Modelling relationships between birds and vegetation structure using airborne LiDAR data: a review with case studies from agricultural and woodland environments

Airborne LiDAR (Light Detection and Ranging) is a remote sensing technology that offers the ability to collect high horizontal sampling densities of high vertical resolution vegetation height data, over larger spatial extents than could be obtained by field survey. The influence of vegetation structure on the bird is a key mechanism underlying bird–habitat models. However, manual survey of vegetation structure becomes prohibitive in terms of time and cost if sampling needs to be of sufficient density to incorporate fine-grained heterogeneity at a landscape extent. We show that LiDAR data can help bridge the gap between grain and extent in organism–habitat models. Two examples are provided of bird–habitat models that use structural habitat information derived from airborne LiDAR data. First, it is shown that data on crop and field boundary height can be derived from LiDAR data, and so have the potential to predict the distribution of breeding Sky Larks in a farmed landscape. Secondly, LiDAR-retrieved canopy height and structural data are used to predict the breeding success of Great Tits and Blue Tits in broad-leaved woodland. LiDAR thus offers great potential for parameterizing predictive bird–habitat association models. This could be enhanced by the combination of LiDAR data with multispectral remote sensing data, which enables a wider range of habitat information to be derived, including both structural and compositional characteristics.     

Occupancy of red‐naped sapsuckers in a coniferous forest: using LiDAR to understand effects of vegetation structure and disturbance

Red‐naped sapsuckers (Sphyrapicus nuchalis) are functionally important because they create sapwells and cavities that other species use for food and nesting. Red‐naped sapsucker ecology within aspen (Populus tremuloides) has been well studied, but relatively little is known about red‐naped sapsuckers in conifer forests. We used light detection and ranging (LiDAR) data to examine occupancy patterns of red‐naped sapsuckers in a conifer‐dominated system. We surveyed for sapsuckers at 162 sites in northern Idaho, USA, during 2009 and 2010. We used occupancy models and an information‐theoretic approach to model sapsucker occupancy as a function of four LiDAR‐based metrics that characterized vegetation structure and tree harvest, and one non‐LiDAR metric that characterized distance to major roads. We evaluated model support across a range of territory sizes using Akaike's information criterion. Top model support was highest at the 4‐ha extent, which suggested that 4 ha was the most relevant scale describing sapsucker occupancy. Sapsuckers were positively associated with variation of canopy height and harvested area, and negatively associated with shrub and large tree density. These results suggest that harvest regimes and structural diversity of vegetation at moderate extents (e.g., 4 ha) largely influence occurrence of red‐naped sapsuckers in conifer forests. Given the current and projected declines of aspen populations, it will be increasingly important to assess habitat relationships, as well as demographic characteristics, of aspen‐associated species such as red‐naped sapsuckers within conifer‐dominated systems to meet future management and conservation goals.     

Willow Warbler Phylloscopus trochilus habitat in woods with different structure and management in southern England

Capsule Use of Light Detection and Ranging (LiDAR) data identified suitable Willow Warbler habitat based on mean vegetation height. This habitat model provided maps of distribution and occupation of suitable habitat.

Aims To identify habitat associations in woods with different vegetation structure and management systems during a period of low Willow Warbler populations.

Methods Locations of all Willow Warblers were mapped during the breeding season in three woods of contrasting management; recent low intervention, actively coppiced woodland and high forest with clear‐fells. Height profile models of each wood were derived from airborne LiDAR. The mean vegetation height at locations with Willow Warblers and a sample from the rest of the wood were used to produce models of optimum habitat and breadth of habitat occupied in each wood. The habitat model was then used to produce maps of suitable habitat.

Results The habitat models did not differ between woods, with highest probability of Willow Warbler occurrence in mean vegetation heights of 3.7–5.3 m. Habitat of heights 6–11 m appeared less suitable, being only partly occupied. Habitat maps showed that habitat of suitable height was only occupied when it occurred as large patches; smaller patches (mostly <0.5 ha) and edges along rides and fields were not used.

Conclusion The use of LiDAR derived measures of vegetation height identified areas of suitable habitat for Willow Warblers. Willow Warblers occupied areas of low mean vegetation height either as early successional or open canopy woodland in all woods. Height‐based habitat maps can identify areas of suitable habitat within larger expanses of heterogeneous woodland and are a potentially useful tool in assessing changes in extent of what are often temporary patches of habitat.     

Identifying Core Areas in a Species’ Range Using Temporal Suitability Analysis: an Example Using Little Bustards <Emphasis Type="Italic">Tetrax Tetrax</Emphasis> L. in Spain

Variations in habitat quality impact on breeding success, leading to strong selection pressure for the best sites to be occupied first during a population increase and last during a decline. Coupled with dispersal and metapopulation processes, the result is that snapshot surveys of wildlife distributions may fail to reveal core areas that conservation seeks to protect. At a local scale, territory occupancy is a good indicator of quality but data are not readily available to assess occupancy for rarer species, in remote areas, and over large spatial extents. We introduce temporal suitability analysis as a way to generate an analogue of occupancy from a single survey and illustrate it using data on the little bustard in Spain. We first used Generalised Additive Modelling (GAM) to build a predictive distribution model using Geographic Information System (GIS) coverages and satellite imagery, and then applied the model retrospectively to a time series of satellite images to produce one distribution map for each year. These annual maps differed in the extent of Spain predicted as suitable for little bustards. By overlaying the maps, we identified areas predicted as suitable in one to n years. We show that this temporal suitability map correlates with a conventional habitat suitability map based on a single year but contains extra information on hierarchical use of habitats and the lag between suitability and use. The technique may be applied at a variety of spatial scales to reveal changes in expected occupancy as land use or external factors determining land cover types vary over time.     

Vegetation structure derived from airborne laser scanning to assess species distribution and habitat suitability: The way forward

Ecosystem structure, especially vertical vegetation structure, is one of the six essential biodiversity variable classes and is an important aspect of habitat heterogeneity, affecting species distributions and diversity by providing shelter, foraging, and nesting sites. Point clouds from airborne laser scanning (ALS) can be used to derive such detailed information on vegetation structure. However, public agencies usually only provide digital elevation models, which do not provide information on vertical vegetation structure. Calculating vertical structure variables from ALS point clouds requires extensive data processing and remote sensing skills that most ecologists do not have. However, such information on vegetation structure is extremely valuable for many analyses of habitat use and species distribution. We here propose 10 variables that should be easily accessible to researchers and stakeholders through national data portals. In addition, we argue for a consistent selection of variables and their systematic testing, which would allow for continuous improvement of such a list to keep it up-to-date with the latest evidence. This initiative is particularly needed not only to advance ecological and biodiversity research by providing valuable open datasets but also to guide potential users in the face of increasing availability of global vegetation structure products.     

Laserfarm – A high-throughput workflow for generating geospatial data products of ecosystem structure from airborne laser scanning point clouds

Quantifying ecosystem structure is of key importance for ecology, conservation, restoration, and biodiversity monitoring because the diversity, geographic distribution and abundance of animals, plants and other organisms is tightly linked to the physical structure of vegetation and associated microclimates. Light Detection And Ranging (LiDAR) — an active remote sensing technique — can provide detailed and high resolution information on ecosystem structure because the laser pulse emitted from the sensor and its subsequent return signal from the vegetation (leaves, branches, stems) delivers three-dimensional point clouds from which metrics of vegetation structure (e.g. ecosystem height, cover, and structural complexity) can be derived. However, processing 3D LiDAR point clouds into geospatial data products of ecosystem structure remains challenging across broad spatial extents due to the large volume of national or regional point cloud datasets (typically multiple terabytes consisting of hundreds of billions of points). Here, we present a high-throughput workflow called ‘Laserfarm’ enabling the efficient, scalable and distributed processing of multi-terabyte LiDAR point clouds from national and regional airborne laser scanning (ALS) surveys into geospatial data products of ecosystem structure. Laserfarm is a free and open-source, end-to-end workflow which contains modular pipelines for the re-tiling, normalization, feature extraction and rasterization of point cloud information from ALS and other LiDAR surveys. The workflow is designed with horizontal scalability and can be deployed with distributed computing on different infrastructures, e.g. a cluster of virtual machines. We demonstrate the Laserfarm workflow by processing a country-wide multi-terabyte ALS dataset of the Netherlands (covering ∼34,000 km² with ∼700 billion points and ∼ 16 TB uncompressed LiDAR point clouds) into 25 raster layers at 10 m resolution capturing ecosystem height, cover and structural complexity at a national extent. The Laserfarm workflow, implemented in Python and available as Jupyter Notebooks, is applicable to other LiDAR datasets and enables users to execute automated pipelines for generating consistent and reproducible geospatial data products of ecosystems structure from massive amounts of LiDAR point clouds on distributed computing infrastructures, including cloud computing environments. We provide information on workflow performance (including total CPU times, total wall-time estimates and average CPU times for single files and LiDAR metrics) and discuss how the Laserfarm workflow can be scaled to other LiDAR datasets and computing environments, including remote cloud infrastructures. The Laserfarm workflow allows a broad user community to process massive amounts of LiDAR point clouds for mapping vegetation structure, e.g. for applications in ecology, biodiversity monitoring and ecosystem restoration.     

LiDAR as a rapid tool to predict forest habitat types in Natura 2000 networks

Management strategies for the conservation of biodiversity can be developed only with precise information on the spatial distribution of organisms on relevant, mostly regional, spatial scales. Current surrogates for approximating the distribution of biodiversity are habitats mapped within a number of national and international frameworks (e.g., Natura 2000), even though conventional habitat mapping is time consuming and requires well-trained personnel. Here we evaluated the use of light detection and ranging (LiDAR) to map forest habitat types to simplify the process. We used available data of habitat types for the Bavarian Forest National Park as a basis to predict habitat types with LiDAR-derived variables. Furthermore, we compared these results with predictions based on extensive ground-based climate, soil and vegetation data. Using linear and flexible discriminant analyses, we found that LiDAR is able to predict forest habitat types with the same overall accuracy as the extensive ground data for climate, soil and vegetation composition. Subtle differences in the vegetation structure between habitat types, particularly in the vertical and horizontal vegetation profiles, were captured by LiDAR. These differences in the physiognomy were in part caused by changes in altitude, which also influence tree species composition. We propose that the most-efficient way to identify forest habitat types according Natura 2000 is to combine remote-sensing LiDAR data with well-directed field surveys.     

Using airborne laser scanning to model potential abundance and assemblages of forest passerines

Modelling and forecasting of the distribution and abundance of organisms using environmental variables is a major focus of applied ecological research. High-resolution airborne laser scanning is a recently developed remote-sensing method that provides data that can be used as surrogates for the vertical structure of the vegetation. These data can be used for modelling the occurrence and abundance of species or species assemblages. Until now, few studies evaluated the potential of these data for use in such models, or compared the suitability of data obtained by airborne systems with data gained by alternative methods. To fill part of this gap, we used forest passerine bird species to evaluate airborne laser scanning data for statistical modelling of potential bird abundances and composition of assemblages. Birds were counted in a mixed montane forest, on 223 1-ha plots along four transects. In the same period, these areas were scanned using Light Detection And Ranging (LiDAR) to characterise canopy structure. Additionally, we used visual interpretations of aerial photographs and field measurements on the same plots to derive habitat variables for comparison. We found clear correlations between the LiDAR variables and the other two variable sets using canonical correlation analysis. With a few exceptions, predictive power of the LiDAR data set for modelling abundances of single species, with up to 40% explained variance, was superior to that of the other two data sets. Models agreed with existing ecological knowledge for these species. For modelling of species composition with redundancy analysis, LiDAR was also superior to the other two data sets with more than 20% unique contribution to the explained variance. Our results clearly showed that LiDAR provides valuable data for describing and modelling single species as well as assemblages of forest organisms.     

Which metrics derived from airborne laser scanning are essential to measure the vertical profile of ecosystems?

In a recent perspective (Diversity and Distributions, 29, 39–50), ‘10 variables’ were proposed to measure vegetation structure from airborne laser scanning (ALS) for assessing species distributions and habitat suitability. We worry about this list because the variables predominantly represent variation in vegetation height, the vertical variability of vegetation biomass is insufficiently captured, and variables of vegetation cover are ill-defined or not ecosystem agnostic. We urge for a better defined, more comprehensive and more balanced list, and for assessing which information from ALS point clouds is truly essential to measure the major dimensions of 3D vegetation structure within and across ecosystems and animal habitats. We think that the currently proposed ‘list of 10 ALS metrics’ is premature and that researchers and stakeholders should be cautious in adopting this list.     

Comparing canopy metrics derived from terrestrial and airborne laser scanning in a Douglas-fir dominated forest stand

Accurate estimates of vegetation structure are important for a large number of applications including ecological modeling and carbon budgets. Light detection and ranging (LiDAR) measures the three-dimensional structure of vegetation using laser beams. Most LiDAR applications today rely on airborne platforms for data acquisitions, which typically record between 1 and 5 “discrete” returns for each outgoing laser pulse. Although airborne LiDAR allows sampling of canopy characteristics at stand and landscape level scales, this method is largely insensitive to below canopy biomass, such as understorey and trunk volumes, as these elements are often occluded by the upper parts of the crown, especially in denser canopies. As a supplement to airborne laser scanning (ALS), a number of recent studies used terrestrial laser scanning (TLS) for the biomass estimation in spatially confined areas. One such instrument is the Echidna^® Validation Instrument (EVI), which is configured to fully digitize the returned energy of an emitted laser pulse to establish a complete profile of the observed vegetation elements. In this study we assess and compare a number of canopy metrics derived from airborne and TLS. Three different experiments were conducted using discrete return ALS data and discrete and full waveform observations derived from the EVI. Although considerable differences were found in the return distribution of both systems, ALS and TLS were both able to accurately determine canopy height (Δ height < 2.5 m) and the vertical distribution of foliage and leaf area (0.86 > r ² > 0.90, p < 0.01). When using more spatially explicit approaches for modeling the biomass and volume throughout the stands, the differences between ALS and TLS observations were more distinct; however, predictable patterns exist based on sensor position and configuration.     

A forest structure habitat index based on airborne laser scanning data

Most efforts to link remote sensing to species distributions and movement have focused on indirect estimates of traits based on components of physiological and functional biodiversity. Such a view reflects one perspective on the general needs (habitat) of species. However, information on the vertical and horizontal structure of habitat may play a critical role in defining what a suitable habitat is. The development and application of highly accurate airborne laser scanning (ALS) systems, which are capable of describing the three-dimensional distribution of vegetation, have significant potential value in deriving quantitative relationships between species distributions and their habitat structure. In this paper we review the use of ALS for biodiversity studies, and propose a three-dimensional index which captures the three main components of vertical and horizontal vegetation structure: height, cover, and complexity. Once developed, we apply the index across the forested area of the Canadian province of Alberta, and compare and contrast the differences across natural subregions and land cover types. We also demonstrate how the index can be used with biodiversity data, in this case examining patterns in avian species richness. We conclude with a discussion on the potential use of the habitat structure index with other biodiversity-related research.     

Ecotope mapping for landscape ecological assessment of habitat and ecosystem

An ecotope (spatial eco-space) map that considers topography and bio-organism-relevant variables emerges as an important basic framework when landscape-scale characteristics for ecosystem management and wildlife conservation are needed. A spatio-geoecological framework based on geographic information systems (GIS) and a vegetation survey were developed for wildlife habitat evaluation of national parks and applied to a representative rugged valley area of Mt. Sorak National Park in Korea. An ecotope map was classified into hundreds of types and dozens of groups by combining biological and geophysical variables. Variables included: forest vegetation type, topographic solar radiation, normalized difference vegetation index (NDVI), elevation, and anthropogenic factors, such as, streams and roads. Layers of GIS variables were produced by field surveys, modeling, satellite images, or digitalization. Vegetation surveys were carried out to identify finer-scale distribution of vegetation types in the rugged valley area. Digital forest vegetation maps from the Forestry Administrator were then modified using the field-surveyed vegetation maps. Topographic solar radiation was predicted with a daily topographic radiation model. The NDVI was calculated from the satellite imagery of a Landsat Thematic Mapper. A digital elevation model (DEM) was used and the other layers were digitized using topographical maps with a scale of 1:25000. The aim of this study is to determine the geoecological factors relating to the spatial pattern of plant community. It was cleared by the spatial pattern of environmental variables and vegetation characteristics by detrended correspondence analysis using plant species and the environmental variables of each plot. The ordination component value of the first axis shows significant regression to some environmental variables. A case study of habitat evaluation was carried out using the resultant ecotope map. The spatial distribution of potential goral habitat and vegetation characteristics were predicted and the impact of human trails on the neighboring vegetation was also examined for restoration planning. The GIS-based framework developed for wildlife habitat evaluation is useful for natural resource management and human activity control in national parks in Korea.     

Radar remote sensing as a novel tool to assess the performance of an agri-environment scheme in coastal grasslands

Coastal grasslands are semi-natural habitats used for agriculture, but they are also vital habitats for many taxa, such as waders. The importance of this habitat for wildlife has found recognition in the policies of the European Union, resulting in the implementation of biodiversity-friendly agri-environment schemes (AES) to ensure the sustainable management of these areas. However, the performance of AESs has often been questioned and data to analyse their effectiveness is mostly lacking.To fill this knowledge gap, we used freely available European Space Agency's Sentinel-1 Synthetic Aperture Radar (SAR) images and ground truth data to describe the vegetation height based habitat suitability of coastal grasslands as breeding sites for waders at a country-wide scale in Estonia, where the main threat to this habitat is overgrowing with high sward plant species.We show that satellite remote sensing can be used as a straight-forward and reliable tool to estimate the grazed coastal grassland habitat suitability for breeding waders in terms of vegetation height in two broad classes.We demonstrate that the AES applied on Estonian coastal grasslands is important for improving the suitability of this habitat for the breeding wader community: compared to 24% of suitable habitat in areas where no agricultural subsidies are paid, 60% of the land under the targeted AES is suitably managed for breeding waders. The highest proportion of habitat suitability (76%) is achieved in areas receiving targeted top-up subsidy for wader conservation.Continued monitoring of the performance of the AES is essential for taking timely and targeted conservation actions and we strongly recommend the wider use of remotely sensed data for this purpose. We promote the continuation of the AES scheme applied on Estonian coastal grasslands while improving its efficiency through carefully raised stocking rates and increased uptake of the top-up scheme.     

Swamp rabbits as indicators of wildlife habitat quality in bottomland hardwood forest ecosystems

Reforestation of bottomland hardwood (BLH) forests has occurred within the Lower Mississippi Alluvial Valley (LMAV), USA, to support a wide range of ecosystem services, but especially wildlife habitat enhancement. As ecosystem restoration efforts proceed in BLH ecosystems, managers and policymakers are seeking criteria to evaluate wildlife habitat enhancement goals. Specialist wildlife that evolved within forest ecosystems can be sensitive to the composition, structure, and function of an ecosystem in relation to the system's natural or historical range of variation and thereby serve as indicators of habitat quality. The swamp rabbit (Sylvilagus aquaticus) is a specialist species of BLH forests throughout the LMAV and therefore may be an appropriate indicator species for this ecosystem. To address this, we reviewed peer-reviewed literature to evaluate the utility of swamp rabbits as an indicator species according to three commonly-used criteria: habitat factors defining swamp rabbit relationships to BLH forests, the importance of swamp rabbit habitat to other wildlife, and the efficiency of swamp rabbit monitoring. We conclude that the swamp rabbit is a suitable indicator of wildlife habitat quality in BLH ecosystems in the LMAV because they evolved and remain endemic to the ecosystem, use habitat that integrates desirable characteristics that positively influence wildlife biodiversity, and are easy to monitor routinely.     

Applying an animal‐centric approach to improve ecological restoration

Traditionally, ecological restoration is based on re‐establishing patterns of vegetation communities with the expectation that wildlife will recolonize, restoring the ecological function. However, in many restoration projects, wildlife fails to recolonize, even when vegetation is restored, in many cases because revegetated habitats lack the critical features required by wildlife. We present a new approach to restoration, based on a detailed understanding of ecological process, the mechanisms by which wildlife respond to landscape patterns. Our animal‐centric approach involves measuring the risk‐sensitive decision‐making of individual animals as they balance searching for food, mates, and breeding sites with avoiding being eaten by predators and relates this to fine‐scale habitat and landscape structure. The outcome of these decisions can be measured in occupancy of habitat, the information on which conventional restoration is based. Incorporating landscape genetics allows retrospective assessment of the outcome of dispersal decisions by individual animals on a deeper time frame and at regional scales. Fine‐scale connectivity models can be parameterized with these multiscale spatial and temporal data to direct restoration efforts. We are translating this novel approach to practice in the large Midlands restoration project (4 years, AUD $6 million) in Tasmania, Australia, in partnership with Greening Australia. More than 200 years of intensive agricultural practice in this National Biodiversity Hotspot has resulted in extensive landscape modification, high densities of feral cats, and decline of many native mammals. Our research–practice partnership will alter the way that restoration is done, leading hopefully to successful restoration of wildlife, gene flow, and ecological function.     

Food abundance determines distribution and density of a frugivorous bird across seasons

Although food abundance is a principal determinant of distribution and abundance of many animals, most previous studies have not quantitatively assessed its importance relative to other factors that may also determine species distributions. We estimated frugivorous phainopepla Phainopepla nitens occupancy and density, food density, and vegetation structure on transects in fragmented mesquite and acacia woodlands over three years in non‐breeding and breeding seasons. Using an AIC framework and controlling for detection probability, we determined relative impacts of food abundance, vegetation structure, and habitat fragmentation on patch occupancy and density, and concomitant extinction and colonization probabilities of phainopeplas. Initial occupancy in winter 2002 was high (0.87 ± 0.047), and primarily positively correlated with food abundance and woodland area (Akaike weights w_i= 0.998 and 0.750 respectively). Woodland area more strongly influenced occupancy where food was scarcer. Phainopepla density in both seasons was strongly positively correlated with food abundance, especially in the 2002 drought when density was higher (w_i=1.0 for food and year). Density was higher in acacia than mesquite woodlands (w_i= 1.0), and moderately negatively correlated with elevation (w_i= 0.789). Extinction probability (patches vacated) was low (0.078 ± 0.040), and principally influenced by phainopepla density (w_i= 0.968) and tree height (w_i= 0.749). Colonization probability was low (0.15 ± 0.034) and determined by vegetation structure (w_i= 1.0). Much recorded colonization was reoccupancy of woodlands previously occupied by single males in winter, then vacated in a breeding season. These results suggest that for an animal occupying a highly fragmented landscape, distributions and densities at the habitat patch scale are driven by food abundance, are moderately affected by habitat fragmentation, and are slightly influenced by vegetation structure.     

Influence of fine-scale habitat structure on nest-site occupancy,laying date and clutch size in Blue Tits Cyanistes caeruleus

Most birds have specific habitat requirements for breeding. The vegetation structure surrounding nest-sites is an important component of habitat quality, and can have large effects on avian breeding performance. We studied 13 years of Blue Tit Cyanistes caeruleus population data to determine whether characteristics of vegetation structure predict site occupancy, laying date and number of eggs laid. Measurements of vegetation structure included the density of English Oak Quercus robur, European Beech Fagus sylvatica, and other deciduous, coniferous and non-coniferous evergreen trees, within a 20-m radius of nest-boxes used for breeding. Trees were further sub-divided into specific classes of trunk circumferences to determine the densities for different maturity levels. Based on Principal Component Analysis (PCA), we reduced the total number of 17 measured vegetation variables to 7 main categories, which we used for further analyses. We found that the occupancy rate of sites and the number of eggs laid correlated positively with the proportion of deciduous trees and negatively with the density of coniferous trees. Laying of the first egg was advanced with a greater proportion of deciduous trees. Among deciduous trees, the English Oak appeared to be most important, as a higher density of more mature English Oak trees was associated with more frequent nest-box occupancy, a larger number of eggs laid, and an earlier laying start. Furthermore, laying started earlier and more eggs were laid in nest-boxes with higher occupancy rates. Together, these findings highlight the role of deciduous trees, particularly more mature English Oak, as important predictors of high-quality preferred habitat. These results aid in defining habitat quality and will facilitate future studies on the importance of environmental quality for breeding performance.     

Characterization of diverse plant communities in Aspen Parkland rangeland using LiDAR data

Question: How effective is high-resolution airborne LiDAR technology for quantifying biophysical characteristics of multiple community types within diverse rangeland environments? Location: Native Aspen Parkland vegetation in central Alberta, Canada. Methods: Vegetation within 117 reference plots stratified across eight types, including forest, shrubland, upland grassland and lowland meadow communities, were assessed in 2001 for the height, cover and density of vegetation within various strata (herb, shrub and tree layers). Actual ground data were subsequently compared against modelled values for each community type and strata derived from the analysis of airborne LiDAR data obtained in 2000. Results: LiDAR data were effective for quantifying vegetation height, cover and density of the overstory within closed- and open Populus forest communities. However, LiDAR measurements typically underestimated the height and cover of shrublands, as well as most of the herbaceous communities. Analysis of LiDAR intensity data indicated reflectance generally decreased as LiDAR sampling points moved upwards from the ground to the vegetation canopy. Conclusions: While LiDAR technology is useful for characterizing deciduous forest properties, the quantification of understory vegetation characteristics, as well as those of individual shrublands and grasslands, was more limiting. Further refinements in analysis methods are necessary to increase the reliability of characterizing these communities.     

Human disturbance and stage‐specific habitat requirements influence snowy plover site occupancy during the breeding season

Habitat use has important consequences for avian reproductive success and survival. In coastal areas with recreational activity, human disturbance may limit use of otherwise suitable habitat. Snowy plovers Charadrius nivosus have a patchy breeding distribution along the coastal areas on the Florida Panhandle, USA. Our goal was to determine the relative effects of seasonal human disturbance and habitat requirements on snowy plover habitat use. We surveyed 303 sites for snowy plovers, human disturbance, and habitat features between January and July 2009 and 2010. We made multiple visits during three different sampling periods that corresponded to snowy plover breeding: pre‐breeding, incubation, and brood‐rearing and used multi‐season occupancy models to examine whether human disturbance, habitat features, or both influenced site occupancy, colonization (probability of transition from an unoccupied site to an occupied site), and extinction (probability of transition from an occupied site to an unoccupied site). Snowy plover site occupancy and colonization was negatively associated with human disturbance and site extinction was positively associated with human disturbance. Interdune vegetation had a negative effect on occupancy and colonization, indicating that plovers were less likely to use areas with uniform, dense vegetation among dunes. Also, dune shape, beach debris, and access to low‐energy foraging areas influenced site occupancy, colonization, and extinction. Plovers used habitat based on beach characteristics that provided stage‐specific resource needs; however, human disturbance was the strongest predictor of site occupancy. In addition, vegetation plantings used to enhance dune rehabilitation may negatively impact plover site occupancy. Management actions that decrease human disturbance, such as symbolic fencing and signage, may increase the amount of breeding habitat available to snowy plovers on the Florida Panhandle and in other areas with high human activity. The specific areas that require this protection may vary across snowy plover life history stages.