Differentiating aquatic plant communities in a eutrophic river using hyperspectral and multispectral remote sensing

1. This study evaluates the efficacy of remote sensing technology to monitor species composition, areal extent and density of aquatic plants (macrophytes and filamentous algae) in impoundments where their presence may violate water‐quality standards. 2. Multispectral satellite (IKONOS) images and more than 500 in situ hyperspectral samples were acquired to map aquatic plant distributions. By analyzing field measurements, we created a library of hyperspectral signatures for a variety of aquatic plant species, associations and densities. We also used three vegetation indices. Normalized Difference Vegetation Index (NDVI), near‐infrared (NIR)‐Green Angle Index (NGAI) and normalized water absorption depth (D_H)_, at wavelengths 554, 680, 820 and 977 nm to differentiate among aquatic plant species composition, areal density and thickness in cases where hyperspectral analysis yielded potentially ambiguous interpretations. 3. We compared the NDVI derived from IKONOS imagery with the in situ, hyperspectral‐derived NDVI. The IKONOS‐based images were also compared to data obtained through routine visual observations. Our results confirmed that aquatic species composition alters spectral signatures and affects the accuracy of remote sensing of aquatic plant density. The results also demonstrated that the NGAI has apparent advantages in estimating density over the NDVI and the D_H. 4. In the feature space of the three indices, 3D scatter plot analysis revealed that hyperspectral data can differentiate several aquatic plant associations. High‐resolution multispectral imagery provided useful information to distinguish among biophysical aquatic plant characteristics. Classification analysis indicated that using satellite imagery to assess Lemna coverage yielded an overall agreement of 79% with visual observations and >90% agreement for the densest aquatic plant coverages. 5. Interpretation of biophysical parameters derived from high‐resolution satellite or airborne imagery should prove to be a valuable approach for assessing the effectiveness of management practices for controlling aquatic plant growth in inland waters, as well as for routine monitoring of aquatic plants in lakes and suitable lentic environments.     

High-Resolution Satellite Imagery Is an Important yet Underutilized Resource in Conservation Biology

Technological advances and increasing availability of high-resolution satellite imagery offer the potential for more accurate land cover classifications and pattern analyses, which could greatly improve the detection and quantification of land cover change for conservation. Such remotely-sensed products, however, are often expensive and difficult to acquire, which prohibits or reduces their use. We tested whether imagery of high spatial resolution (≤5 m) differs from lower-resolution imagery (≥30 m) in performance and extent of use for conservation applications. To assess performance, we classified land cover in a heterogeneous region of Interior Atlantic Forest in Paraguay, which has undergone recent and dramatic human-induced habitat loss and fragmentation. We used 4 m multispectral IKONOS and 30 m multispectral Landsat imagery and determined the extent to which resolution influenced the delineation of land cover classes and patch-level metrics. Higher-resolution imagery more accurately delineated cover classes, identified smaller patches, retained patch shape, and detected narrower, linear patches. To assess extent of use, we surveyed three conservation journals (Biological Conservation, Biotropica, Conservation Biology) and found limited application of high-resolution imagery in research, with only 26.8% of land cover studies analyzing satellite imagery, and of these studies only 10.4% used imagery ≤5 m resolution. Our results suggest that high-resolution imagery is warranted yet under-utilized in conservation research, but is needed to adequately monitor and evaluate forest loss and conversion, and to delineate potentially important stepping-stone fragments that may serve as corridors in a human-modified landscape. Greater access to low-cost, multiband, high-resolution satellite imagery would therefore greatly facilitate conservation management and decision-making.     

Detection, differentiation, and abundance estimation of penguin species by high-resolution satellite imagery

Due to its high spatial resolution, broad spatial coverage, and cost-effectiveness, commercial satellite imagery is rapidly becoming a key component of biological monitoring in the Antarctic. While considerable success in surveying emperor penguins (Aptenodytes forsteri) has been facilitated by their large size and the visual simplicity of their habitat, there has been considerably less progress in mapping colonies on the Antarctic Peninsula and associated sub-Antarctic islands where smaller penguin species breed on topographically complex terrain composed of mixed substrates. Here, we demonstrate that Adélie penguin (Pygoscelis adeliae), chinstrap penguin (P. antarcticus), gentoo penguin (P. papua), and macaroni penguin (Eudyptes chrysolophus) colonies can be detected by high-resolution (2-m multispectral, 40–50-cm panchromatic) satellite imagery and that under ideal conditions, such imagery is capable of distinguishing among groups of species where they breed contiguously. To demonstrate the potential for satellite imagery to estimate penguin population abundance, we use satellite imagery of Paulet Island (63°35′S, 55°47′W) to estimate a site-wide population of 115,673 (99,222–127,203) breeding pairs of Adélie penguins.     

Mangrove forest distributions and dynamics (1975–2005) of the tsunami‐affected region of Asia†

Aim We aimed to estimate the present extent of tsunami‐affected mangrove forests and determine the rates and causes of deforestation from 1975 to 2005. Location Our study region covers the tsunami‐affected coastal areas of Indonesia, Malaysia, Thailand, Burma (Myanmar), Bangladesh, India and Sri Lanka in Asia. Methods We interpreted time‐series Landsat data using a hybrid supervised and unsupervised classification approach. Landsat data were geometrically corrected to an accuracy of plus‐or‐minus half a pixel, an accuracy necessary for change analysis. Each image was normalized for solar irradiance by converting digital number values to the top‐of‐the atmosphere reflectance. Ground truth data and existing maps and data bases were used to select training samples and also for iterative labelling. We used a post‐classification change detection approach. Results were validated with the help of local experts and/or high‐resolution commercial satellite data. Results The region lost 12% of its mangrove forests from 1975 to 2005, to a present extent of c. 1,670,000 ha. Rates and causes of deforestation varied both spatially and temporally. Annual deforestation was highest in Burma (c. 1%) and lowest in Sri Lanka (0.1%). In contrast, mangrove forests in India and Bangladesh remained unchanged or gained a small percentage. Net deforestation peaked at 137,000 ha during 1990–2000, increasing from 97,000 ha during 1975–90, and declining to 14,000 ha during 2000–05. The major causes of deforestation were agricultural expansion (81%), aquaculture (12%) and urban development (2%). Main conclusions We assessed and monitored mangrove forests in the tsunami‐affected region of Asia using the historical archive of Landsat data. We also measured the rates of change and determined possible causes. The results of our study can be used to better understand the role of mangrove forests in saving lives and property from natural disasters such as the Indian Ocean tsunami, and to identify possible areas for conservation, restoration and rehabilitation.     

A global assessment of forest surface albedo and its relationships with climate and atmospheric nitrogen deposition

We present a global assessment of the relationships between the short‐wave surface albedo of forests, derived from the MODIS satellite instrument product at 0.5° spatial resolution, with simulated atmospheric nitrogen deposition rates (N_dep), and climatic variables (mean annual temperature T_m and total annual precipitation P), compiled at the same spatial resolution. The analysis was performed on the following five forest plant functional types (PFTs): evergreen needle‐leaf forests (ENF); evergreen broad‐leaf forests (EBF); deciduous needle‐leaf forests (DNF); deciduous broad‐leaf forests (DBF); and mixed‐forests (MF). Generalized additive models (GAMs) were applied in the exploratory analysis to assess the functional nature of short‐wave surface albedo relations to environmental variables. The analysis showed evident correlations of albedo with environmental predictors when data were pooled across PFTs: T_m and N_dep displayed a positive relationship with forest albedo, while a negative relationship was detected with P. These correlations are primarily due to surface albedo differences between conifer and broad‐leaf species, and different species geographical distributions. However, the analysis performed within individual PFTs, strengthened by attempts to select ‘pure’ pixels in terms of species composition, showed significant correlations with annual precipitation and nitrogen deposition, pointing toward the potential effect of environmental variables on forest surface albedo at the ecosystem level. Overall, our global assessment emphasizes the importance of elucidating the ecological mechanisms that link environmental conditions and forest canopy properties for an improved parameterization of surface albedo in climate models.     

Estimating Canopy Structure in an Amazon Forest from Laser Range Finder and IKONOS Satellite Observations1

Canopy structural data can be used for biomass estimation and studies of carbon cycling, disturbance, energy balance, and hydrological processes in tropical forest ecosystems. Scarce information on canopy dimensions reflects the difficulties associated with measuring crown height, width, depth, and area in tall, humid tropical forests. New field and spaceborne observations provide an opportunity to acquire these measurements, but the accuracy and reliability of the methods are unknown. We used a handheld laser range finder to estimate tree crown height, diameter, and depth in a lowland tropical forest in the eastern Amazon, Brazil, for a sampling of 300 trees stratified by diameter at breast height (DBH). We found significant relationships between DBH and both tree height and crown diameter derived from the laser measurements. We also quantified changes in crown shape between tree height classes, finding a significant but weak positive trend between crown depth and width. We then compared the field‐based measurements of crown diameter and area to estimates derived manually from panchromatic 0.8 m spatial resolution IKONOS satellite imagery. Median crown diameter derived from satellite observations was 78 percent greater than that derived from field‐based laser measurements. The statistical distribution of crown diameters from IKONOS was biased toward larger trees, probably due to merging of smaller tree crowns, underestimation of understory trees, and overestimation of individual crown dimensions. The median crown area derived from IKONOS was 65 percent higher than the value modeled from field‐based measurements. We conclude that manual interpretation of IKONOS satellite data did not accurately estimate distributions of tree crown dimensions in a tall tropical forest of eastern Amazonia. Other methods will be needed to more accurately estimate crown dimensions from high spatial resolution satellite imagery.     

Local Mangrove Planting in the Philippines: Are Fisherfolk and Fishpond Owners Effective Restorationists?

Local fisherfolk and fishpond owners have been practicing “restoration” of mangrove forests in some parts of the Philippines for decades, well before governments and non‐government organizations began to promote the activity as a conservation tool. This paper examines ecological characteristics of these mangrove plantations and compares them to natural mangroves in the same areas. Mangrove planters were interviewed and plantation and natural mangrove forests were surveyed to measure forest structure, composition and regeneration. Compared with natural forests, mangrove plantations were characterized by high densities of small stems, shorter and narrower canopies, and fewer species. For both economic and ecological reasons, the vast majority of people dispersed and planted only Rhizophora mucronata/stylosa and, furthermore, they often thinned other species out of planted areas. There was remarkably little subsequent recruitment of other, nonplanted mangrove species into plantations up to 50 and 60 years of age. This pattern held across a diversity of sites, including plantations that had not been selectively cut or weeded. Important ecological and economic benefits result from local mangrove planting, but catalyzing diverse forest regeneration—at least in the short to medium term—is not one of them. The lesson: if you want to restore diverse mangrove forests, you have to plant diverse mangrove forests.     

Zonation of mangrove flora and fauna in a subtropical estuarine wetland based on surface elevation

In the context of sea‐level rise (SLR), an understanding of the spatial distributions of mangrove flora and fauna is required for effective ecosystem management and conservation. These distributions are greatly affected by tidal inundation, and surface elevation is a reliable quantitative indicator of the effects of tidal inundation. Most recent studies have focused exclusively on the quantitative relationships between mangrove‐plant zonation and surface elevation, neglecting mangrove fauna. Here, we measured surface elevation along six transects through the mangrove forests of a subtropical estuarine wetland in Zhenzhu Bay (Guangxi, China), using a real‐time kinematic global positioning system. We identified the mangrove plants along each transect and investigated the spatial distributions of arboreal, epifaunal, and infaunal molluscs, as well as infaunal crabs, using traditional quadrats. Our results indicated that almost all mangrove forests in the bay were distributed within the 400–750 m intertidal zone, between the local mean sea level and mean high water (119 cm above mean sea level). Mangrove plants exhibited obvious zonation patterns, and different species tended to inhabit different niches along the elevation gradient: Aegiceras corniculatum dominated in seaward locations while Lumnitzera racemosa dominated in landward areas. Mangrove molluscs also showed distinct patterns of spatial zonation related to surface elevation, independent of life‐form and season. The spatial distributions of some molluscs were correlated to the relative abundances of certain mangrove plants. In contrast, the spatial distributions of crabs were not related to surface elevation. To the best of our knowledge, this is the first study to explicitly quantify the influences of surface elevation on the spatial distributions of mangrove fauna. This characterization of the vertical ranges of various flora and fauna in mangrove forests provides a basic framework for future studies aimed at predicting changes in the structure and functions of mangrove forests in response to SLR.     

基于多源遥感的红树林监测

红树林是生长在热带以及亚热带海岸潮间带上的生态群落, 其生产力高, 固碳能力强, 对保持海岸带生物多样性具有十分重要的价值。本文介绍了利用多源遥感数据监测红树林的一些主要研究内容, 分为3个方面: (1)在时空模式研究方面, 利用高空间分辨率影像像素和对象结合的方法对红树林树种进行分类以及利用Landsat影像对红树林进行动态变化监测并分析其驱动因素; (2)在结构参数研究方面, 利用无人机多光谱数据及地面激光雷达数据对红树林叶面积指数进行反演; (3)在生理生化参数研究方面, 探讨了红树林叶绿素含量对淹没状况的响应、互花米草(Spartina alterniflora)入侵是否影响红树林光能利用率, 以及光化学反射指数(photochemical reflectance index, PRI)与光能利用率(light use efficiency, LUE)的关系。上述系列研究为提取红树林相关信息要素时如何选择合适的分析方法提供了有力的参考, 强调了遥感在研究红树林时空模式, 提取结构参数和生物生化参数监测的有效性, 从而更好地促进红树林生态系统的生物多样性保育工作。     

The current status of mangroves along the Kenyan coast: a case study of Mida Creek mangroves based on remote sensing

Mangroves form a unique ecosystem of limited extent covering an area of about 53 000 ha along the Kenyan coast which need protection from overexploitation. Background information on the areal extent and status of these mangroves is limited and makes their protection and management difficult.A model study has been carried out on the Mida Creek mangroves based on a double sampling approach starting with SPOT multispectral satellite imagery followed by ground checks to provide information on species composition, density and distribution of mangroves. The utilization of mangroves and uses around and within the mangroves were determined.During this study, seven mangrove species were identified in Mida Creek, namely:Avicennia marina, Bruguiera gymnorrhiza, Ceriops tagal, Rhizophora mucronata, Sonneratia alba, Xylocarpus granatum andLumnitzera racemosa. This report briefly outlines the present utilisation of mangrove species. It is recommended that a multidisciplinary management plan be developed in order to conserve and manage the mangroves of Kenya on a sustainable yield basis.     

Evaluation of remote sensing techniques for monitoring giant kelp populations

Photographs and maps of the floating canopy of the giant kelp, Macrocystis pyrifera, provide an important data source to monitor nearshore water quality in southern California. Declines in water quality related to turbidity from coastal development, ocean discharges, and non-point source runoff have caused reductions in the areal extent of these kelp beds. Historically the kelp beds have been monitored by a variety of methods including small format infrared and color photography. New digital remote sensing instruments combined with geographical information system (GIS) databases offer an efficient method for collecting and analyzing data on changes in kelp bed size and location. SPOT satellite imagery has been found to provide adequate resolution for mapping the larger beds of giant kelp along the California coast. Beds smaller than 10 ha are not resolved well with SPOT imagery and need to be mapped with a resolution greater than the 20 m pixel size provided by the SPOT multispectral imagery. Imagery from a prototype of the Positive Systems ADAR system, an airplane mounted multispectral video sensor, provided a spatial resolution of 2.3 m in 4 spectral bands. ADAR imagery taken on 2 October 1991 of the San Onofre Kelp Bed in northern San Diego County showed 39% more kelp than small format color infrared photography made during the same time period.     

Application of QuickBird and aerial imagery to detect Pinus radiata in remnant vegetation

The invasion of Pinus radiata from long‐term established plantations is contributing to the degradation of fragmented and isolated remnants of native vegetation. Within the south‐east of South Australia, the 20 vegetation communities that occur within 500 m of a plantation edge are at risk, including nine state threatened communities. To plan effective mitigation strategies, the current extent and distribution of P. radiata needs to be ascertained. High spatial resolution, multispectral QuickBird imagery and aerial photography were used to classify P. radiata within eucalypt and acacia woodlands, melaleuca shrubland, modified pasture and an Eucalyptus globulus plantation. Unsupervised classification of aerial photography gave the best result showing reasonable conformity with the observed distribution of P. radiata at the site scale. However, the 9.4 ± 13.5 (SD) cover classified in the quadrats sampled for the accuracy assessment exceeded the 1.4 ± 2.4 (SD) P. radiata cover determined from an independent dataset. Only 30.1 ± 37.9% (SD) of trees within the quadrats and 9.40 ± 13.49% (SD) of their foliage cover were classified. Trees detected by partial classification of canopy were positively correlated with both tree height and canopy diameter. Overall, the low detection rates were attributed to insufficient spectral resolution. Using higher resolution imagery, together with an object‐based image analysis or combination of multispectral and airborne digital image classification, restricted to large emergent adult trees using LiDAR analysis, is likely to improve adult P. radiata detection accuracy.     

Host Specialization among Wood-Decay Polypore Fungi in a Caribbean Mangrove Forest1

Host specialization in highly diverse tropical forests may be limited by the low local abundance of suitable hosts. To address whether or not fungi in a low‐diversity tropical forest were released from this restriction, fruiting bodies of polypore basidiomycete fungi were collected from three species of mangroves (Avicennia germinans, Laguncularia racemosa, and Rhizophora mangle) in a Caribbean mangrove forest in Panama. Unlike other tropical forests, the polypore assemblage in this mangrove forest was strongly dominated by a few host‐specialized species. Three fungal species, each with strong preference for a different mangrove host species, comprised 88 percent of all fungi collected.     

Whales from space: Four mysticete species described using new VHR satellite imagery

Large‐bodied animals such as baleen whales can now be detected with very high resolution (VHR) satellite imagery, allowing for scientific studies of whales in remote and inaccessible areas where traditional survey methods are limited or impractical. Here we present the first study of baleen whales using the WorldView‐3 satellite, which has a maximum spatial resolution of 31 cm in the panchromatic band, the highest currently available to nonmilitary professionals. We manually detected, described, and counted four different mysticete species: fin whales (Balaenoptera physalus) in the Ligurian Sea, humpback whales (Megaptera novaeangliae) off Hawaii, southern right whales (Eubalaena australis) off Península Valdés, and gray whales (Eschrichtius robustus) in Laguna San Ignacio. Visual and spectral analyses were conducted for each species, their surrounding waters, and nonwhale objects (e.g., boats). We found that behavioral and morphological differences made some species more distinguishable than others. Fin and gray whales were the easiest to discern due to their contrasting body coloration with surrounding water, and their prone body position, which is proximal to the sea surface (i.e., body parallel to the sea surface). These results demonstrate the feasibility of using VHR satellite technology for monitoring the great whales.     

Mapping canopy nitrogen‐scapes to assess foraging habitat for a vulnerable arboreal folivore in mixed‐species Eucalyptus forests

Herbivore foraging decisions are closely related to plant nutritional quality. For arboreal folivores with specialized diets, such as the vulnerable greater glider (Petauroides volans), the abundance of suitable forage trees can influence habitat suitability and species occurrence. The ability to model and map foliar nitrogen would therefore enhance our understanding of folivore habitat use at finer scales. We tested whether high‐resolution multispectral imagery, collected by a lightweight and low‐cost commercial unoccupied aerial vehicle (UAV), could be used to predict total and digestible foliar nitrogen (N and digN) at the tree canopy level and forest stand‐scale from leaf‐scale chemistry measurements across a gradient of mixed‐species Eucalyptus forests in southeastern Australia. We surveyed temperate Eucalyptus forests across an elevational and topographic gradient from sea level to high elevation (50–1200 m a.s.l.) for forest structure, leaf chemistry, and greater glider occurrence. Using measures of multispectral leaf reflectance and spectral indices, we estimated N and digN and mapped N and favorable feeding habitat using machine learning algorithms. Our surveys covered 17 Eucalyptus species ranging in foliar N from 0.63% to 1.92% dry matter (DM) and digN from 0.45% to 1.73% DM. Both multispectral leaf reflectance and spectral indices were strong predictors for N and digN in model cross‐validation. At the tree level, 79% of variability between observed and predicted measures of nitrogen was explained. A spatial supervised classification model correctly identified 80% of canopy pixels associated with high N concentrations (≥1% DM). We developed a successful method for estimating foliar nitrogen of a range of temperate Eucalyptus species using UAV multispectral imagery at the tree canopy level and stand scale. The ability to spatially quantify feeding habitat using UAV imagery allows remote assessments of greater glider habitat at a scale relevant to support ground surveys, management, and conservation for the vulnerable greater glider across southeastern Australia.     

Differentiating geological fertility derived vegetation zones in Kruger National Park,South Africa,using Landsat and MODIS imagery

Spatial technologies present possibilities for producing frequently updated and accurate habitat maps, which are important in biodiversity conservation. Assemblages of vegetation are equivalent to habitats. This study examined the use of satellite imagery in vegetation differentiation in South Africa's Kruger National Park (KNP). A vegetation classification scheme based on dominant tree species but also related to the park's geology was tested, the geology generally consisting of high and low fertility lithology. Currently available multispectral satellite imagery is broadly either of high spatial but low temporal resolution or low spatial but high temporal resolution. Landsat TM/ETM+ and MODIS images were used to represent these broad categories. Rain season dates were selected as the period when discrimination between key habitats in KNP is most likely to be successful. Principal Component Analysis enhanced vegetated areas on the Landsat images, while NDVI vegetation enhancement was employed on the MODIS image. The images were classified into six field sampling derived classes depicting a vegetation density and phenology gradient, with high (about 89%) indicative classification accuracy. The results indicate that, using image processing procedures that enhance vegetation density, image classification can be used to map the park's vegetation at the high versus low geological fertility zone level, to accuracies above 80% on high spatial resolution imagery and slightly lower accuracy on lower spatial resolution imagery. Rainfall just prior to the image date influences herbaceous vegetation and, therefore, success at image scene vegetation mapping, while cloud cover limits image availability. Small scale habitat differentiation using multispectral satellite imagery for large protected savanna areas appears feasible, indicating the potential for use of remote sensing in savanna habitat monitoring. However, factors affecting successful habitat mapping need to be considered. Therefore, adoption of remote sensing in vegetation mapping and monitoring for large protected savanna areas merits consideration by conservation agencies.     

Modelling mangrove propagule dispersal trajectories using high‐resolution estimates of ocean surface winds and currents

Mangrove forests are systems that provide ecosystem services and rely on floating propagules of which the dispersal trajectories are determined by ocean currents and winds. Quantitating connectivity of mangrove patches is an important conservation concern. Current estimates of connectivity, however, fail to integrate the link between ocean currents at different spatial scales and dispersal trajectories. Here, we use high‐resolution estimates of ocean currents and surface winds from meteorological and oceanographic analyses, in conjunction with experimental data on propagule traits (e.g., density, size, and shape) and dispersal vector properties (e.g., strength and direction of water and wind currents). We incorporate these data in a dispersal model to illustrate the potential effect of wind on dispersal trajectories of hydrochorous propagules from different mangrove species. We focus on the Western Indian Ocean, including the Mozambique Channel, which has received much attention because of its reported oceanic complexity, to illustrate the effect of oceanic features such as eddy currents and tides. In spite of the complex pattern of ocean surface currents and winds, some propagules are able to cross the Mozambique Channel. Eddy currents and tides may delay arrival at a suitable site. Experimentally demonstrated differences in wind sensitivity among propagule types were shown to affect the probability of departure and the shape of dispersal trajectories. The model could be used to reconstruct current fluxes of mangrove propagules that may help explain past and current distributions of mangrove forests and assess the potential for natural expansion of these forests.     

Evaluating,predicting and mapping belowground carbon stores in Kenyan mangroves

Despite covering only approximately 138 000 km², mangroves are globally important carbon sinks with carbon density values three to four times that of terrestrial forests. A key challenge in evaluating the carbon benefits from mangrove forest conservation is the lack of rigorous spatially resolved estimates of mangrove sediment carbon stocks; most mangrove carbon is stored belowground. Previous work has focused on detailed estimations of carbon stores over relatively small areas, which has obvious limitations in terms of generality and scope of application. Most studies have focused only on quantifying the top 1 m of belowground carbon (BGC). Carbon stored at depths beyond 1 m, and the effects of mangrove species, location and environmental context on these stores, are poorly studied. This study investigated these variables at two sites (Gazi and Vanga in the south of Kenya) and used the data to produce a country‐specific BGC predictive model for Kenya and map BGC store estimates throughout Kenya at spatial scales relevant for climate change research, forest management and REDD+ (reduced emissions from deforestation and degradation). The results revealed that mangrove species was the most reliable predictor of BGC; Rhizophora muronata had the highest mean BGC with 1485.5 t C ha^?1. Applying the species‐based predictive model to a base map of species distribution in Kenya for the year 2010 with a 2.5 m² resolution produced an estimate of 69.41 Mt C [±9.15 95% confidence interval (C.I.)] for BGC in Kenyan mangroves. When applied to a 1992 mangrove distribution map, the BGC estimate was 75.65 Mt C (±12.21 95% C.I.), an 8.3% loss in BGC stores between 1992 and 2010 in Kenya. The country‐level mangrove map provides a valuable tool for assessing carbon stocks and visualizing the distribution of BGC. Estimates at the 2.5 m² resolution provide sufficient details for highlighting and prioritizing areas for mangrove conservation and restoration.     

Abundance and population trends of mangrove landbirds in southwest Florida

ABSTRACT The avifauna of south Florida's mangrove forests is unique and relatively unstudied. The population status of landbirds that breed in these forests is currently unknown, and this lack of information is especially problematic for species that have North American ranges limited almost exclusively to Florida's mangroves. To address this information gap, we estimated trends in abundance using data generated during bird surveys conducted from 2000 to 2008 at 101 points in mangrove forests in southwestern Florida. We found that populations of two of three mangrove‐dependent species that breed in these forests, Black‐whiskered Vireos (Vireo altiloquus) and Mangrove Cuckoos (Coccyzus minor), declined significantly during our study. In contrast, only one of seven species with a broader North American range (Red‐bellied Woodpecker, Melanerpes carolinensis) declined in abundance. No species increased in abundance. The Mangrove Cuckoo population exhibited the greatest decline, with numbers declining 87.1% from 2000 to 2008. Numbers of Black‐whiskered Vireos declined 63.9%. These declines coincided with the outbreak of West Nile virus that has been linked to population declines of other North American birds, but we could not rule out other potential causes, including changes in the quality or extent of breeding or wintering habitat.