Taxon-dependent Scaling: Beetles,Birds, and Vegetation at Four North American Grassland Sites

Because organisms respond to the environment at different scales, it is important to develop ways of determining the appropriate scales for a specific ecological process and organism. We consider whether the relative importance of different scales is associated with organism mobility, and whether this relationship is independent of landscape characteristics. We observed abundances of particular species for vascular plants, ground-dwelling beetles and breeding birds along eight 2-km transects of 40 sampling stations each, distributed over four sites along the regional gradient from shortgrass steppe in central Colorado to tallgrass prairie in central Kansas. For each transect and taxonomic group, the relative importance of factors measured at the trap scale (1 m; soil texture and hardness, vegetation height, bare ground), at the local scale (10 m; density of shrubs and cacti) and at the landscape scale (30 m; Landsat 7 TM spectral bands, slope and elevation) was assessed using hierarchical canonical variance partitioning with forward selection of explanatory variables. Plant, beetle and bird community composition was explained by environmental factors measured at all three scales. Factor influence was more consistent between transects and between plants and beetles for the more homogeneous landscapes of the shortgrass steppe than for the more heterogeneous landscapes of the tallgrass prairie. We conclude that, independent of the mobility of a taxonomic group, factors at several scales are important in explaining community composition. The importance of different scales shifts along a regional gradient, and the variability between sites is high even for nearby sites.     

Influence of landscape structure and stand age on species density and biomass of a tropical dry forest across spatial scales

Three central related issues in ecology are to identify spatial variation of ecological processes, to understand the relative influence of environmental and spatial variables, and to investigate the response of environmental variables at different spatial scales. These issues are particularly important for tropical dry forests, which have been comparatively less studied and are more threatened than other terrestrial ecosystems. This study aims to characterize relationships between community structure and landscape configuration and habitat type (stand age) considering different spatial scales for a tropical dry forest in Yucatan. Species density and above ground biomass were calculated from 276 sampling sites, while land cover classes were obtained from multi-spectral classification of a Spot 5 satellite imagery. Species density and biomass were related to stand age, landscape metrics of patch types (area, edge, shape, similarity and contrast) and principal coordinate of neighbor matrices (PCNM) variables using regression analysis. PCNM analysis was performed to interpret results in terms of spatial scales as well as to decompose variation into spatial, stand age and landscape structure components. Stand age was the most important variable for biomass, whereas landscape structure and spatial dependence had a comparable or even stronger influence on species density than stand age. At the very broad scale (8,000–10,500 m), stand age contributed most to biomass and landscape structure to species density. At the broad scale (2,000–8,000 m), stand age was the most important variable predicting both species density and biomass. Our results shed light on which landscape configurations could enhance plant diversity and above ground biomass.     

The Importance of Spatial Scale for Conservation and Assessment of Anuran Populations in Coastal Wetlands of the Western Great Lakes,USA

Distributions of pond-breeding amphibians may be influenced by habitat factors at different spatial scales. We used anuran calling surveys to investigate the association between 5 anuran species and habitat variables measured within 100, 500, 1000, and 3000 m of sampling points at 63 coastal wetlands along the US shores of Lake Michigan and Lake Huron. Stepwise logistic regression was used to create predictive models for each species at each spatial scale. Our results confirm the view that habitat variables at multiple scales influence frog distributions, but the strength of predictive models was significantly affected by the spatial scale at which habitat variables were derived. Remotely sensed habitat variables within a 3000 m radius were among the most effective predictors of occurrence for American toad (Bufo americanus), eastern gray treefrog (Hyla versicolor), spring peeper (Pseudacris crucifer), and green frog (Rana clamitans melanota). The western chorus frog (Pseudacris triseriata) was predicted most effectively by variables derived within a 500 m radius. For the most part, these anurans exhibited species-specific responses to habitat variables; however the suite of landscape-scale variables associated with urban land use appeared in all species’ regression models. Associations with landscape-scale variables coupled with well-documented habitat needs at local breeding sites suggest that conservation and assessment of frogs and toads in coastal wetlands should consider the influence of habitat variables at multiple spatial scales.     

Effects of interactive scale-dependent variables on beetle diversity patterns in a semi-arid agricultural landscape

Understanding species-diversity patterns in heterogeneous landscapes invites comprehensive research on how scale-dependent processes interact across scales. We used two common beetle families (Tenebrionidae, detrivores; Carabidae, predators) to conduct such a study in the heterogeneous semi-arid landscape of the Southern Judean Lowland (SJL) of Israel, currently undergoing intensive fragmentation. Beetles were censused in 25 different-sized patches (500–40,000 m²). We used Fisher’s α and non-parametric extrapolators to estimate species diversity from 11,125 individuals belonging to 56 species. Patch characteristics (plant species diversity and cover, soil cover and degree of stoniness) were measured by field transects. Spatial variables (patch size, shape, physiognomy and connectivity) and landscape characteristics were analyzed by GIS and remote-sensing applications. Both patch-scale and landscape-scale variables affected beetle species diversity. Path-analysis models showed that landscape-scale variables had the strongest effect on carabid diversity in all patches. The tenebrionids responded differently: both patch-scale and landscape-scale variables affected species diversity in small patches, while mainly patch-scale variables affected species diversity in large patches. Most of the paths affected species diversity both directly and indirectly, combining the effects of both patch-scale and landscape-scale variables. These results match the biology of the two beetle families: Tenebrionidae, the less mobile and more site-attached family, responded to the environment in a fine-grained manner, while the highly dispersed Carabidae responded to the environment in a coarse-grained manner. We suggest that understanding abiotic and biotic variable interactions across scales has important consequences for our knowledge of community structure and species diversity patterns at large spatial scales.     

Associations between soil carbon and ecological landscape variables at escalating spatial scales in Florida,USA

The spatial distribution of soil carbon (C) is controlled by ecological processes that evolve and interact over a range of spatial scales across the landscape. The relationships between hydrologic and biotic processes and soil C patterns and spatial behavior are still poorly understood. Our objectives were to (i) identify the appropriate spatial scale to observe soil total C (TC) in a subtropical landscape with pronounced hydrologic and biotic variation, and (ii) investigate the spatial behavior and relationships between TC and ecological landscape variables which aggregate various hydrologic and biotic processes. The study was conducted in Florida, USA, characterized by extreme hydrologic (poorly to excessively drained soils), and vegetation/land use gradients ranging from natural uplands and wetlands to intensively managed forest, agricultural, and urban systems. We used semivariogram and landscape indices to compare the spatial dependence structures of TC and 19 ecological landscape variables, identifying similarities and establishing pattern–process relationships. Soil, hydrologic, and biotic ecological variables mirrored the spatial behavior of TC at fine (few kilometers), and coarse (hundreds of kilometers) spatial scales. Specifically, soil available water capacity resembled the spatial dependence structure of TC at escalating scales, supporting a multi-scale soil hydrology-soil C process–pattern relationship in Florida. Our findings suggest two appropriate scales to observe TC, one at a short range (autocorrelation range of 5.6 km), representing local soil-landscape variation, and another at a longer range (119 km), accounting for regional variation. Moreover, our results provide further guidance to measure ecological variables influencing C dynamics.     

Spatial variability of soil metabolic rate along a dryland elevation gradient

A general framework of ecosystem hotspots suggests variation in soil metabolic activity can be understood through the relative distribution and intensity of patches of disproportionately high ecosystem process rates. To better understand the causes of soil metabolic spatial variability and the variation in ecosystem hotspots we quantified soil respiration (R) spatial heterogeneity across a network of seven sites spanning a 2,489 m elevation gradient in the Santa Rosa Mountains of Southern California. At each site, soil samples were collected from 0–5 and 5–15 cm soil depths at 2 m intervals along three 100 m transects. Each soil sample was analyzed for organic matter content (SOM) and was incubated at 40% water holding capacity for 20 days. R was measured at days 5 and 20. Strong contrasts were observed between the relationships of soil physical variables and R at scales of individual landscapes and the whole region. Notably, the relationship between SOM and R was positive within individual landscapes and negative across the entire region. Plant canopy microenvironments were associated with elevated SOM and R relative to the interspaces. This microenvironment effect on R was reduced by elevation, incubation interval, and soil depth. Geostatistical analyses conducted individually for each site identified an increasing range of autocorrelation from 2 to 10 m and a decreasing proportion of variation that was included in this range with elevation. These results suggest hotspots increase in size but decrease in intensity with elevation thereby creating a maximum hotspot effect at middle elevations.     

Optimal Sampling Scheme for Estimation Landscape Mapping of Vegetation Cover

An optimization of a sampling design aims at decreasing costs without losing necessary spatial information and desired precision for estimation and mapping of vegetation cover. This study concentrates on investigating optimal solutions for sampling design, considering both plot and sample size in terms of cost and variance estimated for global estimation and local landscape mapping of overall vegetation cover used in the management of soil erosion. A geostatistical method was developed based on regionalized variable theory and compared to a classical random sampling method for a case study in which optimal sampling was designed for estimating and mapping vegetation cover. Cost is introduced into the sampling design in terms of measurement time. This method has made it possible to seek optimal solutions for determining plot and sample sizes given a desired precision and allowable survey cost budget for both local and global estimation. The results show that the geostatistical method is more cost-efficient than the classical designs because it accounts for spatial dependence of variables in the sampling design. Moreover, plot size affects kriging standard error of the local estimate more significantly than sample size, while sample size has more effect on precision of the global estimate than does plot size.     

Statistical treatment and comparative analysis of scale-dependent aquatic transect data in estuarine landscapes

Estuarine ecosystem dynamics have evolved around and respond to landscape-level influences that are dynamic in space and time. The estuarine water column is effectively the physical and biologial integrator of these landscape inputs. In this paper, we present a floating window Analysis of Covariance (ANCOVA) technique to statistically compare and contrast aquatic transect data that were taken at different times and under different tidal conditions, yet were geographically parallel and spatially articulate. The floating window ANCOVA compared two transects by testing whether the means of the dependent variable were significantly different while also testing whether the slopes of patterns in the dependent variable were significantly different. By varying the size of the floating window where the ANCOVA was run, we were able to examine how scale affected the magnitude and spatial pattern of that variable. The percentages of total models run, at a given window size, that generated significantly different magnitudes (means) and patterns (slopes) in the dependent variable were referred to as the degree of dissimilarity. Plots of window size versus degree of dissimilarity elucidated temporal and spatial variability in water column parameters at a range of scales. The advantages of this new statistical method in relation to traditional spatial statistics are discussed.We demonstrated the efficacy of the floating window ANCOVA method by comparing chlorophyll and salinity transect data taken at the North Inlet, SC estuary during flooding and ebbing tides in Winter, Spring, and Summer 1991. Chlorophyll concentrations represented the biological characteristics of the estuarine water column and salinity represented the physical processes affecting that water column. We found total dissimilarity in the magnitude of salinity data from one season to the next at all scales, but inter-seasonal similarity in spatial patterns over both short (hourly) and long (monthly) time scales. We also found a large seasonal dissimilarity in the magnitude of chlorophyll levels, as expected. Spatial patterns in phytoplankton biomass (as chlorophyll concentrations) appeared to be largely controlled by the physical processes represented with the salinity data. Often, we observed greater dissimilarity in biological and physical parameters from one tide to the next [on a given day] than from one season to the next. In these cases, the greatest flood-ebb differences were associated with landscape-level influences - from rivers and the coastal ocean - that varied greatly with direction of tidal flow. We are currently using spatially articulate aquatic transect data and the floating window ANCOVA technique to validate spatial simulation models at different scales. By using this variable-scale statistical technique to determine coherence between the actual transect data and model output from simulations run at different scales, we will test hypotheses about the scale-dependent relationships between data resolution and model predictability in landscape analysis.     

Analysis of fine-scale spatial pattern of a grassland from remotely-sensed imagery and field collected data

An important practical problem in the analysis of spatial pattern in ecological systems is that requires spatially-intensive data, with both fine resolution and large extent. Such information is often difficult to obtain from field-measured variables. Digital imagery can offer a valuable, alternative source of information in the analysis of ecological pattern. In the present paper, we use remotely-sensed imagery to provide a link between field-based information and spatially-explicit modeling of ecological processes. We analyzed one digitized color infrared aerial photograph of a serpentine grassland to develop a detailed digital map of land cover categories (31.24 m × 50.04 m of extent and 135 mm of resolution), and an image of vegetation index (proportional to the amount of green biomass cover in the field). We conducted a variogram analysis of the spatial pattern of both field-measured (microtopography, soil depth) and image-derived (land cover map, vegetation index, gopher disturbance) landscape variables, and used a statistical simulation method to produce random realizations of the image of vegetation index based upon our characterization of its spatial structure. The analysis revealed strong relationships in the spatial distribution of the ecological variables (e.g., gopher mounds and perennial grasses are found primarily on deeper soils) and a non-fractal nested spatial pattern in the distribution of green biomass as measured by the vegetation index. The spatial pattern of the vegetation index was composed of three basic components: an exponential trend from 0 m to 4 m, which is related to local ecological processes, a linear trend at broader scales, which is related to a general change in topography across the study site, and a superimposed periodic structure, which is related to the regular spacing of deeper soils within the study site. Simulations of the image of vegetation index confirmed our interpretation of the variograms. The simulations also illustrated the limits of statistical analysis and interpolations based solely on the semivariogram, because they cannot adequately characterize spatial discontinuities.     

Hierarchical relationships between landscape structure and temperature in a managed forest landscape

Management may influence abiotic environments differently across time and spatial scale, greatly influencing perceptions of fragmentation of the landscape. It is vital to consider a priori the spatial scales that are most relevant to an investigation, and to reflect on the influence that scale may have on conclusions. While the importance of scale in understanding ecological patterns and processes has been widely recognized, few researchers have investigated how the relationships between pattern and process change across spatial and temporal scales. We used wavelet analysis to examine the multiscale structure of surface and soil temperature, measured every 5 m across a 3820 m transect within a national forest in northern Wisconsin. Temperature functioned as an indicator – or end product – of processes associated with energy budget dynamics, such as radiative inputs, evapotranspiration and convective losses across the landscape. We hoped to determine whether functional relationships between landscape structure and temperature could be generalized, by examining patterns and relationships at multiple spatial scales and time periods during the day. The pattern of temperature varied between surface and soil temperature and among daily time periods. Wavelet variances indicated that no single scale dominated the pattern in temperature at any time, though values were highest at finest scales and at midday. Using general linear models, we explained 38% to 60% of the variation in temperature along the transect. Broad categorical variables describing the vegetation patch in which a point was located and the closest vegetation patch of a different type (landscape context) were important in models of both surface and soil temperature across time periods. Variables associated with slope and microtopography were more commonly incorporated into models explaining variation in soil temperature, whereas variables associated with vegetation or ground cover explained more variation in surface temperature. We examined correlations between wavelet transforms of temperature and vegetation (i.e., structural) pattern to determine whether these associations occurred at predictable scales or were consistent across time. Correlations between transforms characteristically had two peaks; one at finer scales of 100 to 150 m and one at broader scales of ^>300 m. These scales differed among times of day and between surface and soil temperatures. Our results indicate that temperature structure is distinct from vegetation structure and is spatially and temporally dynamic. There did not appear to be any single scale at which it was more relevant to study temperature or this pattern-process relationship, although the strongest relationships between vegetation structure and temperature occurred within a predictable range of scales. Forest managers and conservation biologists must recognize the dynamic relationship between temperature and structure across landscapes and incorporate the landscape elements created by temperature-structure interactions into management decisions.     

A comparison of satellite data and landscape variables in predicting bird species occurrences in the Greater Yellowstone Ecosystem,USA

We compare the accuracy of predicting the occurrence of 11 bird species in montane meadows of the Greater Yellowstone National Park ecosystem, in the states of Montana and Wyoming, USA. We used remotely sensed, landscape, and habitat data. The meadow type, as determined from the remotely sensed data, was highly correlated with abundances of six of the 11 bird species. Landscape variables significant in predicting occurrence were selected using a stepwise multiple regression for each bird species. These variables were then used in a multiple regression with the variable meadow type. As expected, the abundances of the generalist species (American Robin, Dark-eyed Junco, White-crowned Sparrow, Brewer's Blackbird, and Chipping Sparrow) were not strongly correlated with landscape variables or meadow type. Conversely, abundances of the Common Snipe, Common Yellowthroat, Lincoln's Sparrow, Savannah Sparrow, Vesper Sparrow, and Yellow Warbler were highly correlated with meadow type and landscape variables such as percent cover of willow (Salix spp.), graminoid, woody vegetation, sagebrush (Artemisia spp.), and graminoid and shrub biomass. The results from our study indicate that remotely sensed data are applicable for estimating potential habitats for bird species in the different types of montane meadows. However, to improve predictions about species in specific sites or areas, we recommend the use of additional landscape metrics and habitat data collected in the field.     

Predicting carnivore occurrence with noninvasive surveys and occupancy modeling

Terrestrial carnivores typically have large home ranges and exist at low population densities, thus presenting challenges to wildlife researchers. We employed multiple, noninvasive survey methods—scat detection dogs, remote cameras, and hair snares—to collect detection–nondetection data for elusive American black bears (Ursus americanus), fishers (Martes pennanti), and bobcats (Lynx rufus) throughout the rugged Vermont landscape. We analyzed these data using occupancy modeling that explicitly incorporated detectability as well as habitat and landscape variables. For black bears, percentage of forested land within 5 km of survey sites was an important positive predictor of occupancy, and percentage of human developed land within 5 km was a negative predictor. Although the relationship was less clear for bobcats, occupancy appeared positively related to the percentage of both mixed forest and forested wetland habitat within 1 km of survey sites. The relationship between specific covariates and fisher occupancy was unclear, with no specific habitat or landscape variables directly related to occupancy. For all species, we used model averaging to predict occurrence across the study area. Receiver operating characteristic (ROC) analyses of our black bear and fisher models suggested that occupancy modeling efforts with data from noninvasive surveys could be useful for carnivore conservation and management, as they provide insights into habitat use at the regional and landscape scale without requiring capture or direct observation of study species.     

Multi-scale effects of landscape complexity and crop management on pollen beetle parasitism rate

Improving our understanding about how natural enemies respond to semi-natural habitats and crop management scattered in the landscape may contribute to the development of ecologically based pest management strategies maximising biological control services. We investigated how soil tillage and semi-natural habitats influenced the parasitism rates of pollen beetle (Meligethes aeneus F.) larvae at 8 different spatial scales (from 250 to 2000 m radius circular sectors) in 42 oilseed rape (OSR) fields. We used multimodel inference approaches to identify and rank the influence of soil tillage and semi-natural habitats on parasitism rates, and to quantify the importance of each scale. Parasitism rates were due to three univoltine parasitoid species (Tersilochus heterocerus, Phradis morionellus and P. interstitialis) and varied from 0 to 98%. We found that both fine and large scales contributed to explain significantly parasitism rates, indicating that biological control of pollen beetle is a multi-scale process. At the 250 m scale, parasitism rates of T. heterocerus were positively related to the proportion of semi-natural habitats and the proximity to previous year OSR fields. At large scales (1500 to 2000 m), parasitism rates of T. heterocerus were positively related to semi-natural habitats and negatively related to the proportion of previous year OSR fields with conventional soil tillage. Parasitism rates of Phradis spp. were only positively related to the proportion of semi-natural habitats at the 1250 and 1500 m scales. These multi-scale effects are discussed in relation to the influence of semi-natural habitats and soil tillage on parasitoid populations and their movement behaviours within the landscape.     

An invasive species’ relationship with environmental variables changes across multiple spatial scales

To further our understanding of invasive species?? novel distributions, knowledge of invasive species?? relationships with environmental variables at multiple spatial scales is paramount. Here, we investigate which environmental variables and which spatial scales best explain the invasive mute swan??s (Cygnus olor) distribution in southern Ontario (Canada). Specifically we model mute swan distribution changes according to ecologically-relevant spatial scales: average territory size radius, 140?m; median dispersal distance of cygnets, 3,000?m; and average activity distance of males, 8,000?m. For individual spatial scales, global models using variables measured at each particular scale result in the highest Akaike weights, AUC, and Cohen??s Kappa values. Yet composite models (models combining variables measured at different scales) elicit the best models, as determined by higher Akaike weights and high AUC and Cohen??s Kappa values. Overall, percent water, waterbody perimeter density, temperature, precipitation, and road density are positively correlated with mute swan distribution, while percent forest and elevation are negatively correlated at all scales of analysis. Only percent water and annual precipitation are more influential in determining mute swan distribution at the 3,000 and 8,000?m zone scales than the territory scale. While most species distribution models are performed at a single scale, the results of our study suggest that composite models reflecting a species?? ecological needs provide models of better fit with similar, if not better, predictive accuracy. When analyzing species distributions, we also recommend that ecologists consider the scale of the underlying landscape processes and the effect that this may have on their modelling outcomes.     

Effects of changing spatial scale on acoustic observations of patchiness in the Gulf Stream

We examine the influence of spatial scale on studies of nektonic patchiness at the north edge of the Gulf Stream by altering the grain size of acoustic cross sections and applying a patch-finding algorithm. From original pictures of 180 pixels deep by 540–1260 pixels long, we averaged depth and length, to give 9 scales ranging from fine grain (1 m vertical × 25 m horizontal sized pixels) to coarse grain (12 m × 300 m sized pixels). Measures of overall echo intensity within patches were the most predictable and showed little change with scale. Measures of variability of the echo within patches showed simple negative trends with scale and were best measured at fine spatial scales. Patch size and shape related variables have a more complex relationship with scale with differences between day and night transects more pronounced at intermediate scales. This suggests behavioral patch forming mechanisms within the nekton occur at a specific grain size (4 m vertical × 100 m horizontal) within the Gulf Stream front.     

Biodiversity is associated with indicators of soil ecosystem functions over a landscape gradient of agricultural intensification

Agricultural intensification has led to dramatic losses in biodiversity over the past several decades. Many studies have shown the effects of intensification on vegetation or soil communities at field or local scales. However, the functional significance of biodiversity may only appear at larger spatial and temporal scales, due to exchanges among local ecosystems throughout a landscape. To examine how patterns of biodiversity loss are reflected at larger spatial scales, plant and soil biodiversity and associated indicators of ecosystem functions were assessed in riparian areas over a 150 km² agricultural landscape in the Sacramento Valley of California. Publicly-available GIS data were first used to classify and select sites over the range of soils, topography and plant community types. Representative sites from the landscape were sampled for soil physiochemical properties, as well as microbial, nematode, and plant communities. Higher agricultural intensification, based on field and landscape indices, was negatively correlated with richness and diversity of plant and soil taxa, and was related to indicators of ecosystem functions, such as increased soil nitrate and phosphorus loading, decreased riparian health ratings, and lower soil carbon, soil microbial biomass and soil food web structure. Both field- and landscape-scale factors played important roles in the measured losses. The study area was composed of a wide array of soils, vegetation, and land management, indicating that the observed trends transcended site-specific conditions.     

Does the scale of our observational window affect our conclusions about correlations between endangered salmon populations and their habitat?

Differences in the strength of species-habitat relationships across scales provide insights into the mechanisms that drive these relationships and guidance for designing in situ monitoring programs, conservation efforts and mechanistic studies. The scale of our observation can also impact the strength of perceived relationships between animals and habitat conditions. We examined the relationship between geographic information system (GIS)-based landscape data and Endangered Species Act-listed anadromous Pacific salmon (Oncorhynchus spp.) populations in three subbasins of the Columbia River basin, USA. We characterized the landscape data and ran our models at three spatial scales: local (stream reach), intermediate (6th field hydrologic units directly in contact with a given reach) and catchment (entire drainage basin). We addressed three questions about the effect of scale on relationships between salmon and GIS representations of landscape conditions: (1) at which scale does each predictor best correlate with salmon redd density, (2) at which scale is overall model fit maximized, and (3) how does a mixed-scale model compare with single scale models (mixed-scale meaning models that contain variables characterized at different spatial scales)? We developed mixed models to identify relationships between redd density and candidate explanatory variables at each of these spatial scales. Predictor variables had the strongest relationships with redd density when they were summarized over the catchment scale. Meanwhile strong models could be developed using landscape variables summarized at only the local scale. Model performance did not improve when we used suites of potential predictors summarized over multiple scales. Relationships between species abundance and land use or intrinsic habitat suitability detected at one scale cannot necessarily be extrapolated to other scales. Therefore, habitat restoration efforts should take place in the context of conditions found in the associated watershed or landscape.     

Nest site use by crested ibis: dependence of a multifactor model on spatial scale

The crested ibis (Nipponia nippon), a species at the brink of extinction in 1981, remain restricted to a small (25 km radius) area of temperate forests in central China. To improve the chances of successful reintroduction into new areas we developed a multifactor logistic regression model of habitat association at multiple scales. Using habitat variables, i.e. vegetation, human impact, elevation, and wetland, we compared occupied and unoccupied sites at grain sizes ranging from 1 to 6400 ha. The goodness-of-fit of the habitat suitability model depended on grain size, with the best fit (most information) at a grain size of 2 ha. Semivariograms showed the habitat variables at control sites have a gradient pattern, yet the crested ibis had their specific habitat preferences, and only selected a narrow range from the available gradient. Our results indicated that spatial scale needs to be considered in developing habitat models for applications such as conservation planning.     

Large-scale effects on the spatial distribution of seabirds in the Northwest Atlantic

Scale questions are particularly important for organisms which range over large areas, as pelagic seabirds do. The investigations of scale are of practical importance for describing patch size of predator and prey, determining the appropriate scale of study and correcting survey transects. We conducted this study in order to explore a substantially wider diversity of spatial scales than has previously been attempted in the pelagic bird literature. As an example of large monitoring datasets dealing with seabirds, we use the PIROP (Programme intégré pour le recherche des oiseaux pélagiques) data set to investigate relevant large scale issues for these species in the Northwestern Atlantic. We analyzed autocorrelation within selected winter and summer transects, and for 1 degree analysis units (‘bins’) for data collected June–August 1966–1992. We also investigated effects of the analysis unit on counting results and on the links between seabirds and their environment (depth, sea surface salinity and temperature). We selected scales of 1, 2, 5 and 10 degrees analysis units; an ecological mapping scale (‘Banks’ not deeper than 200 m) and a political scale (management convention zones of the North Atlantic Fisheries Organization, NAFO) were also included. Using ‘binning’ of various scales, our results show that the Coefficient of Variation for seabird abundances varies among aggregation scales, and that seabird associations with their environment can show scale effects. Autocorrelation of analysis units indicated some distinct larger scale patch sizes for particular species during the breeding season.     

Effects of spatial habitat heterogeneity on habitat selection and annual fecundity for a migratory forest songbird

Understanding how spatial habitat patterns influence abundance and dynamics of animal populations is a primary goal in landscape ecology. We used an information-theoretic approach to investigate the association between habitat patterns at multiple spatial scales and demographic patterns for black-throated blue warblers (Dendroica caerulescens) at 20 study sites in west-central Vermont, USA from 2002 to 2005. Sites were characterized by: (1) territory-scale shrub density, (2) patch-scale shrub density occurring within 25 ha of territories, and (3) landscape-scale habitat patterns occurring within 5 km radius extents of territories. We considered multiple population parameters including abundance, age ratios, and annual fecundity. Territory-scale shrub density was most important for determining abundance and age ratios, but landscape-scale habitat structure strongly influenced reproductive output. Sites with higher territory-scale shrub density had higher abundance, and were more likely to be occupied by older, more experienced individuals compared to sites with lower shrub density. However, annual fecundity was higher on sites located in contiguously forested landscapes where shrub density was lower than the fragmented sites. Further, effects of habitat pattern at one spatial scale depended on habitat conditions at different scales. For example, abundance increased with increasing territory-scale shrub density, but this effect was much stronger in fragmented landscapes than in contiguously forested landscapes. These results suggest that habitat pattern at different spatial scales affect demographic parameters in different ways, and that effects of habitat patterns at one spatial scale depends on habitat conditions at other scales.