1), M. CLAY GREEN, DAVID G. HUFFMAN and THOMAS R. SIMPSON
Wildlife Ecology Program, Department of Biology, Texas Stale University-San Marcos, 78666
(1) Corresponding author: e-mail: firstname.lastname@example.org
Submitted 4 January 2015
Accepted 15 June 2016
Table 1.--Mean proportion of time ([+ or -]SE) Green Herons (Butorides virescens) spent exhibiting each documented behavior and efficiency rates for all observations and at each study site, San Marcos, Texas, 2013 and 2014 All birds Low disturbance site N [bar.x] [+ or -] SE N Standing 154 0.784 [+ or -] 0.012 48 Stalking 152 0.071 [+ or -] 0.006 48 Walk Slowly 154 0.050 [+ or -] 0.004 48 Peering/Alert * 154 0.047 [+ or -] 0.008 48 Prey Handling 154 0.024 [+ or -] 0.005 48 Preening 154 0.013 [+ or -] 0.005 48 Flight * 154 0.008 [+ or -] 0.001 48 Walk Quickly 154 0.005 [+ or -] 0.001 48 Efficiency 138 0.756 [+ or -] 0.022 40 Medium disturbance site [bar.x] [+ or -] SE N Standing 0.805 [+ or -] 0.022 60 Stalking 0.038 [+ or -] 0.007 58 Walk Slowly 0.031 [+ or -] 0.005 60 Peering/Alert * 0.057 [+ or -] 0.016 60 Prey Handling 0.042 [+ or -] 0.015 60 Preening 0.012 [+ or -] 0.005 60 Flight * 0.010 [+ or -] 0.001 60 Walk Quickly 0.005 [+ or -] 0.001 60 Efficiency 0.841 [+ or -] 0.029 56 High disturbance site [bar.x] [+ or -] SE N Standing 0.782 [+ or -] 0.019 46 Stalking 0.089 [+ or -] 0.010 46 Walk Slowly 0.063 [+ or -] 0.009 46 Peering/Alert * 0.036 [+ or -] 0.068 46 Prey Handling 0.016 [+ or -] 0.005 46 Preening 0.002 [+ or -] 0.001 46 Flight * 0.007 [+ or -] 0.002 46 Walk Quickly 0.007 [+ or -] 0.001 46 Efficiency 0.699 [+ or -] 0.040 42 [bar.x] [+ or -] SE Standing 0.763 [+ or -] 0.023 Stalking 0.082 [+ or -] 0.011 Walk Slowly 0.054 [+ or -] 0.007 Peering/Alert * 0.048 [+ or -] 0.015 Prey Handling 0.014 [+ or -] 0.006 Preening 0.028 [+ or -] 0.015 Flight * 0.008 [+ or -] 0.001 Walk Quickly 0.003 [+ or -] 0.001 Efficiency 0.762 [+ or -] 0.040 * Behavior not considered for further analysis Table 2.--Candidate models examining the relationship between habitat and disturbance on the foraging behaviors of Green Herons (Butorides virescens) in San Marcos, Texas, 2013 and 2014 Model K A1C [DELTA] Log-likelihood Standing Disturbance 3 -144.30 0.00 1.00 Null# 2 -142.35 1.95 0.38# Habitat 4 -140.18 4.12 0.13 Stalking Habitat# 4# -373.58# 0.00 1.00 Disturbance 3 -370.13 3.44 0.18 Null 2 -361.90 11.68 0.00 Walking slowly Habitat# 4# -465.04# 0.00 1.00 Disturbance 3 -462.71 2.33 0.31 Null 2 -459.28 5.76 0.06 Walking quickly Habitat# 4# -1039.78# 0.00 1.00 Null 2 -1037.30 2.49 0.29 Disturbance 3 -1035.38 4.40 0.11 Efficiency Habitat# 4# 142.495# 0.00 1.00 Null 2 145.788 3.29 0.19 Disturbance 3 147.225 4.73 0.09 Model [w.sub.i] [r.sup.2] Standing Disturbance 0.67 0.019 Null# 0.25 -- Habitat 0.08 0.001 Stalking Habitat# 0.85# 0.086# Disturbance 0.15 0.059 Null 0.00 -- Walking slowly Habitat# 0.73# 0.049# Disturbance 0.23 0.028 Null 0.04 -- Walking quickly Habitat# 0.71# 0.029# Null 0.21 -- Disturbance 0.08 0.006 Efficiency Habitat# 0.78# 0.037# Null 0.15 -- Disturbance 0.07 0.003 The model selection procedure is summarized by the number of parameters estimated in the model (K), Akaike's Information Criterion (AIC), the difference between the AIC of a model and the model with the smallest AIC ([DELTA]), the Akaike weight indicating relative support for the model ([w.sub.i]), and [r.sup.2] values. The bold- faced values indicate the model selected for that response variable Table 3.--Model parameter estimate, standard errors (SE), and confidence intervals of specified foraging behaviors and efficiency rates of Green Herons in San Marcos, Texas Stalk Coef. Coefficient SE lb * Est. ub * Study site model River (intercept) 0.010# 0.062# 0.082# 0.102# Boardwalk 0.014# -0.073# -0.044# -0.016# Lake 0.014 -0.020 0.007 0.035 Disturbance model Undisturbed (intercept) 0.008# 0.038# 0.053# 0.069# Disturbed 0.011 0.014# 0.037# 0.060# Still Coef. Coefficient SE lb * Est. ub * Study site model River (intercept) 0.022# 0.719# 0.763# 0.807# Boardwalk 0.031 -0.020 0.042 0.103 Lake 0.030 -0.039 0.019 0.078 Disturbance model Undisturbed (intercept) 0.016# 0.773# 0.806# 0.838# Disturbed 0.024 -0.096 -0.048 0.000 Slow walk Coefficient SE lb * Coef. Est. ub * Study site model 0.008# 0.039# 0.054# 0.069# River (intercept) 0.011 -0.044# -0.023# -0.001 Boardwalk Lake Disturbance model Undisturbed 0.010 -0.012 0.009 0.029 (intercept) 0.006# 0.029# 0.041# 0.053# Disturbed 0.009# 0.003# 0.020# 0.037# Fast walk Coefficient SE lb * Coef. Est. ub * Study site model 0.001 0.000 0.003# 0.005# River (intercept) 0.002 -0.001# 0.003 0.006 Boardwalk Lake Disturbance model Undisturbed 0.002# 0.001 0.004# 0.007# (intercept) 0.001 0.003# 0.005# 0.007# Disturbed 0.001 -0.002 0.000 0.003 Efficiency (a) Coefficient SE lb * Coef. Est. ub * Study site model 0.039# 0.684# 0.762# 0.840# River (intercept) 0.056 -0.033 0.079 0.190 Boardwalk Lake Disturbance model Undisturbed 0.052 -0.173 -0.071 0.032 (intercept) 0.031# 0.708# 0.769# 0.831# Disturbed 0.044 -0.114 -0.026 0.061 Note: Estimates in boldface type represent the statistically significant covariates for each response variable. * Coefficient estimates are given with lower (lb) and upper (ub) bounds of 95% confidence intervals. Covariates are statistically significant if confidence intervals exclude 0. (a) Statistical analyses were performed with arcsine-transformed data, but untransformed data are presented here.
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|Author:||Moore, Amanda A.; Green, M. Clay; Huffman, David G.; Simpson, Thomas R.|
|Publication:||The American Midland Naturalist|
|Date:||Oct 1, 2016|
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