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.     

The protean relationship between boreal forest landscape structure and red squirrel distribution at multiple spatial scales

This paper investigates two fundamental questions in landscape ecology: what influence does landscape context, or the composition of the matrix, have on an animals’ response to landscape structure, and how does this relationship extrapolate between landscapes? We investigate how the distribution of North American red squirrels (Tamiasciurus hudsonicus) in the boreal mixedwood forest is influenced by anthropogenically (forest harvest) and naturally (forest fire) derived landscape structure. We studied the presence and absence of red squirrels over two years in three landscape types: one managed for timber harvest, one recently burned by wildfire, and a third unburned unmanaged landscape. Landscape composition and configuration, measured at several spatial scales, predicted red squirrel’s distribution in all three landscapes, but the significant landscape variables changed across spatial scales, across time, and across landscapes. These findings emphasize the variability in landscape structure/animal distribution relationships, and enforce the need to link pattern-finding studies, such as this one, with searches for the mechanisms behind the observed pattern.     

Spatial and temporal analysis of landscape patterns

A variety of ecological questions now require the study of large regions and the understanding of spatial heterogeneity. Methods for spatial-temporal analyses are becoming increasingly important for ecological studies. A grid cell based spatial analysis program (SPAN) is described and results of landscape pattern analysis using SPAN are presentedd. Several ecological topics in which geographic information systems (GIS) can play an important role (landscape pattern analysis, neutral models of pattern and process, and extrapolation across spatial scales) are reviewed. To study the relationship between observed landscape patterns and ecological processes, a neutral model approach is recommended. For example, the expected pattern (i.e., neutral model) of the spread of disturbance across a landscape can be generated and then tested using actual landscape data that are stored in a GIS. Observed spatial or temporal patterns in ecological data may also be influenced by scale. Creating a spatial data base frequently requires integrating data at different scales. Spatial is shown to influence landscape pattern analyses, but extrapolation of data across spatial scales may be possible if the grain and extent of the data are specified. The continued development and testing of new methods for spatial-temporal analysis will contribute to a general understanding of landscape dynamics.     

Agricultural soil redistribution and landscape complexity

A number of hypotheses and conceptual models, particularly those emphasizing nonlinear dynamics and self-organization, postulate increases or decreases in complexity in the evolution of drainage basins, topography, soils, ecosystems, and other earth surface systems. Accordingly, it is important to determine under what circumstances and at what scales either trend might occur. This paper is concerned with changes in soil landscape complexity due to redistribution of sediment by fluvial, aeolian, and tillage processes at historical time scales in an agricultural field system near Grifton, North Carolina. Soil mapping and soil stratigraphic investigations were used to identify and map soil changes associated with erosion and deposition by water, wind, and tillage; reconstruct the pre-agricultural soil pattern; and identify transformations between soil types. The Kolmogorov entropy of the pre- and post- agricultural landscapes was then compared. The soil transformations associated with erosion and deposition created four distinct new soils and made possible new transformations among soil series, increasing the number of soil types from seven to 11 and the number of possible transformations from 14 to 22. However, the entropy and complexity of the soil landscape decreased, with associated increases in information and redundancy. The mass redistributions created a lower-entropy landscape by concentrating particular soils and soil transformations in specific landscape settings. This result is contrary to studies showing a trend toward increasing pedological complexity at comparable spatial scales, but over much longer time scales. These results point to the importance of temporal scale, and to the fact that environmental complexity is influenced by factors other than the number of different landscape units present.     

Influences of scale on bat habitat relationships in a forested landscape in Nicaragua

Context

Scale dependence of bat habitat selection is poorly known with few studies evaluating relationships among landscape metrics such as class versus landscape, or metrics that measure composition or configuration. This knowledge can inform conservation approaches to mitigate habitat loss and fragmentation.

Objectives

We evaluated scale dependence of habitat associations and scaling patterns of landscape metrics in relation to bat occurrence or capture rate in forests of southwestern Nicaragua.

Methods

We captured 1537 bats at 35 locations and measured landscape and class metrics across 10 spatial scales (100–1000 m) surrounding capture locations. We conducted univariate scaling across the 10 scales and identified scales and variables most related to bat occurrence or capture rate.

Results

Edge and patch density, at both landscape and class levels, were the most important variables across species. Feeding guilds varied in their response to metrics. Certain landscape and configuration metrics were most influential at fine (100 m) and/or broad (1000 m) spatial scales while most class and composition metrics were influential at intermediate scales.

Conclusions

These results provide insight into the scale dependence of habitat associations of bat species and the influence of fine and broad scales on habitat associations. The effects of scale, examined in our study and others from fine (100 m) to broad (5 km) indicate habitat relationships for bats may be more informative at larger scales. Our results suggest there could be general differences in scale relationships for different groups of landscape metrics, which deserves further evaluation in other taxonomic groups.

     

Surface metrics for landscape ecology: a comparison of landscape models across ecoregions and scales

Context

The patch-mosaic model is lauded for its conceptual simplicity and ease with which conventional landscape metrics can be computed from categorical maps, yet many argue it is inconsistent with ecological theory. Gradient surface models (GSMs) are an alternative for representing landscapes, but adoption of surface metrics for analyzing spatial patterns in GSMs is hindered by several factors including a lack of meaningful interpretations.

Objectives

We investigate the performance and applicability of surface metrics across a range of ecoregions and scales to strengthen theoretical foundations for their adoption in landscape ecology.

Methods

We examine metric clustering across scales and ecoregions, test correlations with patch-based metrics, and provide ecological interpretations for a variety of surface metrics with respect to forest cover to support the basis for selecting surface metrics for ecological analyses.

Results

We identify several factors complicating the interpretation of surface metrics from a landscape perspective. First, not all surface metrics are appropriate for landscape analyses. Second, true analogs between surface metrics and patch-based, landscape metrics are rare. Researchers should focus instead on how surface measures can uniquely measure spatial patterns. Lastly, scale dependencies exist for surface metrics, but relationships between metrics do not appear to change considerably with scale.

Conclusions

Incorporating gradient surfaces into landscape ecological analyses is challenging, and many surface metrics may not have patch analogs or be ecologically relevant. For this reason, surface metrics should be considered in terms of the set of pattern elements they represent that can then be linked to landscape characteristics.

     

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.     

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.     

Effects of landscape structure on nest predation in roadsides of a midwestern agroecosystem: a multiscale analysis

Nest predation is an important cause of mortality for many bird species, especially in grassland ecosystems where generalist predators have responded positively to human disturbance and landscape fragmentation. Our study evaluated the influence of the composition and configuration of the surrounding landscape on nest predation. Transects consisting of 10 artificial ground nests each were set up in 136 roadsides in six watersheds in south-central Iowa. Nest predation on individual roadside transects ranged from 0 to 100% and averaged 23%. The relationship of landscape structure within spatially-nested landscapes surrounding each roadside transect (within 200, 400, 800, 1200, and 1600 m of the transect line) to nest predation was evaluated by using multiple regression and canonical correlation analyses. The results of this multiscale landscape analysis demonstrated that predation on ground nests was affected by the surrounding landscape mosaic and that nest predators with different-sized home ranges and habitat affinities responded to landscapes in different ways. In general, wooded habitats were associated with greater nest predation, whereas herbaceous habitats (except alfalfa/pasture) either were associated with less nest predation or were not important. Different landscape variables were important at different spatial scales. Whereas some block-cover habitats such as woodland were important at all scales, others such as rowcrops and alfalfa/pasture were important at large scales. Some strip-cover habitats such as gravel roads and paved roads were important at small scales, but others such as wooded roadsides were important at all all scales. Most landscape metrics (e.g., mean patch size and edge density) were important at large scales. Our study demonstrated that the relationships between landscape structure and predator assemblages are complex, thus making efforts to enhance avian productivity in agricultural landscapes a difficult management goal.     

Scaling patterns of biomass and soil properties: an empirical analysis

We argue that studies at multiple scales must necessarilychange the extent of measurements, not just the spacing, in order toeffectivelycapture information regarding processes at multiple scales. We have implementeda multi-scale sampling scheme using transects of 10 cm, 1m, 10 m, 100 m, and 1 km ateach of four sites along an elevational gradient from dry foothills forest toalpine tundra in the Front Range of Colorado; these four sites form anadditional transect of 22 km. Along each of these transects wetookten equally spaced soil cores and measured variables important in determiningboth microbial and plant community structure: soil water content, organicmattercontent, pH, and total soil biomass. With this sampling scheme we are able totreat scale as an independent variable in our analyses, and our data show thatboth particular sites and particular variables can determine whether estimatesof mean values are scale-dependent or not. A geostatistical analysis using allof our data shows common relationships between scales across ecologicallydiverse sites; biomass shows the most complex pattern of distribution acrossscales, as measured by fractal dimension. Our analyses also reveal theinadequacy of several standard geostatistical models when applied to data frommultiple scales of measurement – we recommend the use of the boundedpowerlaw model in such cases. We hypothesize that because biological communitiesmustrespond simultaneously to multiple variables with differing patterns of spatialvariation, the spatial variation of biological communities will be at least ascomplex as the most complex environmental variable at any given site.This revised version was published online in May 2005 with corrections to the Cover Date.     

Habitat factors related to wild rabbit conservation in an agricultural landscape

Populations of European wild rabbit (Oryctolagus cuniculus) have been decreasing since the 1950s. Changes in agricultural practices have been suggested as reasons for their decline in Mediterranean landscapes. We evaluated the environmental variables affecting rabbit distribution in a semiarid agricultural landscape of Northeastern Spain. Sampling was performed in 147 sites randomly distributed across Zaragoza province. At each site, data were recorded in five 100 m segments along a 1 km transect, following ecotones between crops and natural-vegetation areas. A rabbit abundance index was estimated from latrine count, pellet density and number of plots with pellets. In addition to environmental variables that have been shown to be related to rabbit abundance in other habitats, as climate, soil hardness and topography of the site, we measured landscape components related to agricultural use, such as structure of natural vegetation in remaining areas non-devoted to agricultural use and distances to different types of crops and to ecotone between crop and natural vegetation. Our results showed that rabbit abundance was positively correlated to yearly mean temperature, February and May mean rainfall, and negatively correlated to September and November mean rainfall, hardness of soil, and site topography. In relation to agricultural use, rabbit abundance was positively correlated to the scrub structure of natural-vegetation areas and negatively correlated to distance to edge between cultivated unirrigated cereal crops (wheat or barley) and yearly resting cereal crops. Rabbit abundance increased only when the edge between alternate cereal crops was less than 50 m from the ecotone between crops and natural vegetation.This revised version was published online in May 2005 with corrections to the Cover Date.     

Use of landscape pattern metrics and multiscale data in aquatic species distribution models: a case study of a freshwater mussel

The distributions of freshwater mussels are controlled by landscape factors operating at multiple spatial scales. Changes in land use/land cover (LULC) have been implicated in severe population declines and range contractions of freshwater mussels across North America. Despite widespread recognition of multiscale influences few studies have addressed these issues when developing distribution models. Furthermore, most studies have disregarded the role of landscape pattern in regulating aquatic species distributions, focusing only on landscape composition. In this study, the distribution of Rabbitsfoot (Quadrula cylindrica) in the upper Green River system (Ohio River drainage) is modeled with environmental variables from multiple scales: subcatchment, riparian buffer, and reach buffer. Four types of landscape environment metrics are used, including: LULC pattern, LULC composition, soil composition, and geology composition. The study shows that LULC pattern metrics are very useful in modeling the distribution of Rabbitsfoot. Together with LULC compositional metrics, pattern metrics permit a more detailed analysis of functional linkages between aquatic species distributions and landscape structure. Moreover, the inclusion of multiple spatial scales is necessary to accurately model the hierarchical processes in stream systems. Geomorphic features play important roles in regulating species distributions at intermediate and large scales while LULC variables appear more influential at proximal scales.     

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.     

Selection of scale for Everglades landscape models

This article addresses the problem of determining the optimal “Model Grain” or spatial resolution (scale) for landscape modeling in the Everglades. Selecting an appropriate scale for landscape modeling is a critical task that is necessary before using spatial data for model development. How the landscape is viewed in a simulation model is dependent on the scale (cell size) in which it is created. Given that different processes usually have different rates of fluctuations (frequencies), the question of selection of an appropriate modeling scale is a difficult one and most relevant to developing spatial ecosystem models. The question of choosing the appropriate scale for modeling is addressed using the landscape indices (e.g., cover fraction, diversity index, fractal dimension, and transition probabilities) recently developed for quantifying overall characteristics of spatial patterns. A vegetation map of an Everglades impoundment area developed from SPOT satellite data was used in the analyses. The data from this original 20 × 20 m data set was spatially aggregated to a 40 × 40 m resolution and incremented by 40 meters on up to 1000 × 1000 m (i.e., 40, 80, 120, 160 … 1000) scale. The primary focus was on the loss of information and the variation of spatial indices as a function of broadening “Model Grain” or scale. Cover fraction and diversity indices with broadening scale indicate important features, such as tree islands and brush mixture communities in the landscape, nearly disappear at or beyond the 700 m scale. The fractal analyses indicate that the area perimeter relationship changes quite rapidly after about 100 m scale. These results and others reported in the paper should be useful for setting appropriate objectives and expectations for Everglades landscape models built to varying spatial scales.     

Evidence of Hierarchical Patch Dynamics in an East African savanna?

The Hierarchical Patch Dynamics Paradigm provides a conceptual framework for linking pattern, process and scale in ecosystems, but there have been few attempts to test this theory because most ecological studies focus on only one spatial scale, or are limited in their temporal scope. Here I use palaeoecological techniques (analysis of fossil pollen and stable carbon isotopes) to compare vegetation heterogeneity in an east African savanna at three spatial scales, over hundreds of years. The data show that patterns of vegetation change are different at the three spatial scales of observation, and suggest that different ecological processes dominate tree abundance at micro, local and landscape scales. Interactions between plants, disturbance (e.g., by fire and herbivores), climate and soil type may influence tree density at differing spatial and temporal scales. This hierarchical explanation of savanna vegetation dynamics could inform future biodiversity conservation and management in savannas. This revised version was published online in July 2006 with corrections to the Cover Date.     

Evidence of Hierarchical Patch Dynamics in an East African savanna?

The Hierarchical Patch Dynamics Paradigm provides a conceptual framework for linking pattern, process and scale in ecosystems, but there have been few attempts to test this theory because most ecological studies focus on only one spatial scale, or are limited in their temporal scope. Here I use palaeoecological techniques (analysis of fossil pollen and stable carbon isotopes) to compare vegetation heterogeneity in an east African savanna at three spatial scales, over hundreds of years. The data show that patterns of vegetation change are different at the three spatial scales of observation, and suggest that different ecological processes dominate tree abundance at micro, local and landscape scales. Interactions between plants, disturbance (e.g., by fire and herbivores), climate and soil type may influence tree density at differing spatial and temporal scales. This hierarchical explanation of savanna vegetation dynamics could inform future biodiversity conservation and management in savannas. This revised version was published online in July 2006 with corrections to the Cover Date.     

Effects of changing spatial resolution on the results of landscape pattern analysis using spatial autocorrelation indices

Understanding the relationship between pattern and scale is a central issue in landscape ecology. Pattern analysis is necessarily a critical step to achieve this understanding. Pattern and scale are inseparable in theory and in reality. Pattern occurs on different scales, and scale affects pattern to be observed. The objective of our study is to investigate how changing scale might affect the results of landscape pattern analysis using three commonly adopted spatial autocorrelation indices,i.e., Moran Coefficient, Geary Ratio, and Cliff-Ord statistic. The data sets used in this study are spatially referenced digital data sets of topography and biomass in 1972 of Peninsular Malaysia. Our results show that all three autocorrelation indices were scale-dependent. In other words, the degree of spatial autocorrelation measured by these indices vary with the spatial scale on which analysis was performed. While all the data sets show a positive spatial autocorrelation across a range of scales, Moran coefficient and Cliff-Ord statistic decrease and Geary Ratio increases with increasing grain size, indicating an overall decline in the degree of spatial autocorrelation with scale. The effect of changing scale varies in their magnitude and rate of change when different types of landscape data are used. We have also explored why this could happen by examining the formulation of the Moran coefficient. The pattern of change in spatial autocorrelation with scale exhibits threshold behavior,i.e., scale effects fade away after certain spatial scales are reached (for elevation). We recommend that multiple methods be used for pattern analysis whenever feasible, and that scale effects must be taken into account in all spatial analysis.     

Scaling properties in landscape patterns: New Zealand experience

In this paper we present a case study of spatial structure in landscape patterns for the North and South Islands of New Zealand. The aim was to characterise quantitatively landscape heterogeneity and investigate its possible scaling properties. The study examines spatial heterogeneity, in particular patchiness, at a range of spatial scales, to help build understanding on the effects of landscape heterogeneity on water movement in particular, and landscape ecology in general.We used spatial information on various landscape properties (soils, hydrogeology, vegetation, topography) generated from the New Zealand Land Resource Inventory. To analyse this data set we applied various methods of fractal analyses following the hypothesis that patchiness in selected landscape properties demonstrates fractal scaling behaviour at two structural levels: (1) individual patches; and (2) mosaics (sets) of patches.Individual patches revealed scaling behaviour for both patch shape and boundary. We found self-affinity in patch shape with Hurst exponent H from 0.75 to 0.95. We also showed that patch boundaries in most cases were self-similar and in a few cases of large patches were self-affine. The degree of self-affinity was lower for finer patches. Similarly, when patch scale decreases the orientation of patches tends to be uniformly distributed, though patch orientation on average is clearly correlated with broad scale geological structures. These results reflect a tendency to isotropic behaviour of individual patches from broad to finer scales. Mosaics of patches also revealed fractal scaling in the total patch boundaries, patch centers of mass, and in patch area distribution. All these reflect a special organisation in patchiness represented in fractal patch clustering. General relationships which interconnect fractal scaling exponents were derived and tested. These relationships show how scaling properties of individual patches affect those for mosaics of patches and vice-versa. To explain similarity in scaling behaviour in patchiness of different types we suggest that the Self-Organised Criticality concept should be used. Also, potential applications of our results in landscape ecology are discussed, especially in relation to improved neutral landscape models.     

Multi-scale study of bird species distribution and of their response to vegetation change: a Mediterranean example

Land use changes operate at different scales. They trigger a cascade of effects that simultaneously modify the composition or structure of the landscape and of the local vegetation. Mobil animals, and birds in particular, can respond quickly to such multi-scalar changes. We took advantage of a long term study on the response of songbirds to land-use changes on four Mediterranean islands in Corsica and Sardinia to explore the benefits of a multi-scale analysis of the relationships between songbird distribution, vegetation structure and landscape dynamics. Field data and aerial photographs were used to describe the vegetation at three different scales. Birds were censused by point counts. We used statistical variance decomposition to study how bird distribution and vegetation at various scales were linked. We analysed multi-scale vegetation changes (floristic composition, plot vegetation type, and landscape structure) and their consequences on bird distribution with multivariate and non-parametrical tests. The distribution of most species was linked to at least two spatial scales. The weight of a given scale was consistent with life-history traits for species whose biology was well-known. In the examples studied, vegetation composition, vegetation type and landscape changes that resulted from land abandonment negatively affected birds depending on open or heterogeneous areas. Our results emphasize that multi-scale analyses can greatly enhance our understanding of bird distribution and of their changes. Management of these populations should take into account measures at various spatial scales depending on the sensitivity of the species.