Southeastern Naturalist 691 M.F. Delany, R.A. Kiltie, S.L. Glass, and C.L. Hannon 22001144 SOUTHEASTERN NATURALIST 1V3o(4l.) :1639,1 N–7o0. 44 Spatial Occupancy and Abundance Trends of Endangered Florida Grasshopper Sparrows at Three Lakes Wildlife Management Area Michael F. Delany1,*, Richard A. Kiltie1, Stephen L. Glass2, and Christina L. Hannon2 Abstract - We analyzed spatiotemporal variations in point counts of Ammodramus savannarum floridanus (Florida Grasshopper Sparrow) at Three Lakes Wildlife Management Area (WMA) during 2003–2012 to provide a detailed report of population changes during that period. There were significant increases in estimates of occupancy probability and abundance of Florida Grasshopper Sparrows at count points in some parts of Three Lakes WMA from 2003 to 2008 followed by significant reductions in these estimates from 2008 to 2012. Inconsistent, finer-scale population fluctuations appeared to be occurring within these time periods. From 2003 to 2012, estimates of overall change in occupancy probability and abundance of the Florida Grasshopper Sparrows at count points were largely negative throughout the area, but a region in the northeast portion of the WMA may offer the greatest chance for population persistence. The count points characterized by most persistent occurrence and abundance were ≥600 m from the edge of non-prairie habitat, at higher elevations (18.5–19.0 m above sea level), and associated with areas burned within the previous 2 years. Causes for the overall population decline are unknown, but appear to be acting over the entire range of the Florida Grasshopper Sparrow. Introduction Ammodramus savannarum floridanus Mearns (Florida Grasshopper Sparrow) (AOU 1957) is an endangered subspecies endemic to the south-central prairie region of Florida (USFWS 1999). The metapopulation was comprised of 7 partially isolated breeding populations and fewer than 1000 individuals (Delany et al. 2007a, Tucker et al. 2010). Florida Grasshopper Sparrows on Three Lakes Wildlife Management Area (WMA) have been monitored with annual point-count surveys since 1991 and were the most stable breeding aggregation monitored on public land in terms of population trends during that time (Tucker et al. 2010). Previously, we described environmental influences on abundance and detection of Florida Grasshopper Sparrows from studies conducted at Three Lakes WMA prior to 2009, when the population appeared to be thriving (Delany et al. 2013). However, populations on all monitored public lands are apparently now declining for unknown reasons, and the subspecies may be in jeopardy of extinction (Florida Grasshopper Sparrow Working Group, Vero Beach, FL, unpubl. data). To augment available information so that the population trajectories at Three Lakes WMA may be better understood, we examined Florida Grasshopper Sparrow point-count data at Three Lakes WMA from a spatial perspective during 1Florida Fish and Wildlife Conservation Commission, 1105 SW Williston Road, Gainesville, FL 32601. 2Florida Fish and Wildlife Conservation Commission, 1231 Prairie Lakes Road, Kenansville, FL 34738. *Corresponding author - mike.delany@myFWC.com. Manuscript Editor: John Kilgo Southeastern Naturalist M.F. Delany, R.A. Kiltie, S.L. Glass, and C.L. Hannon 2014 Vol. 13, No. 4 692 extreme years of a recent population fluctuation (2003–2008) described in Delany et al. (2013) plus the ensuing 4 years of apparent decline. Accurate spatial and temporal information on the abundance and occurrence of avian species is needed to predict their ability to persist and determine appropriate management strategies (Ruth et al. 2003). Point counts provide comparable parameters of abundance at the species level (Blondel et al. 1981, Ralph et al. 1993) and are useful for determining population trends (Nur et al. 1999). The spatial scale of occurrence and abundance is also important in understanding population dynamics (Buckland and Elston 1993, Channell and Lomolino 2000, Wiens 1989). Patterns of distribution and abundance determined from point-count surveys may provide insight into management actions needed to benefit Florida Grassh opper Sparrows. Methods Study site and population monitoring The northernmost Florida Grasshopper Sparrow population documented thus far occurs at Three Lakes WMA on a 3000-ha relict patch of native dry prairie (Bridges 2006). Dry prairie is a distinct floristic region of south–central Florida dominated by fire-dependent grasses, Serenoa repens (Bartram) Small (Saw Palmetto), low shrubs, and abundant forbs (Orzell and Bridges 2006). The plant community type is determined by topography, fire history, hydrology, and land-management history (Platt et al. 2006, Stephenson 2011). The WMA used prescribed fire during the dormant (October–March) and growing seasons at 2- to 3-year intervals to manage for Florida Grasshopper Sparrows. We monitored the Florida Grasshopper Sparrow population on Three Lakes WMA annually during the breeding seasons (April–June) of 2003–2012 using point-count surveys (Ralph et al. 1993, modified after Walsh et al. 1995). Florida Grasshopper Sparrows are non-migratory and sedentary during the breeding season (Delany et al. 1995). We established a fixed-grid system of 190 count-points 400 m apart to cover most of the area occupied by Florida Grasshopper Sparrows; 24 points located in unsuitable vegetation were not included in our analysis (n = 166 points). For most count points, we conducted a survey at each point on 3 separate days each year by recording visual and auditory observations of Florida Grasshopper Sparrows during a 5-minute interval. We made unlimited-radius observations between sunrise and 10:00 hrs in the absence of rain and at wind velocities <10 km/hr. Maximum detection distance at a point was not formally limited, but was estimated to be 200 m. See Delany et al. (2013) for more details about the locale, study population, and the point-count methodology. Statistical analysis We used the package “unmarked” (Fiske and Chandler 2011, Fiske et al. 2012) for the R statistical environment (R Development Core Team 2012) to perform our analyses. We applied MacKenzie et al.’s (2002, 2006) method for modeling occupancy and detection probabilities from replicated presence/absence surveys, and we applied Royle’s (2004) maximum-likelihood method for modeling abundance and detection probability from replicated point counts. Southeastern Naturalist 693 M.F. Delany, R.A. Kiltie, S.L. Glass, and C.L. Hannon 2014 Vol. 13, No. 4 We explored the use of multiyear, open population models (Dail and Madsen 2011), but they provided unsatisfactory fits to the data; therefore, occupancy probability and abundance models were applied separately to each year’s set of 3 replicate visits. Covariates of occupancy probability or abundance were: distance (m) from a point to the nearest edge of suitable habitat for Florida Grasshopper Sparrows (i.e., toward the perimeter of the study area), mean elevation (ft) above sea level of the area around the survey point (average elevation of GIS-grid cells within the 200-m radius count-point sample area), UTM easting (corresponding to longitude), and UTM northing (corresponding to latitude); covariates were on a log scale. We derived elevation from the National Elevation Data Set (Gesch et al. 2002), and extracted distance to edge from habitat maps based on Landsat images using ArcView Version 3.3 (ESRI 2002). Covariates of detectability were on a logistic scale and included: observer identification, starting time, and ordinal day of year. All covariate effects were modeled as linear and additive, and the continuous covariates were scaled to mean = 0, standard deviation = 1 (Fiske and Chandler 2012). These covariates differed somewhat from those used in Delany et al. (2013) because of different availabilities for the years treated here. Goodness-of-model-fit was assessed by parametric bootstrap (250 resamples) on the estimated model (MacKenzie et al. 2006). We set the threshold value P ≥ 0.05 as the criterion for acceptable fit based on error chi-square for occupancyprobability models and on sum-of-squared errors for abundance models. Quantiles (0.025 and 0.975) from 1000 parametric bootstrap resamples of the models were used to obtain 95% confidence intervals of yearly mean occupancy-probability and abundance estimates. To examine spatial patterns of occupancy and abundance during apparent extreme years of the recent fluctuation (2003, 2008, and 2012), we arrayed mean estimates for each survey point-grid on maps of the area. We calculated absolute differences (later minus earlier) between mean estimates for these years for each count point. We standardized the differences by dividing by the square root of the sum of the average squared standard error of the estimates for the two years compared at each survey point (Yang and Dalton 2012). Because absolute between-year differences in mean occupancy probability and in abundance estimates at survey points were positively correlated with the mean squared standard errors at the points, standardizing the differences for precision effectively also standardized them for their absolute magnitude. Standardized differences can be treated as equivalent to a Z-score of a standard normal distribution and are considered large when Z ≥ 0.8 (Yang and Dalton 2012), but we used Z ≥ 1.0 as the large criterion. In addition, because 5% of a standardized normal-density distribution is less than -1.96 or >1.96 (Sokal and Rohlf 1981), absolute values of standardized difference values ≥2 can be considered significant at 2-sided P ≤ 0.05 (Bowman and Azzalini 1997). Our approach was spatial in the sense that the design and analysis were applied and interpreted on a point-wise basis across the spatial grid of count points, not in the more formal sense of statistical analyses that focus on spatial autocorrelations (e.g., Schabenberger and Gotway 2005). We placed survey points such that each Southeastern Naturalist M.F. Delany, R.A. Kiltie, S.L. Glass, and C.L. Hannon 2014 Vol. 13, No. 4 694 year’s counts could be considered independent other than through effects subsumed in environmental covariates. Results Mean occupancy-probability estimates for Florida Grasshopper Sparrows on Three Lakes WMA were highest in 2003 and 2008 and lowest in 2012 (Fig. 1). Figure 1. (Top) Mean ± 95% CI for yearly modeled estimates of % occupancy probability for Florida Grasshopper Sparrow in survey-point areas at Three Lakes WMA. For all occupancy probability models, goodness-of-fit tests gave P > 0.05. (Bottom) Mean ± 95% CI for yearly modeled estimates of Grasshopper Sparrow abundance in count-point areas. Values of truncated upper confidence limits for 2007, 2008, and 201 1 are shown. Southeastern Naturalist 695 M.F. Delany, R.A. Kiltie, S.L. Glass, and C.L. Hannon 2014 Vol. 13, No. 4 Mean abundance estimates were highest in 2008 and lowest in 2009 and 2012. The mean estimates showed some directional changes between these extreme years. Bootstrapped confidence intervals of the estimated mean occupancy probabilities each year were fairly consistent, whereas those for abundance estimates tended to be more variable. All occupancy-probability models, but not all abundance models, qualified as acceptable fits to the data. Distance to non-prairie edge and elevation were important covariates for Florida Grasshopper Sparrow occurrence and abundance (Tables 1, 2). Time of day and time of season were important sources of variation affecting the probability of detection. There was some variation among years (2003, 2008, and 2012) in effects of covariates. All models for these years met the criterion for adequate model fit. Mean estimated Florida Grasshopper Sparrow detection probabilities for the occupancy models were 0.34 (95% confidence limits = 0.28–0.40) for 2003, 0.44 (0.37–0.52) for 2008, and 0.38 (0.28–0.48) for 2012. For the abundance models, the mean estimated detection probabilities were 0.19 (0.07–0.29) for 2003, 0.15 (0.04–0.26) for 2008, and 0.22 (0.10–0.34) for 2012. The estimates have different ranges because the occupancy models estimate the probability of detecting that an area around a survey count-point is occupied by any individual, whereas abundance models estimate the probability of detecting each resident indi vidual at the point. Estimates of occupancy probability and abundance for Florida Grasshopper Sparrows at each survey point for 2003, 2008, and 2012 fluctuated (Fig. 2). From 2003 to 2008, mean estimates of occupancy probability decreased slightly over most of the western and southern portions of the area, and were greatest at the extreme southwest; occupancy probability increased in the northeastern portion Table 1. Parameter estimates of covariates for occupancy-probability models for Florida Grasshopper Sparrows at Three Lakes WMA fit for 2003, 2008, and 2012. Both model components were on logistic scales. Obs = reference observer; reference observer in all years was the same individual and effects for other observers represents comparison with the reference observer, but identities of the other observers differed among years. Parameter 2003 2008 2012 Occupancy Intercept 1.281 0.889 –0.559 Distance to edge 2.419A 1.141A 1.421A Mean elevation 1.758A 0.706 1.029 Easting -1.343 0.325 –0.961A Northing -0.896 0.821 -0.067 Detection Intercept -0.319 –0.053 –1.019A Obs 1 -1.406A -0.174 0.970A Obs 2 -0.815A -0.408 0.441 Time -0.634A -0.333A -0.418A Date -0.376A 0.054 -0.416A P(GOF) 0.833 0.924 0.936 AP(>|z|) < 0.05. The covariate had a significant effect on occupancy probability and detection. Southeastern Naturalist M.F. Delany, R.A. Kiltie, S.L. Glass, and C.L. Hannon 2014 Vol. 13, No. 4 696 (Fig. 3). Conversely, surveyors’ abundance estimates increased at count points in most of the area during this time period, but especially in the eastern portion. Both occupancy probabilities and abundance estimates decreased at most points from 2008 to 2012, but these patterns were somewhat less marked in the western portion (Fig. 3). The net absolute change from 2003 to 2012 was a decline that tended to be most extreme for occupancy probability in the central and southern parts of the area, whereas the decrease in abundance was most marked in the north. When we standardized these absolute changes by the precisions of the pointwise estimates, the patterns of change became somewhat starker (Fig. 4). Occupancy probability from 2003 to 2008 showed large or significant decreases toward the west and south, whereas it showed large or significant increases in the northeastern portion of the area. Most points showed large or significant standardized increases in abundance between 2003 and 2008, except in parts of the northwest and extreme southwest. From 2008 to 2012, most standardized changes were significantly negative except for occupancy probability in the extreme west and south, where standardized changes were milder or even positive. Overall for 2003 to 2012, most points showed large or significant negative change. Table 2. Parameter estimates of covariates for abundance models for Florida Grasshopper Sparrows at Three Lakes WMA fit for 2003, 2008, and 2012. Abundance-model components were on the log scale and detection components were on the logistic scale. Obs = reference observer; reference observer in all years was the same individual and effects for other observers represents comparison with the reference observer, but identities of the other observers dif fered among years. Parameter 2003 2008 2012 Abundance Intercept 0.117 0.802A -0.591A Distance to edge 0.529A 0.419A 0.757A Mean elevation 0.845A 0.430A 0.636A Easting -0.658A -0.023 -0.610A Northing 0.071 0.223 0.070 Detection Intercept -1.474A -1.668A -1.771A Obs 1 -0.830A 0.382 0.838A Obs 2 0.381 -0.443A 0.343 Time -0.564A -0.455A -0.537A Date -0.261A 0.001 -0.445 P(GOF) 0.474 0.076 0.665 AP(>|z|) < 0.05. The covariate had a significant effect on abundance and detection. Figure 2 (following page). Estimated percent occupancy probability (left) and abundance (right) of Florida Grasshopper Sparrows at each count point on Three Lakes WMA in 2003, 2008, and 2012. Symbol-point sizes here and in following figures reflect magnitude of the estimated quantities and are not proportional to areas sampled at each point. Southeastern Naturalist 697 M.F. Delany, R.A. Kiltie, S.L. Glass, and C.L. Hannon 2014 Vol. 13, No. 4 Southeastern Naturalist M.F. Delany, R.A. Kiltie, S.L. Glass, and C.L. Hannon 2014 Vol. 13, No. 4 698 Figure 3. Estimated absolute change in percent occupancy probability (left) and in estimate d a b u n d a n c e (right) of Florida Grasshopper Sparrows at each count point on Three Lakes WMA from 2003 to 2008, 2008 to 2012, and overall from 2003 to 2012. Southeastern Naturalist 699 M.F. Delany, R.A. Kiltie, S.L. Glass, and C.L. Hannon 2014 Vol. 13, No. 4 Figure 4. Estimated standardized change in percent occupancy probability (left) and in estimated abundance (right) of Florida Grasshopper Sparrows at each count point on Three Lakes WMA from 2003 to 2008, 2008 to 2012, and overall from 2003 to 2012. Standardized change >1 or less than -1 is considered large (Yang and Dalton 2012), and standardized change >2 or less than -2 is considered significant (Bowman and Azzalini 1997). Southeastern Naturalist M.F. Delany, R.A. Kiltie, S.L. Glass, and C.L. Hannon 2014 Vol. 13, No. 4 700 Discussion Our summaries of Florida Grasshopper Sparrow abundance and occupancy for 2003, 2008, and 2012 may represent snapshots between which appreciable change on a finer time scale may have occurred. Average occupancy probability appears to have declined from 2003 to 2006, when it may have reached a low level close to that of 2012, before rebounding to the high level of 2008. Average abundance at count points appears to have changed more gradually from 2003 to a peak in 2008 before crashing in 2009, and then failing to recover through 2012. Maps of Florida Grasshopper Sparrow occupancy probability and abundance at Three Lakes WMA for 2009 (not shown) are essentially identical to those for 2012. Mean estimated occupancy probability decreased by 38% from 2003 to 2012, whereas mean estimated abundance at count points decreased by 47% over that interval. Occupancy probability on Three Lakes WMA tends to be somewhat less volatile than abundance. Our data suggest that Florida Grasshopper Sparrows may have varied between being widely distributed at low abundance in 2003 to having more extensive regions of greater abundance when numbers increased (2008). This pattern of habitat occupancy is consistent with the clustered distribution of Florida Grasshopper Sparrow territories elsewhere in its range (Delany et al. 2007b). On an absolute basis, volatility of occupancy probability and abundance at particular points has depended on the time scale of comparisons. During 2003–2012, absolute abundance changed more in the northern half of the area, whereas occupancy probability changed more in the southern half. During the periods 2003–2008 and 2008–2012, abundance varied more in the northeast portion of th e area. Occupancy probability also varied considerably in that portion over the 4- and 5-year intervals, but varied in the southwest portion as well. The decline in occupancy probability from 2003 to 2008 in the southwest may have been accentuated along the two southernmost rows of point-count stations due to mechanical treatment of the prairie (roller chopping) at these locations during January 2008 and delayed prescribed fire. The resulting dense accumulation of thatch may have made vegetation structure in this area less suitable for nesting Grasshopper Sparrows (see Vickery 1996). Although the magnitude of changes in occupancy probability and in abundance varied among count points and time scales, the negative changes during 2003–2012 generally were large and significant. The area around and south of Godwin Hammock (a lacunar area in the northeast portion of the WMA that is characterized by woody vegetation unsuitable for Grasshopper Sparrows) offers the greatest chance of serving as a refugium for occurrence and to a lesser extent abundance, from which the population may recover. Nevertheless, this area exhibited the same basic changes in occupancy and abundance observed on the rest of the study site. Variation in the spatial configuration of habitat quality apparently influenced patterns of Florida Grasshopper Sparrow occurrence and abundance. Documenting unambiguous associations from avian-survey data can be difficult (Temple and Wiens 1989). However, multi-year distributional data may provide useful information on habitat quality (Hames et al. 2001). Areas of persistent sparrow occurrence and abundance identified here corresponded to landscape features (i.e., ≥600 m Southeastern Naturalist 701 M.F. Delany, R.A. Kiltie, S.L. Glass, and C.L. Hannon 2014 Vol. 13, No. 4 from the edge of non-prairie habitat, higher elevations [18.5–19.0 m above sea level]) that correlated with increased Florida Grasshopper Sparrow abundance on the WMA from 2003 to 2008 (Delany et al. 2013, this study). Occurrence and increased abundance of Florida Grasshopper Sparrow at Three Lakes WMA was also associated with areas that were recently burned (Delany et al. 2013). Similarly, three declining populations of Florida Grasshopper Sparrows on Avon Park Air Force Range (Polk and Highlands counties, FL) exhibited spatial contractions to centrally located areas away from non-prairie edges (Delany and Kubilis 2002). Areas of Florida Grasshopper Sparrow persistence at Avon Park Air Force Range had greater cover of potential sparrow runways (i.e., continuous open spaces ≥4.0 cm wide) and bare ground than areas with lower probabilities of occurrence (Tucker and Bowman 2006). These features of vegetation structure are characteristic of recently burned locations and important for this ground-dwellin g sparrow. Land-management activities at Three Lakes WMA (prescribed fire and removal of encroaching woody vegetation) seem to meet habitat requirements of the Florida Grasshopper Sparrow (Delany et al. 2013). Further, the large area of dry prairie at Three Lakes WMA and characteristics of the vegetation composition and structure appear to be conducive to the persistence of Florida Grasshopper Sparrows. Therefore, interrelated local-scale factors (e.g., at the level of the breeding territory or nest site) not measured here may be causing the population decl ine. The Florida Grasshopper Sparrows at Three Lakes WMA represent a disjunct population at the edge of the species’ range, and therefore, it may be more variable in density and abundance than populations nearer to the center of the species distribution (Curnutt et al. 1996, Qinfeng et al. 2005). Estimates of population density variability usually increase with the number of years included in the calculation (Pimm and Redfearn 1988). The recent decline of the Florida Grasshopper Sparrow at Three Lakes WMA and at other monitored locations (Delany et al. 2007a, Tucker et al. 2010) may be part of a long-term trend or a fluctuation in the population cycle. The Florida Grasshopper Sparrow breeding aggregation on Three Lakes WMA is considered crucial to the persistence of the subspecies (Perkins et al. 2008). However, unless the population trend reverses, Florida Grasshopper Sparrows will become extirpated from Three Lakes WMA. Monitoring is a critical part of effective species conservation. In addition, a management plan is needed in which a specific management action is triggered by a severe population decline (Lindenmayer et al. 2013). Annual point-count surveys should continue to monitor the status of Florida Grasshopper Sparrows. Centrally located areas of more persistent sparrow occurrence within a population seem to be especially important, and suitable habitat at these locations should be maintained with prescribed fire and the removal of encroaching woody vegetation. Additional study of reproductive success and survival is needed to identify causes of decline. Information on spatial changes presented here may inform the design of future research (e.g., locations of study plots). Three Lakes WMA may contain the only remaining population of Florida Grasshopper Sparrows with a sufficient number of birds t o study. Southeastern Naturalist M.F. Delany, R.A. Kiltie, S.L. Glass, and C.L. Hannon 2014 Vol. 13, No. 4 702 Acknowledgments B. Ames, A. Blackford, H. 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