|
||||
| Home | Archives | About | Login | Submissions | Notify | Contact | Search | ||||
Copyright © 2007 by the author(s). Published here under license by The Resilience Alliance. Go to the pdf version of this article The following is the established format for referencing this article: Nocera, J. J., G. Forbes, and G. Milton 2007. Habitat relationships of three grassland breeding bird species: broadscale comparisons and hayfield management implications. Avian Conservation and Ecology - Écologie et conservation des oiseaux 2(1): 7. [online] URL: http://www.ace-eco.org/vol2/iss1/art7/ Research Papers Habitat Relationships of Three Grassland Breeding Bird Species: Broadscale Comparisons and Hayfield Management Implications Relations entre l’habitat et trois espèces d’oiseaux nicheurs de prairies : comparaisons à grande échelle et implications pour l’aménagement de champs de foin 1Queen's University, 2University of New Brunswick, 3Nova Scotia Department of Natural Resources
Generalized recommendations for the conservation of birds in agro-ecosystems have been elusive because studies are often of a local nature, and do not compare source data against those from other regions. In this study, we developed geographically broad habitat relationship models to provide conservation prescriptions for three species that breed in farmed grasslands: Bobolink (Dolichonyx oryzivorus), Savannah Sparrow (Passerculus sandwichensis), and Nelson’s Sharp-tailed Sparrow (Ammodramus nelsoni subvirgatus). We develop models from our study in Nova Scotia, Canada and confront them with independent data from Wisconsin, USA pastures and Iowa, USA restored prairies. Vegetation that was higher and denser in the prebreeding season was linked to increased occupancy rates and abundance of Bobolinks in each study region. Providing tall spring grass is easily accomplished by not cutting late in the previous year. Savannah Sparrows were instead associated with shorter and sparser spring grass, which highlights the need to simultaneously provide heterogeneous habitat for otherwise ecologically similar species. Nelson’s Sharp-tailed Sparrows were more likely to occur, and be numerous, in areas with greater availability of drainage ditches. They and several other species would benefit from provision of ditches with adequate vegetation to promote occupancy. By combining these with other well-established recommendations, such as a delayed first harvest, a greater net conservation benefit can be realized from these working landscapes. Key words: agriculture; grassland birds; habitat-relationship models; harvest dates; vegetation height-density. Many grassland birds breed readily and successfully in natural landscapes, such as tall and shortgrass prairie, but working agricultural landscapes, i.e., agro-ecosystems, are becoming an increasingly important focus for grassland bird conservation (e.g., Vickery et al. 2004). More than half the world’s ecologically productive lands are under agricultural production (Badgley 2003), and the sum of conservation efforts in farmlands can therefore provide an enormous net benefit (Petit et al. 1999). Agro-ecosystems are too often treated as “ecological sacrifice areas” (Badgley 2003) in conservation planning. It has been consistently difficult to recommend broad-scale conservation actions for grassland birds in working landscapes partly as a result of two factors: (1) most information is collected at a local extent and assumed to apply across a species’ distribution, and (2) vegetation characteristics, although important, are often the only set of explanatory variables considered. For example, invertebrates are the primary prey of many grassland bird species and show correlations with abundance (Söderström et al. 2001), but very few studies of grassland birds have addressed such variables (c.f., Wittenberger 1980, Clere and Bretagnolle 2001). This is likely because it is easier to discuss management options with producers regarding vegetation than it is invertebrate prey availability; nonetheless, examining such variables is essential to refining conservation approaches for grassland birds. Appropriate conservation recommendations should come from examination of more than one correlate of fitness, such as where the species is found (presence/absence), how many are there, i.e., abundance, and whether they are breeding, i.e., reproductive activity. Many studies examine only one of these parameters at a time. These results may be misleading, for instance, because reproductive rates and abundance may not be correlated (Winter and Faaborg 1999, Fletcher and Koford 2002) and can provide very different indications of habitat quality and population persistence from which to direct conservation efforts (Van Horne 1983). Effective management in agro-ecosystems requires understanding the broadscale importance of explanatory variables biologically relevant to more than one population response. The predictive accuracy of models should also be tested both within the generating dataset and against independent datasets from other areas (e.g., Betts et al. 2006). In this study, we sought to determine which, if any, variables were consistently associated with indirect measures of fitness components for several bird species residing in agricultural grasslands. We selected three grassland bird species to study: Bobolink (Dolichonyx oryzivorus), Savannah Sparrow (Passerculus sandwichensis), and Nelson’s Sharp-tailed Sparrow (Ammodramus nelsoni subvirgatus – Acadian race). The Acadian race of Nelson’s Sharp-tailed Sparrow, hereafter “Nelson’s Sparrow”, primarily breeds in saltmarsh, but commonly shows facultative breeding in wetter areas of agricultural land (Nocera et al. 2007), similar to the midwestern subspecies (A. n. nelsoni; Murray 1969, Greenlaw and Rising 1994). We chose these species because they breed in different grassland types, e.g., hayfields, pastures, prairie, and have broad geographic ranges, except for Nelson’s Sparrow, which has a very restricted range and offers a basis for comparison. We model their occurrence, abundance, and reproductive activity with predictive variables describing vegetation structure, management activity, physiography, and prey abundance. The models are developed from field data collected from hayfields in Nova Scotia, Canada, and tested against models generated using independent data, made available to us from extensive studies in different environments, i.e., prairie, restored grasslands, and pasture, elsewhere in Iowa and Wisconsin, USA. Ultimately, if certain variables retain their importance across such a geographic distance as Nova Scotia to Iowa and Wisconsin, and across the range of agro-ecosystem land uses we examine here, then we can make a case for model generality and more confidently identify appropriate conservation actions at a broad scale. Study sites From May to August 2002–2004, we conducted breeding bird and habitat surveys across sites in the western Annapolis Valley, Nova Scotia, Canada, centered on 44°45’N, 65°31’W): Belleisle (210 ha), Upper Belleisle (116 ha), Queen Anne (180 ha), and Pea Round (142 ha). These sites are dyked and drained for agriculture. Fields at Queen Anne, Belleisle and Upper Belleisle are mixtures of timothy (Phleum pratense), meadow fox-tail (Alopecurus pratensis), various bluegrass species (Poa spp.), and reed canary grass (Phalaris arundinacea). The same mixtures of grasses are planted at Pea Round, with the addition of several homogenous alfalfa swards (Medicago sativa). Study fields were not grazed or planted in nonforage crops, and did not receive any pesticide applications during the study. Date of first hay harvest at Queen Anne, Upper Belleisle, and Belleisle takes place after 1 July, which is later than the typical for the region, except for a small field cut early for silage at Queen Anne. Harvest dates were staggered within these three sites, i.e., 1 July–15 August. Conversely, Pea Round is cultivated solely for dairy operations and most harvest is for silage; first-cut begins before 15 June. Breeding bird sampling Bobolinks, Savannah Sparrows, and Nelson’s Sparrows were surveyed at 52 point-count stations distributed among a total of 18 fields, i.e., 52 stations x 3 yr each, visited 8-12 times/yr [mean = 10 counts per stations]. To avoid double-counting birds, point-count station centres were placed >175 m apart. This distance is greater than the typical territory size of Bobolinks (Martin and Gavin 1995), which have the largest territories among the species we studied. Five-minute point-count surveys (50 m radii; Hutto et al. 1986) were conducted between 30 min after sunrise and 1000 AST, i.e., weather permitting: wind <25kph, no precipitation. We chose a 50 m radius for point-counts because Nelson’s Sparrow can be difficult to detect reliably beyond this distance (Nocera, unpublished data). Because most fields at our study sites were under a delayed hay harvest regimen, we conducted point-count surveys throughout the breeding season. However, in five instances at Pea Round, where fields were cut early, we ceased counts at stations in those fields after cutting. All point-count data were summarized by mean abundance per season, to reduce the inflationary effect of floaters and non-residents on overall analyses (Betts et al. 2005). We modeled mean abundance for each study species with the sexes combined and also constructed sex-specific models for Bobolinks, as they are sexually dichromatic. Mean density per station for all years and sexes combined was 0.91 for Bobolink, 1.25 for Savannah Sparrow, and 0.19 for Nelson’s Sparrow. We chose to intensively sample our 52 stations, i.e., 10 times each, to calculate indirect estimates of reproductive activity. Reproductive activity was indexed by monitoring breeding phenology and behavior of birds whose territories included the point-count station (sensu Vickery et al. 1992; Christoferson and Morrison 2001; Betts et al. 2005). Based on these behavioral observations, each station was ascribed an ordinal reproductive activity index, for each species, for the season. Following Vickery et al. (1992), we attributed an index score of 0 when no, or few, birds were present; 1 for male presence only (> three weeks); 2 for male and female presence only (> 3 wk); 3 for a confirmed pair present, seen with nesting material, or exhibiting other signs of breeding; 4 for adults seen carrying food to presumed nestlings; and 5 for evidence of fledging success. Our observations of food carrying are unlikely to have been biased by mate feeding, as no species we studied delivers food to incubating mates (Wheelwright and Rising, 1993, Greenlaw and Rising 1994, Martin and Gavin 1995). However, we cannot rule out possible observations of self-feeding, although this bias is likely to be small because invertebrates, easily observed in the bill, are the sole food fed to nestlings of each study species, whereas adults supplement up to 50% of their diet with small seeds that are eaten whole (Martin and Gavin 1995). To determine if a bird was a resident, we use 3 wk from the date of first observation at a station as a cut-off to help ensure that fewer nonterritorial floaters are counted accidentally. This reproductive activity index has been shown to underestimate true reproductive success (Rivers et al. 2003), but the estimates are consistent and allow for statistical comparisons across many landscape types and study designs (Betts et al. 2005). Predictive variable sampling We included 27 variables from four broad categories: vegetation, prey, management, and physiography as predictors of breeding bird demographics (Table 1). Vegetation was measured in 100 m wide plots, centered on each point-count station. Vegetation measurements were made twice per season, first during territory establishment in late May, and again in late June. The mean of four vegetation measurements, one taken in each cardinal direction, for each plot was used as the predictive variable. Grass height and density, hereafter “HD” (Table 1) were measured simultaneously in each plot using a visual obstruction “Robel pole” (Robel et al. 1970) placed ~5 m from the plot center. Live vegetative cover (%), and the percent of that cover that was grass or forbs, were determined within plots using Daubenmire frames (Daubenmire 1959) thrown (> 5 m) in each cardinal direction from the plot center. Litter depth was measured within the frame with a ruler against a profile cut from the soil. The presence/absence of alfalfa, a known negative correlate of Bobolink abundance (Bollinger 1988; Bollinger et al. 1990), and slough cordgrass (Spartina pectinata), a putative positive correlate of Nelson’s Sparrow occurrence (Murray 1969) was also determined for each plot. Terrestrial invertebrate abundance, quantified as catch-per-unit-effort, was sampled using pitfall traps checked weekly throughout the study season. One pitfall trap was placed 10 m north and south of each plot center. Pitfall traps were 15 cm deep plastic cups, flush to soil surface, with 3–5 cm of a soap and water mixture on the bottom. All captured specimens were identified to order, or further when possible. Although pitfall traps captured specimens from a large number of taxa, those most represented by this method, and reported here, are Aranea (spiders), Chilopoda (centipedes), Coleoptera (beetles), Diplopoda (millipedes), Isopoda (sowbugs), Lumbricina (earthworms; class Oligochaeta), Stylommatophora (slugs; class Gastropoda), and Opiliones (harvestmen). To sample a greater proportion of flying insects, we conducted weekly sweep-netting, which involved a series of horizontal sweeps with a sailcloth sweep net through the grass canopy along a 10 m transect east and west of plot centers. All captured specimens were identified to order, or further when possible, and quantified as catch-per-unit-effort. Although sweeps captured specimens from a large number of taxa, this method captured more aerial arthropods than pitfall trapping, and better represented Diptera (flies), Hemiptera (true bugs), Hymenoptera (wasps, bees, ants), Lepidoptera (moths, butterflies), Odonata (primarily damselflies), and Orthoptera (grasshoppers). Both adults and larvae were represented in Hymenoptera and Lepidoptera, and we use the order name in reference to only adults. Because some larvae of these two orders cannot be separated without labor-intensive techniques, we collapsed larvae data to a composite variable of “Lepidoptera and Hymenoptera larvae” (LHL, Table 1) that can be viewed more simply as “caterpillars”. Note that both techniques captured a large number Coleoptera. We separate these in modeling according to capture method (Table 1) because sweeps collected more Elateridae (click beetles) and pitfalls more Carabidae (ground beetles and tiger beetles). Lastly, we included five variables generally descriptive of physiography and management. For each plot, we converted date of harvest to an annual-Julian date. For modeling, we use date of harvest in the current year and in the previous year. To control for annual effects we included the sample year as a covariate. Because pilot work (Nocera and Milton, unpublished data) in 2000-2001 indicated that Nelson’s Sparrows frequented hayfield drainage ditches, we also quantified the amount of ditch contained in each plot and measured from 1:10,000 scale air photos. Independent data sets To test the models, we obtained data (Renfrew 2002) from a random subsample of 50 point-count stations conducted each year from 1997-1999 across 74 pastures in six southwestern Wisconsin counties. Point-count surveys of five-minute duration with a fixed 100 m radius were conducted twice per year (once each in May and June) during which all individuals seen or heard were counted. Because a 100 m radius was used in the Wisconsin study, which covers four times more area than counts using 50 m at the other two sites, i.e., Nova Scotia and Iowa, we reduced all abundance data from Wisconsin by a factor of four. This correction requires the realistic, although untested, assumption that the relationship between log-area and log-density is linear. The two species in common with the present study are Savannah Sparrow, which were sampled in comparable densities (0.83 individuals/sample point vs. 1.25 in Nova Scotia), and Bobolink, which occurred in higher densities in Nova Scotia (0.19 vs. 0.91). Vegetation measurements in Wisconsin were conducted in late May along four transects at right angles to each point-count station. Measurements in common with our study (as per Table 1) were % live cover, % of live cover that was forbs, and litter depth, which were estimated to the nearest 5% in Daubenmire frames. Visual obstruction measurements were made with a Robel pole (HD as per Table 1), modified slightly as readings were made at a height of 1.5 m. Cover measurements were similar to the present study, but because the study was conducted in grazed pastures, both HD (mean = 0.87 dm) and litter measurements (mean = 1.02 cm) were below those in Nova Scotia hayfields (mean = 3.0 dm and 3.3 cm, respectively). The second independent dataset (Fletcher 2003) is a random subsample of 204 point-count stations drawn from a total 279 and 42 transects drawn from a total 60 conducted from 1999–2000 across 30 prairie/restored grassland sites in northern Iowa. Point-count surveys were conducted within a fixed 50 m radius, and counts along 25 m width transects of 100–400 m length, three times per year, generally once in late May, mid-June, and early July, during which all individuals seen or heard were counted. Bobolinks were sampled at higher densities (1.7 individuals/sample unit) to the present study (0.91), however Savannah Sparrows were encountered at lower densities (0.34 vs. 1.25). Iowa vegetation measurements were conducted in July at four locations within a 50 m radius of each point-count station, and at five locations in each 25 m increment of a transect route. Those in common with the present study (as per Table 1) were live cover (%), and the percent of that cover that was either forbs or grass, estimated in Daubenmire frames. Also, visual obstruction measurements were taken using a Robel pole (see above; HD as per Table 1), where litter depth measurements were also made. Cover measurements were very similar to the present study, but either because of vegetation differences or because measurements were made later in the season, HD is, on average greater (mean = 4.7 dm; Fletcher and Koford 2002) than ours sampled in May (mean = 3.0 dm). Statistical analysis Statistical models were generated using R version 2.4.1 (R Development Core Team 2006). Prior to modeling each response, we reduced the 27 explanatory variables (summarized in Table 1) to a smaller group of candidate variables. From this, we generated global models using the retained variables and used mixed-effects logistic regression to model occupancy, ordinal regression to model reproductive activity, and linear mixed-effects regression to model abundance. We accounted for temporal and possible spatial pseudoreplication in our data by including site and point-count station as nested random effects. We then chose the best-fit model as that which most reduced Akaike’s Information Criterion (AIC; Akaike 1974). We further assess model fit by examining the parameter estimates of the final model and the variance associated with the random effects. Predictive variables We first accounted for the fact that abundance of each study species differed between sites, creating potential heterogeneity in detection probability. Although grassland birds occur in very open areas, and yield a high degree of detection during point-count surveys (particularly at ranges <50 m; Rotella et al. 1999), we accounted for any latent detection heterogeneity by a posteriori calculation of a ‘conditional detection probability’ (Pc; Royle and Nichols 2003) for each site. We then used the resultant Pc as a covariate in all occupancy models. We then reduced the set of 27 predictive variables by (1) eliminating the variable with the strongest collinearity within a pair (Graham 2003), and (2) using statistical criteria to select putatively important variables. To account for collinearity, we calculated r for each pairwise combination of variables. For those pairs with r > 0.7, we retained the variable we felt was more likely associated with habitat selection. For instance, because of persistent strong collinearity between May and June vegetation HD measurements, we used May samples, as they were more likely to be a cue used in territory establishment. For models of occupancy and reproductive activity, candidate explanatory variables were selected, from those not eliminated because of collinearity, by retaining only those singular variables with a Mallow’s Cp score (Mallow 1973) above that of a null model. The resultant set of candidate models contained the global model and all possible combinations of retained variables. For abundance models, following the elimination of variables due to collinearity, candidate explanatory variables were selected by retaining only those above an arbitrarily set threshold of the upper 50% of the 27 candidate explanatory variables with the highest individual F-statistic, which also had at least 0.10 pr(|t|). The 50% threshold lacks statistical basis, but served to substantially reduce the dimensionality of models built from an otherwise complex set of variables. The candidate model set then contained the global model and all possible combinations of retained variables. Variables were log transformed whenever it improved variance homogeneity and brought the model residual distribution closer to normality through visual assessment of qq-norm plots. However, for discussion purposes we present nontransformed variables (e.g., Table 1). Models We built mixed-effects logistic regression (function ‘lmer’ in package ‘lme4’ in R; version 0.9975-13, Bates and Sarkar 2007), incorporating a binomial distribution and logit link, to model point-count data, which were reduced to binary presence/absence (occupancy). Mean abundance of birds was modeled with linear mixed-effects regression (package ‘nlme’ in R; version 3.1-79, Pinheiro et al. 2006), maximizing the restricted log-likelihood to fit the model. In both cases, we included ‘site’ and ‘point-count station’ as nested random effects. Because the reproductive activity data are ordinal (semi-quantitative) and cannot be fitted to a logistic model, we fitted an ordinal regression model using proportional odds (package ‘Design’ in R; version 2.0-12, Harrell 2005; see also Walker and Duncan 1967; Guisan and Harrell 2000). We identified that ordinal models met the equal slopes assumption of ordinal modeling by examining smoothed partial residual plots. We did not explicitly examine random effects in these models because the proportional odds structure applies the logit cumulatively, not separately, so as to estimate both random and fixed effects (Agresti 2002). In addition, the nature of the reproductive activity index is itself cumulative; in other words, its derivation is dependent upon temporal pseudoreplication. The resultant set of top models were compared to the reduced models (i.e., only variables with analogs in our study) for Iowa and Wisconsin data on mean abundance and occupancy (reproductive activity data were not available). From this model set, we then compared the top models to determine which variables were retained in common across the three study regions. Uncertainty about the importance of a variable in Nova Scotia models was considered ‘reduced’ (i.e., prediction success was higher) if Iowa or Wisconsin models retained the same variables. For each response variable modeled, the predictive variables retained for model building are listed in Table 2. In general, conditional detection probability estimates (Pc) were similar among sites. There was a slight difference in Pc between sites for all study species (Table 1) but was not strong enough to be retained as a candidate variable in model sets for any response. All top models describing occupancy (Table 2), abundance and reproductive activity were associated with AIC values substantially below that of the global models. Except for the model describing overall, i.e., both sex, abundance of Bobolinks, little variance was associated with the random effects of site and point-count station. Parameter estimates for each model are presented in Table 3. Abundance models The top model (Table 2) for Bobolink abundance (both sexes combined) indicated positive relationships to HD and the abundance of Orthoptera and Hemiptera (many of which were meadow plant bugs [Leptopterna dolabrata, suborder Heteroptera] and others in family Miridae). The positive relationship between HD and Bobolink abundance was particularly strong (Fig. 1). However, this model had the highest variance associated with the random effects of ‘site’ and ‘point-count station’ of any model assessed. A similar model was selected for female abundance (Table 2), showing a positive relationship with HD and Hemiptera abundance. Among Bobolink models, the model for female abundance also showed the least variance associated with random effects. The top male abundance model (Table 2) was a positive relationship with vegetative cover (%) and a negative relationship with the presence of alfalfa. Models for the two sparrow species could be constructed only for the sexes combined because they are sexually monochromatic. For both species, the variance associated with random effects was trivial. The best Savannah Sparrow abundance model (Table 2) was a positive association with flies (Diptera) and a negative relationship to both HD and centipedes (Chilopoda). The strong negative relationship to HD (Fig. 2) is in contrast to the positive relationship seen with Bobolink abundance. The top model for Nelson’s Sparrow abundance (Table 2) was a positive relationship with abundance of grasshoppers (Orthoptera) and ditches, and a negative relationship with presence of alfalfa. The relationship between Nelson’s Sparrow abundance and ditches was remarkably strong (Table 3). Occupancy models The top model (Table 2) for presence/absence of Bobolink (sexes combined) indicated a positive relationship to HD and a negative relationship to forb cover. The top Savannah Sparrow model (Table 2) showed their presence/absence was related positively with the number of beetles in pitfall traps (Coleoptera, primarily Carabidae) and related negatively with vegetation HD and forb cover. The negative influence of HD was the strongest among the variables in the top model of Savannah Sparrow occupancy. The top Nelson’s Sparrow model (Table 2) showed positive relationships with caterpillars, spiders (Aranea), and ditches. Again, the models for the two sparrow species were not influenced by the variance associated with the random effects. Reproductive activity models The top Bobolink reproductive activity model (Table 2) was a positive relationship with HD, spiders, flies, and damselflies (Odonata). The influence of HD on Bobolink reproductive activity was particularly strong compared to the remaining variables in the top model (Table 3). The top Savannah Sparrow reproductive activity model (Table 2) was a positive association with harvestmen (Opiliones) and a negative association with forb cover. The top model for Nelson’s Sparrows, like Bobolinks, showed a positive relationship to HD and spiders. The relationship between Nelson’s Sparrow reproductive and ditches was the strongest of any assessed in the study (Table 3). Because of the proportional odds structure of ordinal regression, random effects were not modeled. Models from independent data sets Comparing the best models from our study site to only analogous variables for each regional dataset yielded the top models in Table 4. Vegetation HD was most important in every model for Bobolink occupancy and abundance (sexes combined) and represents a strongly predictive variable. HD was also negatively related with Savannah Sparrow abundance in Iowa prairie/restored grasslands (as we observed in Nova Scotia hayfields), but positively associated with Savannah Sparrow abundance in Wisconsin pastures. HD was also retained as a positive term in the top models for Savannah Sparrow occupancy in Wisconsin (but was negative in Nova Scotia). Two very different models (Table 4) to predict male Bobolink abundance were chosen for Iowa and Nova Scotia. Wisconsin data were not available. This study identified habitat characteristics important to three grassland bird species across a large geographic area. Moreover, the importance of these habitat features was observed in agro-ecosystems under three different forms of land use. This allows for the development of some generalized conservation and habitat management prescriptions for the three species, which should benefit other species as a result. This study also illustrates the importance of confronting models with data from other regions and independent research, which makes our recommendations more likely to be applicable across a range of agricultural grasslands and regions. For Bobolinks, higher and denser grass in May, i.e., the pre-breeding season, was associated with increased occupancy rates and abundance in each study region, i.e., Nova Scotia, Iowa, and Wisconsin, representing three agricultural landscape types: hayfield, pasture, and restored prairie. This is consistent with other reported correlations of Bobolink abundance from other regions (Herkert 1994, Bollinger 1995). It is worth noting that the high variance associated with random effects in Bobolink abundance models is likely related to the propensity of males to aggregate their territories, although this speculation warrants further examination. Reproductive activity, in Nova Scotia, was also positively related to vegetation height-density in May for both Bobolinks and Nelson’s Sparrow. This highlights the general importance of vegetation height-density (see also Madden et al. 2000), which has been linked with fitness for other species associated with farmland like the Magpie (Pica pica) and Meadow Pipit (Anthus pratensis) (Perkins et al. 2000). Increased height and density (in the spring) could indicate suitable nesting locations because it facilitates nest concealment and reduces detection by predators (Fondell and Ball 2004), and provides seeds for foraging granivores in both early- and late-season (Vickery and Gill 1999). Conversely, Savannah Sparrow abundance showed a negative relationship with vegetation height-density in both Nova Scotia and Iowa, but the reverse in Wisconsin pastures. This pattern of differential response to vegetation height-density has been documented before (see Madden et al. 2000), particularly for Savannah Sparrows (Fondell and Ball 2004). We attribute that vegetation height-density was negatively related with Savannah Sparrow abundance in Iowa and Nova Scotia, but positively associated with Savannah Sparrow abundance in Wisconsin pastures to an artefact either of pastures being so open that birds were using the tallest of what was otherwise very short grass, or were placing their nests in localized tufts of tall grass, but occurring throughout the pasture. This suggests a unimodal relationship between Savannah Sparrow abundance and vegetation height-density that could be tested with additional data from areas with more mid-range vegetation height-density values than those examined here. This again highlights the ubiquity of the importance of vegetation height-density, but it is counter to the patterns that would benefit Bobolinks and Nelson’s Sparrows. Although this species is limited by other physiographic factors, such as patch size (Vickery et al. 1994), it is not as much of a conservation concern because it is widespread and common (Dale et al. 1997). Vegetation height-density in the pre-breeding season is an easily managed habitat characteristic. Ensuring fields are not cut late (e.g., after late August in Nova Scotia) in the preceding year, to allow for extra growth and height prior to winter, should promote reproductive activity for Nelson’s Sparrow and Bobolink, and both Bobolink occupancy and abundance (see Fig. 1). In northern climes, this would mean forgoing either a third hay crop or a very late second crop for the year, usually harvested in September (Nocera and Milton, unpublished data). Refraining from cutting after August is a feasible practice, as third harvests of forage crops tend to be low yield (e.g., Kallenbach et al. 2002) and are often harvested only to “clean up the field” rather than truly increase haylage stocks (Nocera and Milton, unpublished data). If a farm were in need of late-season harvests, simultaneous management could still be accomplished by staggering harvest across a farm so that some fields are cut after August to benefit species like Savannah Sparrows, while others are not cut after August to benefit other species. Vegetation heterogeneity in landscapes could be managed to benefit as many taxa as possible. Birds can reproduce successfully in agricultural grasslands in which farmers delay their hay harvest until after fledging (Bollinger et al. 1990, Vickery et al. 2004, Nocera et al. 2005). Given the results of the current study, we suggest that, in addition to not harvesting too early (during the breeding period), it is likewise important to also not harvest some fields too late in the season (early fall), to ensure adequate growth in the spring. These recommendations would require that hay harvest be restricted to an optimal time window, which, in Nova Scotia, would be from early July to late August. We predict a large net conservation benefit from such a prescription for many species, which remains to be tested. Such benefits will also help agricultural landscapes shed the sometimes-unwarranted image of being reproductive sinks and “ecological traps” (Best 1986; Dale et al. 1997); a delayed first harvest will allow birds to reproduce successfully and a forgone late-season, often third, harvest will ensure adequate vegetative structure when they arrive in the spring. The farmer need not lose from this prescription, because hay of high nutritional quality can still be harvested (Nocera et al. 2005) during July and August. An important connection to landscape physiography is the positive relationship between the total amount of ditch and Nelson’s Sparrow occupancy and abundance. Nelson’s Sparrows likely use hayfield ditches as foraging areas and saltmarsh surrogates, to fulfill a general requirement for dense, damp swards of vegetation (Greenlaw and Rising 1994; Hanson and Shriver 2006). Typically, ditches that remain uncut provide a damp environment with tall coarse vegetation. A simple management action that would benefit this species in agricultural lands during the breeding season is to ensure some ditches remain uncut. In Nova Scotia, farmers often cut grass in ditches and do not use the resultant hay (Nocera and Milton, unpublished data). This is largely because the dominant grass species in ditches tends to be coarse and early maturing, e.g., reed canary grass, making it unpalatable for livestock. Therefore, an easily executed management action is to limit the cutting of ditch grass to only periods of maintenance, the benefits of which are not limited to Nelson’s Sparrow. For instance, in the UK, species of some conservation concern such as Whitethroats (Sylvia communis), Yellowhammers (Emberiza citrinella), and Reed Buntings (E. schoeniclus) can all benefit from proper ditch management (Vickery et al. 2004). Prey did not influence whether Bobolinks occurred at a site. Savannah Sparrow occupancy patterns, however, did show a positive relationship with the abundance of pitfall-trapped beetles, i.e., primarily ground beetles, Carabidae. This is consistent with the finding that beetles constitute a substantial proportion of the Savannah Sparrow diet (>33%, Wheelwright and Rising 1993; Kobal et al. 1998), particularly during years of low grasshopper abundance (Miller and McEwen 1995). Additionally, Nelson’s Sparrow occupancy was positively associated with abundance of caterpillars and spiders, both of which represent important prey items for adults and nestlings (Greenlaw and Rising 1994; particularly spiders, Montagna 1942). On the other hand, arthropod prey was more often associated with how many individuals resided in an area. This does not suggest that the species are limited by prey availability (Wiens 1969), but rather indicates a simple tendency to settle in areas of greater resources. Bobolink abundance (in models for females and both sexes combined) was positively associated with true bugs; one of the top four prey groups fed to nestling Bobolinks (Wittenberger 1982). Hemipterans have one of the highest levels of gross energy content and fat composition among insects in agro-ecosystems (Robel et al. 1995), particularly members of family Miridae (plant bugs), which constituted the bulk of Hemipterans sampled in this study. Nelson’s Sparrow abundance was likewise positively related to grasshopper abundance; an important link given that grasshopper species representing four different families are known to be food for nestlings (Greenlaw and Rising 1994). Abundance of Savannah Sparrows also showed positive correlation to the abundance of flies, another known prey item (Wheelwright and Rising 1993; Kobal et al. 1998). Reproductive activity, for all three species, was positively related to the abundance of spiders and their allies. The abundance of spiders caught in pitfalls was retained in the top model for Bobolink and Nelson’s Sparrow reproductive activity, while harvestmen were in the top model for Savannah Sparrow. We suggest that, although spiders are important prey for most grassland birds, they may not contribute directly to reproductive activity but are likely associated with a latent variable. This prediction warrants further examination. An important caveat to our prey relationship models is that our taxonomic information was very coarse. This is a common problem in grassland bird literature. Most studies do not identify arthropod prey beyond family or order, largely because they must examine partially digested regurgitate or highly fragmented prey, e.g., recoveries from nestlings with ligatures around their necks to prevent swallowing, which renders detailed taxonomic identification impossible, and is usually biased towards detecting hard-bodied insects (Wheelwright and Rising 1993). Additionally, prey availability, and hence, what a bird may or may not eat, is notoriously difficult to estimate because it is a complex function of abundance, body size, nutrients, energy (calories), life stage, palatability, and ease of capture (Poulin and Lefebvre 1997). Therefore, particularly when size or nutrient value varies greatly within a group, more precise taxonomic identification of prey would yield more informative results (Poulin and Lefebvre 1997). This level of detail is an important and untouched aspect of grassland bird ecology that future studies should try to consider. Our assessment of models across regions highlights the ubiquitous importance of the variables retained in top models. However, we acknowledge that not all relationships, such as those we observed, could be expected to be consistent across space and time. For instance, Davis (2004) found temporal variation in relationships between grassland birds and habitat between years. We found little important variation between years; we included year as a factorial explanatory variable and it was never retained in any best-fit models. Secondly, habitat use patterns can vary across space, sometimes substantially (Johnson and Igl 2001). Such spatial variation has been suggested for Nelson’s Sparrow (Nocera et al. 2007), so it is unfortunate that we were unable to procure independent data to test our habitat models for Nelson’s Sparrow in other parts of its range. Our models for this species are thus only truly pertinent to our study area. However, because the habitat features in our models were at least adequate to support Nelson’s Sparrow in Nova Scotia, our management recommendations likely pertain elsewhere, or are at least unlikely to elicit adverse effects. Therefore, we caution that our models have not defined “high-quality” Nelson’s Sparrow habitat; this needs further study at a broader landscape scale than we performed. Nonetheless, the models we present provide a robust assessment of resource use by some grassland birds in agro-ecosystems and the association with their occurrence, abundance and measures of reproduction. This is an important distinction, because models typically include only one population response parameter of interest, which may be misleading when trying to assess or manage for several aspects of population dynamics (Van Horne 1983). The net benefit derived from any of the management suggestions presented here are all the more likely to be greater if incorporated into sympathetic agro-ecosystem management that provides for habitat heterogeneity. In our study, we examined three response variables from among many other measurable variables. It will be worthwhile for future research to examine how model development when confronted with population parameters that may behave very differently, such as mortality and fledging success. Regardless of the variables chosen, further studies that include multiple responses, study sites, and broad habitat assessments are likely to accrue even greater conservation benefit. Responses to this article are invited. If accepted for publication, your response will be hyperlinked to the article. To submit a response, follow this link. To read responses already accepted, follow this link ACKNOWLEDGMENTSOur appreciation is extended to those who assisted with fieldwork: T. Fitzgerald, W. Fitzgerald, S. Glinz, C. Lawrie, S. LeMoine, Y. Luc, M. Peckford, J. Pennell, L. Penney, F. Rousseu, and E. Whidden. We also thank the farmers of the western Annapolis Valley who cooperated and/or supported this and companion studies. We are indebted to R. Renfrew and R. Fletcher for providing the independent datasets from Wisconsin and Iowa, respectively. Early drafts of this manuscript were enhanced by comments from M. Betts, R. Cunjak, A. Diamond, T. Fitzgerald, R. Fletcher, A. Hanson, J. Houlahan, D. Keppie, R. Renfrew, N. Simon, P. Vickery and R. Webster. Two anonymous reviewers provided valuable suggestions to improve the final draft. JJN was supported by a doctoral scholarship (#2.37A.3S) from Wildlife Habitat Canada, and also gratefully acknowledges financial and logistical assistance from the NS Habitat Conservation Fund, EJLB Foundation, Canadian Wildlife Federation, NS Dept. Natural Resources, and Canadian Wildlife Service. All field methods used in this research complied with the current laws of Canada. Agresti, A. 2002. Categorical data analysis. Wiley, Hoboken, New Jersey, USA. Akaike, H. 1974. A new look at statistical model identification. IEEE Transactions on Automatic Control 19:716-723. Badgley, C. 2003. The farmer as conservationist. American Journal of Alternative Agriculture 18:206-212. Bates, D., and D. Sarkar. 2007. lme4: Linear mixed-effects models using S4 classes. R package version 0.9975-13. Available online at: http://www.r-project.org. Best, L. B. 1986. Conservation tillage: ecological traps for nesting birds? Wildlife Society Bulletin 14:308-317. Betts, M.G., A. W. Diamond, G. J. Forbes, M-A. Villard, and J. S. Gunn. 2006. The importance of spatial autocorrelation, extent and resolution in predicting forest bird occurrence. Ecological Modelling 191:197-224. Betts, M. G., N. P. P. Simon, and J. J. Nocera. 2005. Point-count summary statistics differentially predict reproductive activity in bird-habitat relationship studies. Journal of Ornithology 146:151-159. Bollinger, E. K. 1988. Breeding dispersion and reproductive success of Bobolinks in an agricultural landscape. Dissertation, Cornell University, Ithaca, New York, USA. Bollinger, E. K. 1995. Successional changes and habitat selection in hayfield bird communities. Auk 112:720-730. Bollinger, E. K., P. B. Bollinger, and T. A. Gavin. 1990. Effects of hay-cropping on eastern populations of the Bobolink. Wildlife Society Bulletin 18:142-150. Christoferson, L. L., and M. L. Morrison. 2001. Integrating methods to determine breeding and nesting status of 3 western songbirds. Wildlife Society Bulletin 29:688-696. Clere, E., and V. Bretagnolle. 2001. Disponibilité alimentaire pour les oiseaux en milieu agricole: biomasse et diversité des arthropodes capturés par la méthode des pots-pièges. Revue d'écologie – La Terre et la Vie 56:275-297. Dale, B. C., P. A. Martin, and P. S. Taylor. 1997. Effects of hay management on grassland songbirds in Saskatchewan. Wildlife Society Bulletin 25:616-626. Daubenmire, R. 1959. A canopy-coverage method of vegetational analysis. Northwest Science 33:43-64. Davis, S. K. 2004. Detecting area sensitivity of grassland passerines: effects of patch size, patch shape, and vegetation structure on bird abundance and occurrence in southern Saskatchewan. Auk 121:1130-1145. Fletcher, R. J. 2003. Spatial and temporal scales of distribution and demography in breeding songbirds: implications of habitat fragmentation and restoration. Dissertation, Iowa State University, Ames, Iowa, USA. Fletcher, R. J., and R. R. Koford. 2002. Habitat and landscape associations of breeding birds in native and restored grasslands. Journal of Wildlife Management 66:1011-1022. Fondell, T. F., and I. J. Ball. 2004. Density and success of bird nests relative to grazing on western Montana grasslands. Biological Conservation 117:203-213. Graham, M. H. 2003. Confronting multicollinearity in ecological multiple regression. Ecology 84:2809-2815. Greenlaw, J. S., and J. D. Rising. 1994. Sharp-tailed Sparrow (Ammodramus caudactus). In A. Poole and F. Gill, editors. Birds of North America, Number 112. Academy of Natural Sciences, Philadelphia, Pennsylvania, USA, and American Ornithologists' Union, Washington, D.C., USA. Guisan, A., and F. E. Harrell. 2000. Ordinal response regression models in ecology. Journal of Vegetation Science 11:617-626. Hanson, A. R., and W. G. Shriver. 2006. Breeding birds of northeast saltmarshes: habitat use and conservation. Studies in Avian Biology 32, in press. Harrell, F. E. 2005. Design: Design Package. R package version 2.0-12. Available online at: http://www.r-project.org. Herkert, J. R. 1994. The effects of habitat fragmentation on midwestern grassland bird communities. Ecological Applications 4:461-471. Hutto, R. L., S. M. Pletschet, and P. Hendricks. 1986. A fixed-radius point-count method for non-breeding and breeding season use. Auk 103:593-602. Johnson, D. H., and L. D. Igl. 2001. Area requirements of grassland birds: a regional perspective. Auk 118:24-34. Kallenbach, R. L., C. J. Nelson, and J. H. Coutts. 2002. Yield, quality, and persistence of grazing- and hay-type alfalfa under three harvest frequencies. Agronomy Journal 94:1094-1103. Kobal, S. N., N. F. Payne, and D. R. Ludwig. 1998. Nestling food habits of 7 grassland bird species and insect abundance in grassland habitats in northern Illinois. Transactions of the Illinois State Academy of Science 91:69-75. Madden, E. M., R. K. Murphy, A. J. Hansen, and L. Murray. 2000. Models for guiding management of prairie bird habitat in northwestern North Dakota. American Midland Naturalist 144:377-392. Mallow, C. L. 1973. Some comments on Cp. Technometrics 12:591-612. Martin, S. G., and T. A. Gavin. 1995. Bobolink (Dolichonyx oryzivorus). In A. Poole and F. Gill, editors. Birds of North America, Number 176. Academy of Natural Sciences, Philadelphia, Pennsylvania, USA, and American Ornithologists' Union, Washington, D.C., USA. Miller, C. K., and L. C. McEwen. 1995. Diet of nesting Savannah Sparrows in interior Alaska. Journal of Field Ornithology 66:152-158. Montagna, W. 1942. The Sharp-tailed Sparrows of the Atlantic coast. Wilson Bulletin 54:107-120. Murray, B. G. 1969. A comparative study of the Le Conte’s and Sharp-tailed Sparrows. Auk 86:199-231. Nocera, J. J., T. M. Fitzgerald, A. R. Hanson, and G. R. Milton. 2007. Differential habitat use by Acadian Nelson’s Sharp-tailed Sparrows: implications for regional conservation. Journal of Field Ornithology 78:50-55. Nocera, J. J., G. J. Parsons, G. R. Milton, and A. H. Fredeen. 2005. Compatibility of delayed cutting regime with bird breeding and hay nutritional quality. Agriculture, Ecosystems, and Environment 107:245-253. Perkins, A. J., M. J. Whittingham, R. B. Bradbury, J. D. Wilson, A. J. Morris, and P. R. Barnett. 2000. Habitat characteristics affecting use of lowland agricultural grassland by birds in winter. Biological Conservation 95:279-294. Petit, L. J., D. R. Petit, D. G. Christian, and H. D. W. Powell. 1999. Bird communities of natural and modified habitats in Panama. Ecography 22:292-304. Pinheiro, J., D. Bates, S. DebRoy, and D. Sarkar. 2006. nlme: Linear and nonlinear mixed effects models. R package version 3.1-79. [Available online at: http://www.r-project.org. Poulin, B., and G. Lefebvre. 1997. Estimation of arthropods available to birds: Effect of trapping technique, prey distribution, and bird diet. Journal of Field Ornithology 68:426-442. R Development Core Team. 2006. R: a language environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available online at: http://www.r-project.org. Renfrew, R. B. 2002. The influence of patch and landscape characteristics on grassland passerine density, nest success, and predators in southwestern Wisconsin pastures. Dissertation. University of Wisconsin, Madison, Wisconsin, USA. Rivers, J. W., D. P. Althoff, P. S. Gibson, and J. S. Pontius. 2003. Evaluation of a reproductive index to estimate dickcissel reproductive success. Journal of Wildlife Management 67:136-143 Robel, R. J., J. N. Briggs, A. D. Dayton, and L. C. Hulbert. 1970. Relationships between visual obstruction measurements and weight of grassland vegetation. Journal of Range Management 23:295-298. Robel, R. J., B. M. Press, B. L. Henning, K. W. Johnson, H. D. Blocker, and K. E. Kemp. 1995. Nutrient and energetic characteristics of sweepnet-collected invertebrates. Journal of Field Ornithology 66:44-53. Rotella, J. J., E. M. Madden, and A. J. Hansen. 1999. Sampling considerations for estimating density of passerines in grasslands. Studies in Avian Biology 19:237-243. Royle, J. A., and J. D. Nichols. 2003. Estimating abundance from repeated presence-absence data or point-counts. Ecology 84:777-790. Söderström, B., T. Pärt, and E. Linnarsson. 2001. Grazing effects on between-year variation of farmland bird communities. Ecological Applications 11:1141-1150. Van Horne, B. 1983. Density as a misleading indicator of habitat quality. Journal of Wildlife Management 47:893-901. Venables, W. N., and B. D. Ripley. 2002. Modern Applied Statistics with S. Fourth edition. Springer, New York, New York, USA. Vickery, J. A., and J. A. Gill. 1999. Managing grassland for wild geese in Britain: a review. Biological Conservation 89:93-106. Vickery, J. A., R. B. Bradbury, I. G. Henderson, M. A. Eaton, and P. V. Grice. 2004. The role of agri-environment schemes and farm management practices in reversing the decline of farmland birds in England. Biological Conservation 119:19-39. Vickery, P. D., M. L. Hunter, and S. M. Melvin. 1994. Effects of habitat area on the distribution of grassland birds in Maine. Conservation Biology 8:1087-1097. Vickery, P. D., M. L. Hunter, and J. V. Wells. 1992. Use of a new reproductive index to evaluate relationship between habitat quality and breeding success. Auk 109:697-705. Walker, S. H., and D. B. Duncan. 1967. Estimation of the probability of an event as a function of several independent variables. Biometrika 54:167-178. Wheelwright, N. T., and J. D. Rising. 1993. Savannah Sparrow (Passerculus sandwichensis). In A. Poole and F. Gill, editors. Birds of North America, Number 45. Academy of Natural Sciences, Philadelphia, Pennsylvania, USA, and American Ornithologists' Union, Washington, D.C., USA. Wiens, J. A. 1969. An approach to the study of ecological relationships among grassland birds. Ornithological Monographs 8:1-93. Winter, M., and J. Faaborg. 1999. Patterns of area sensitivity in grassland-nesting birds. Conservation Biology 13:1424-1436. Wittenberger, J. F. 1980. Vegetation structure, food supply, and polygyny in bobolinks (Dolichonyx oryzivorus). Ecology 61:140-150. Wittenberger, J. F. 1982. Factors affecting how male and female Bobolinks apportion parental investments. Condor 84:22-39.
|
||||
| Home | Archives | About | Login | Submissions | Notify | Contact | Search | ||||
