Impacts of remotely sensed environmental drivers on coral outplant survival

Globally, coral reefs are degrading due to a variety of stressors including climate change and pollution. Active restoration is an important effort for sustaining coral reefs where, typically, coral fragments are outplanted onto degraded reefs. Coral outplants, however, can experience mortality in response to a range of stressors. We pair results of outplant monitoring observations with satellite‐based measurements of multiple oceanographic variables to estimate the relative importance of each driver to coral outplant survival. We find that when considering mean environmental conditions experienced by outplants during the monitoring period, particulate organic carbon (POC) levels are most important in determining outplant survival, with certain levels of POC beneficial for outplants. Sea surface temperature anomalies (SSTA) are also important determinants of outplant survival, where survival is greatest in regions with minimal or slightly negative anomalies. Survival also increases with increasing distance to land, likely due to a reduction in negative ridge‐to‐reef effects on coral outplants. When considering the range (min–max) of environmental conditions experienced during the monitoring period, large fluctuations in photosynthetically active radiation (PAR) and POC are most important in determining outplant survival. Increasing outplant depth can help to counter the negative impacts of large fluctuations in environmental variables. We find that a variety of remotely sensed oceanographic variables have significant impacts on survival and should be considered in coral restoration planning to help evaluate potential restoration sites and ultimately maximize coral outplant survival.     

Semivariograms from a forest transect gap model compared with remotely sensed data

Abstract. A spatially linked version of a forest gap model, ZELIG, parameterized for the H. J. Andrews Experimental Forest, Oregon, was used to generate structural properties (i.e. biomass, leaf area, and maximum tree height) of young (80 yr), mature (140 yr), and old-growth (450 yr) Pseudotsuga menziesii (Douglas fir) forests. Semivariograms were produced at 10 and 30 m resolution to describe the spatio-temporal patterns of variation of the simulated structural features along a 5 km transect of contiguous 10 m x 10 m grid cells. These semivariograms from the simulations were compared with semivariograms from matrices of pixel digital values obtained from aerial videography of similarly aged stands. Although autocorrelative spatial patterning was absent from both the remotely sensed imagery (except at < 20 m for the 450 yr stand) and the model output, the pixel-to-pixel and plot-to-plot variances exhibited similar patterns across the chronosequence at both resolutions. This suggests that gap models are able to capture temporal aspects of landscape dynamics associated with canopy texture of Pacific Northwest forests.     

遥感主体图的准确度对景观生态学研究的影响

用各种案例系统地解释了遥感数据分类误差对景观指数误差的必然影响。一方面 ,遥感数据在各种时间和空间尺度上为景观生态学研究提供必需的土地类型数据 ;另一方面 ,遥感技术的灵活性和复杂性可以产生出各种质量的土地类型数据。但景观生态学方面的用户对土地类型数据基本上是没有选择地使用 ,甚至是不知好坏地使用 ,所以景观生态学的发现和结论具有不可避免的任意性。总结了在各种情况下景观指数的变动区间 ,指出了现实较低的遥感数据的分类准确度会引起更低的景观指数的准确度 ,当进行景观变化分析时 ,这种误差的放大效应将更加明显。当前 ,人们对除面积以外的景观指数的误差仍然束手无策 ,尽可能地提高遥感数据的分类准确度是唯一力所能及的办法。     

森林生态系统生物物理参数遥感反演研究进展

论述了利用遥感技术反演推算森林生态系统主要生物物理参数：叶面积指数、吸收光合有效辐射、净第一性生产力和生物量的技术、方法和模型及各个参量之间的相互关系研究进展,阐述了各研究方法、模型的特点、优势及局限性。特别论述了净第一性生产力的森林冠层光合作用理论模型基础、微波遥感估算森林生物量的应用优势和理论模型,展示了遥感技术在森林生态学研究中广阔的应用前景,同时也指出现有研究中各生物物理参数的定量遥感估算还有待进一步深入研究。     

Population decline but increased distribution of an invasive ant genotype on a Pacific atoll

Populations of invasive species are often studied when their effects are perceived as a problem. Yet observing the dynamics of populations over longer time periods can highlight changes in effects on invaded communities, and assist with management decisions. In this study we revisit an invasion of the yellow crazy ant (Anoplolepis gracilipes) in the Tokelau archipelago to determine if the distribution and abundance of the ant has changed ~7 years after surveys completed in 2004. We were particularly interested in whether populations of a previously identified invasive haplotype (D) had increased in distribution and abundance, as this haplotype was implicated in negative effects on resident ant communities. Indeed, haplotype D populations have become more widespread since the initial survey, more likely owing to new introductions or movement by humans, rather than intrinsic characteristics of the haplotype. We also found that despite no significant change in the abundance of A. gracilipes overall, haplotype D populations have declined in abundance. Residents of the Tokelau atolls no longer consider the ant to be a pest as they did 7 years ago, when populations of this ant interfered with their food production and many other aspects of daily life. We observed no significant differences between A. gracilipes invaded and uninvaded communities, which suggests that the ant is at a level of abundance below which significant negative ecological effects may occur. Population declines of invasive species are not infrequent, and understanding these population dynamics, particularly the underlying mechanisms promoting population declines or stabilisation, should be a high priority for invasion ecology.     

基于更新和同化策略相结合的遥感信息与水稻生长模型耦合技术的研究

将遥感与作物模型耦合有利于提高作物模型在区域尺度应用时的精度。基于集合平方根滤波算法(Ensemble Square RootFilter,EnSRF)和粒子群优化算法(Particle Swarm Optimization,PSO),以叶面积指数(Leaf Area Index,LAI)和叶片氮积累量(Leaf Nitrogen Accumulation,LNA)共同作为同化耦合点和过程更新点,将同化与更新策略相结合,研究建立了基于遥感信息与水稻生长模型(RiceGrow)耦合的水稻生长与产量预测技术。结果表明,将更新和同化策略结合后,利用RiceGrow模型模拟的水稻生长指标和产量结果更接近于实测值。其中LAI、LNA和产量与实测值间的RMSE分别为0.94、0.47 g/m2和320.15 kg/hm2;RiceGrow模型直接模拟LAI、LNA和产量的RMSE为1.25、1.24 g/m2和516.83 kg/hm2;而单纯基于同化策略模拟LAI、LNA和产量的RMSE为1.01、0.59 g/m2和335.70 kg/hm2。此外,基于该技术的模型区域尺度预测结果能较好地描述水稻生长和产量的时空分布状况,生长指标及区域总产量的模拟相对误差均小于20%。显示基于更新和同化策略相结合的遥感与模型耦合技术具有较高的预测精度,从而为区域尺度作物生长和产量预测提供了技术支撑。     

Factor analysis and classification of remotely sensed data for monitoring tidal flats

Interest in using remote sensing techniques, principally those involving satellite, in Wadden Sea research has centred on attempting a classification of the various sediment surface types present. Unlike most recent studies which have used mainly Landsat Multispectral Scanner data, we have assessed the feasibility of using Landsat Thematic Mapper data, which in conjunction with time series aerial photography, forms the basis of a strategy for remotely sensing the Wadden Sea. This paper focusses on an approach for extracting potentially “hidden” within-pixel information from multispectral data sets. A hierarchical (unsupervised) classification of a Thematic Mapper image successfully classified five different classes, including land, saltmarsh, water, cloud and tidal flat areas. This procedure thus enabled a “masking-out” of all classes other than those classified as tidal flat, following which a factor analysis was used to determine the minimum number of independent factors necessary to explain the observed variation in the signal received by the satellite. Three factors accounted for a total of 82% of the variation in all seven TM channels. Preliminary studies of the primary factor (score) image shows a good correlation with existing latterday cartographic data. Considering the proximate relationship between topography and other important biotic and abiotic sedimentary characteristics, this approach may prove valuable for future applications of satellite data for monitoring long-term change in physical and thus biological Wadden Sea characteristics. Ongoing research efforts are focussing on a classification and quantification of sub-pixel patchiness using aerial photography and ground surveys. The approaches taken and results obtained to date are discussed. Presented at the VI International Wadden Sea Symposium (Biologische Anstalt Helgoland, Wattenmeerstation Sylt, D-2282 List, FRG, 1–4 November 1988)     

A field based statistical approach for validating a remotely sensed mangrove forest classification scheme

Amongst the most threatened ecosystems on Earth, mangrove forests are also one of the more difficult to work in due to their growth in mud and open water coastal zones and their dense tangled stems, branches and prop roots. Consequently, there has been an impetus to employ remotely sensed imagery as a means for rapid inventory of these coastal wetlands. To date, the majority of mangrove maps derived from satellite imagery utilize a simple mangrove classification scheme which does not distinguish mangrove species and may not be useful for conservation and management purposes. Although more elaborate satellite based mangrove classification schemes are being developed, given their enhanced complexity they deserve additional justification for end users. The purpose of this study was to statistically examine the appropriateness of one such classification scheme based on an inventory of field data. In January of 2007 and May of 2008, 61 field sample plots were selected in a stratified random fashion based on a previous classification of a degraded mangrove forest of the Isla La Palma (Sinaloa, Mexico) using Landsat TM5 data. Unlike other previous Landsat TM based classifications of this region, which simply identified the mangrove forests as one class, the mangroves were classified (i.e. mapped) according to four conditions; healthy tall, healthy dwarf, poor condition, and dead mangroves. Within each sample plot, all mangroves of diameter of breast height (dbh) greater than 2.5 cm were identified and their height, condition and dbh recorded. An estimated Leaf Area Index (LAI) value also was obtained for each sample and the shortest distance from the center of each sample plot to open flowing water was determined using a geographic information system (GIS) overlay procedure. These data were then used to calculate mean values for the four classes as well as to determine stem densities, basal areas, and the Shannon–Wiener diversity index. In order to assess the appropriateness of this mangrove classification scheme a discriminant analysis approach was then applied to these field data. The results indicate this forest has undergone severe degradation, with decreasing mean tree heights, mean dbh and species diversity. In regards to the discriminant analysis procedure, further classification of these field plots and cross-validation based on these significant variables provided high classification accuracy thus validating the appropriateness of the satellite based image classification scheme. Moreover, the discriminant analysis indicated that the estimated LAI, mean height, and mean dbh are significant in the separation of the classification of mangrove forest condition along these field sample plots.     

基于多光谱遥感影像的表层土壤有机质空间格局反演

利用多光谱LandSat TM遥感影像反演辽宁省阜新镇表层土壤有机质的空间格局,筛选出与土壤有机质分布相关的TM波段,分析并确定表层土壤有机质含量与TM1、TM2、TM3、TM4、TM5、TM6、TM7波段亮度值(digital number,DN)的相关关系,建立了土壤有机质含量的光谱预测模型.结果表明:研究区表层土壤有机质含量与TM4、TM5波段DN值呈极显著的负相关关系(r分别为-0.617和-0.623,P0.001),与TM3、TM5波段DN值之间满足负二次多项式回归关系(R2=0.9134,P0.001);基于TM3、TM5波段DN值的回归模型对研究区表层土壤有机质含量的预测结果可靠(R2=0.9151,P0.001).研究区表层土壤有机质含量10g·kg-1的农田主要分布在山地边缘地带,而平坦地区农田表层土壤有机质含量一般10g·kg-1,部分达到15～20g·kg-1.     

MODISTools – downloading and processing MODIS remotely sensed data in R

Remotely sensed data – available at medium to high resolution across global spatial and temporal scales – are a valuable resource for ecologists. In particular, products from NASA's MODerate‐resolution Imaging Spectroradiometer (MODIS), providing twice‐daily global coverage, have been widely used for ecological applications. We present MODISTools, an R package designed to improve the accessing, downloading, and processing of remotely sensed MODIS data. MODISTools automates the process of data downloading and processing from any number of locations, time periods, and MODIS products. This automation reduces the risk of human error, and the researcher effort required compared to manual per‐location downloads. The package will be particularly useful for ecological studies that include multiple sites, such as meta‐analyses, observation networks, and globally distributed experiments. We give examples of the simple, reproducible workflow that MODISTools provides and of the checks that are carried out in the process. The end product is in a format that is amenable to statistical modeling. We analyzed the relationship between species richness across multiple higher taxa observed at 526 sites in temperate forests and vegetation indices, measures of aboveground net primary productivity. We downloaded MODIS derived vegetation index time series for each location where the species richness had been sampled, and summarized the data into three measures: maximum time‐series value, temporal mean, and temporal variability. On average, species richness covaried positively with our vegetation index measures. Different higher taxa show different positive relationships with vegetation indices. Models had high R² values, suggesting higher taxon identity and a gradient of vegetation index together explain most of the variation in species richness in our data. MODISTools can be used on Windows, Mac, and Linux platforms, and is available from CRAN and GitHub ( https://github.com/seantuck12/MODISTools ).     

Host tolerance,not symbiont tolerance,determines the distribution of coral species in relation to their environment at a Central Pacific atoll

Tolerance of environmental variables differs between corals and their dinoflagellate symbionts (Symbiodinium spp.), controlling the holobiont’s (host and symbiont combined) resilience to environmental stress. However, the ecological role that environmental variables play in holobiont distribution remains poorly understood. We compared the drivers of symbiont and coral species distributions at Palmyra Atoll, a location with a range of reef environments from low to high sediment concentrations (1–52 g dry weight m⁻² day⁻¹). We observed uniform holobiont partnerships across the atoll (e.g. Montipora spp. with Symbiodinium type C15 at all sites). Multivariate analysis revealed that field-based estimates of settling sediment predominantly explained the spatial variation of coral species among sites (P < 0.01). However, none of the environmental variables measured (sedimentation, temperature, chlorophyll concentration, salinity) affected symbiont distribution. The discord between environmental variables and symbiont distributions suggests that the symbionts are physiologically tolerant of the variable environmental regime across this location and that the distribution of different host–symbiont combinations present is largely dependent on coral rather than Symbiodinium physiology. The data highlight the importance of host tolerance to environmental stressors, which should be considered simultaneously with symbiont sensitivity when considering the impact of variations in environmental conditions on coral communities.     

Combining geostatistical models and remotely sensed data to improve tropical tree richness mapping

Information on the spatial distribution and composition of biological communities is essential in designing effective strategies for biodiversity conservation and management. Reliable maps of species richness across the landscape can be useful tools for these purposes. Acquiring such information through traditional survey techniques is costly and logistically difficult. The kriging interpolation method has been widely used as an alternative to predict spatial distributions of species richness, as long as the data are spatially dependent. However, even when this requirement is met, researchers often have few sampled sites in relation to the area to be mapped. Remote sensing provides an inexpensive means to derive complete spatial coverage for large areas and can be extremely useful for estimating biodiversity. The aim of this study was to combine remotely sensed data with kriging estimates (hybrid procedures) to evaluate the possibility of improving the accuracy of tree species richness maps. We did this through the comparison of the predictive performance of three hybrid geostatistical procedures, based on tree species density recorded in 141 sampling quadrats: co-kriging (COK), kriging with external drift (KED), and regression kriging (RK). Reflectance values of spectral bands, computed NDVI and texture measurements of Landsat 7 TM imagery were used as ancillary variables in all methods. The R² values of the models increased from 0.35 for ordinary kriging to 0.41 for COK, and from 0.39 for simple regression estimates to 0.52 and 0.53 when using simple KED and RK, respectively. The R² values of the models also increased from 0.60 for multiple regression estimates to 0.62 and 0.66 when using multiple KED and RK, respectively. Overall, our results demonstrate that these procedures are capable of greatly improving estimation accuracy, with multivariate RK being clearly superior, because it produces the most accurate predictions, and because of its flexibility in modeling multivariate relationships between tree richness and remotely sensed data. We conclude that this is a valuable tool for guiding future efforts aimed at conservation and management of highly diverse tropical forests.     

Assessing species density and abundance of tropical trees from remotely sensed data and geostatistics

Question: What relationships exist between remotely sensed measurements and field observations of species density and abundance of tree species? Can these relationships and spatial interpolation approaches be used to improve the accuracy of prediction of species density and abundance of tree species? Location: Quintana Roo, Yucatan peninsula, Mexico. Methods: Spatial prediction of species density and abundance of species for three functional groups was performed using regression kriging, which considers the linear relationship between dependent and explanatory variables, as well as the spatial dependence of the observations. These relationships were explored using regression analysis with species density and abundance of species of three functional groups as dependent variables, and reflectance values of spectral bands, computed NDVI (normalized difference vegetation index), standard deviation of NDVI and texture measurements of Landsat 7 Thematic Mapper (TM) imagery as explanatory variables. Akaike information criterion was employed to select a set of candidate models and calculate model‐averaged parameters. Variogram analysis was used to analyze the spatial structure of the residuals of the linear regressions. Results: Species density of trees was related to reflectance values of TM4, NDVI and spatial heterogeneity of land cover types, while the abundance of species in functional groups showed different patterns of association with remotely sensed data. Models that accounted for spatial autocorrelation improved the accuracy of estimates in all cases. Conclusions: Our approach can substantially increase the accuracy of the spatial estimates of species richness and abundance of tropical tree species and can help guide and evaluate tropical forest management and conservation.     

植被叶面积指数遥感反演的尺度效应及空间变异性

遥感作为宏观生态学研究中数据获取的一种便捷手段,有助于把握较大尺度内生态学现象的特征.应用遥感数据反演LAI时,由于像元的异质性,不同尺度遥感数据之间的转换是遥感发展的一个重要问题.以河北省黄骅市为研究区,在利用TM和MODIS遥感数据对芦苇LAI反演误差产生原因进行分析的基础上,利用半变异函数对像元空间异质性进行了定量描述.发现NDVI算法的非线性带给LAI尺度转换的误差很小,而LAI的空间异质性则是引起LAI尺度效应的根本原因.并且当像元内空间异质性很大时半变异函数的基台值比纯像元要大得多,空间自相关的程度是引起LAI尺度转换误差的主要原因;反之,像元内空间异质性不大时,随机误差是引起LAI尺度转换误差的主要原因.当像元为纯像元时,由像元异质性引起的反演误差基本可以忽略.此外,研究区芦苇的空间相关有效尺度约为360m,超过此距离空间相关性则不复存在.     

Drought footprint on European ecosystems between 1999 and 2010 assessed by remotely sensed vegetation phenology and productivity

Drought affects more people than any other natural disaster but there is little understanding of how ecosystems react to droughts. This study jointly analyzed spatio‐temporal changes of drought patterns with vegetation phenology and productivity changes between 1999 and 2010 in major European bioclimatic zones. The Standardized Precipitation and Evapotranspiration Index (SPEI) was used as drought indicator whereas changes in growing season length and vegetation productivity were assessed using remote sensing time‐series of Normalized Difference Vegetation Index (NDVI). Drought spatio‐temporal variability was analyzed using a Principal Component Analysis, leading to the identification of four major drought events between 1999 and 2010 in Europe. Correspondence Analysis showed that at the continental scale the productivity and phenology reacted differently to the identified drought events depending on ecosystem and land cover. Northern and Mediterranean ecosystems proved to be more resilient to droughts in terms of vegetation phenology and productivity developments. Western Atlantic regions and Eastern Europe showed strong agglomerations of decreased productivity and shorter vegetation growing season length, indicating that these ecosystems did not buffer the effects of drought well. In a climate change perspective, increase in drought frequency or intensity may result in larger impacts over these ecosystems, thus management and adaptation strategies should be strengthened in these areas of concerns.     

Calcium dynamics in microbialite‐forming exopolymer‐rich mats on the atoll of Kiritimati,Republic of Kiribati,Central Pacific

Microbialite‐forming microbial mats in a hypersaline lake on the atoll of Kiritimati were investigated with respect to microgradients, bulk water chemistry, and microbial community composition. O₂, H₂S, and pH microgradients show patterns as commonly observed for phototrophic mats with cyanobacteria‐dominated primary production in upper layers, an intermediate purple layer with sulfide oxidation, and anaerobic bottom layers with sulfate reduction. Ca²⁺ profiles, however, measured in daylight showed an increase of Ca²⁺ with depth in the oxic zone, followed by a sharp decline and low concentrations in anaerobic mat layers. In contrast, dark measurements show a constant Ca²⁺ concentration throughout the entire measured depth. This is explained by an oxygen‐dependent heterotrophic decomposition of Ca²⁺‐binding exopolymers. Strikingly, the daylight maximum in Ca²⁺ and subsequent drop coincides with a major zone of aragonite and gypsum precipitation at the transition from the cyanobacterial layer to the purple sulfur bacterial layer. Therefore, we suggest that Ca²⁺ binding exopolymers function as Ca²⁺ shuttle by their passive downward transport through compression, triggering aragonite precipitation in the mats upon their aerobic microbial decomposition and secondary Ca²⁺ release. This precipitation is mediated by phototrophic sulfide oxidizers whose action additionally leads to the precipitation of part of the available Ca²⁺ as gypsum.     

遥感反演植被理化参数的光谱和空间尺度效应

植被理化参数是生态系统中碳和养分等物质循环与能量交换的重要指标,利用遥感技术反演是获取区域及全球植被理化参数的重要手段,但光谱和空间尺度效应的存在,限制了源自不同遥感传感器植被理化参数产品的统一应用。阐述了遥感反演植被理化参数光谱尺度效应的概念及其产生原因,主要从光谱波段位置和波段宽度两方面对国内外相关研究进行了介绍和评述。同时,从遥感反演植被理化参数的空间尺度效应产生原因、空间异质性描述方法和空间尺度转换方法等方面对其国内外研究现状进行了归纳和评述。最后,总结了遥感反演植被理化参数光谱和空间尺度效应研究的不足之处和发展趋势,并指出光谱和空间耦合效应的研究将是一大趋势,而在生态学等领域形成的尺度效应研究的理论和方法也值得借鉴参考。