Trade-off between number and length of remote videos for rapid assessments of reef fish assemblages

Remote underwater videos are widely employed to assess the structure and composition of reef fish assemblages but the sampling effort employed on each survey differs considerably, indicating that both the number of assessments and video length could be optimized. We searched for this optimal sampling effort in remote video samples to conduct rapid assessments of community composition and discussed the relation between number of replicates and video length, and how it impacts the method's efficiency to characterize species assemblages. Remote video recordings from tropical reefs in northeastern Brazil were used to investigate how fish species richness and composition builds across time and number of assays. Videos as short as 5 min successfully recorded species richness, requiring about five repetitions to record most species that compose 80% of the total biomass. Recording species composition required even less time in these reefs, setting a minimum of 3 min with the same five videos. By comparing the detected richness per analysed time unit, we found several shorter videos recorded for more species than a few longer videos, indicating that increasing the sampling coverage in the reef area might be better than just extending the video length for rapid assessments.     

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 ).     

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.     

基于多光谱影像的森林树种识别及其空间尺度响应

当前,不同空间分辨率卫星影像对森林类型识别结果中普遍存在的尺度效应,而且纹理参量对不同尺度下树种识别精度的影响仍缺乏广泛认知.本研究以中国东北旺业甸林场为研究区,采用观测时相同步、地理坐标匹配的GF-1 PMS、GF-2 PMS、GF-1 WFV,以及Landsat-8 OLI卫星传感器数据组成空间尺度观测序列(1、2、4、8、16、30 m),并结合支持向量机(SVM)模型,探讨了区域内5种优势树种遥感识别结果的尺度变化规律及其纹理特征参数的影响,同时检验了基于尺度上推转换影像的树种识别结果差异.结果表明: 影像空间分辨率对区域树种识别结果具有显著影响,其中,研究区森林树种识别的最佳影像分辨率为4 m,当分辨率降低至30 m时,树种识别结果最差.在1～8 m影像分辨率范围内,增加纹理信息能够显著提高不同优势树种的识别精度,使总分类精度提升了2.0%～3.6%,但纹理信息对16～30 m影像的识别结果没有显著影响.与真实尺度卫星影像相比,基于升尺度转换影像的树种识别结果及其尺度响应特征存在显著差异,表明在面向多个空间尺度的遥感观测和应用研究中,需要采用真实分辨率影像以确保结果的准确性.     

Assessing fragmentation and disturbance of west Kenyan rainforests by means of remotely sensed time series data and landscape metrics

Biodiversity in tropical rainforests is heavily influenced by land use/cover change (LUCC), but so far there have been few LUCC studies conducted in Africa. We present several methods that make use of remotely sensed data and landscape metrics and allow for assessment of the development of land cover and thus forest fragmentation and disturbance over a substantial period of time. The study covers Kakamega Forest and its associated forest areas in western Kenya, over the last 30 years. The accuracy of a supervised multispectral classification of Landsat time series data encompassing seven time steps between 1972 and 2001 is numerically assessed using ground truth reference data considering the 2001 time step. Here, buffering the forest areas by 1 km, highest user's accuracies for the forest classes ‘near natural + old secondary forest’ (87.50%), ‘secondary forest’ (80.00%) and ‘bushland/shrubs’ (81.08%) are revealed. Images of a spatially distributed fragmentation index derived from the land cover time series by applying a three by 3 pixel‐sized moving window to determine forest pixels’ adjacency, highlight trends in forest fragmentation, e.g. the splitting into two separate forests along the Yala/Ikuywa corridor. Calculations of mean fragmentation indices for the Biodiversity Monitoring Transect Analysis in Eastern Africa (BIOTA‐East Africa) focus research areas are used to evaluate the fragmentation index and to demonstrate its potential to extrapolate (e.g. biological) field findings in space and time. Here we argue for a correlation of the fragmentation indices results not only with forest management regimes, but with population distribution and accessibility (e.g. by roads). A cluster analysis applying the isodata‐algorithm on the classification results of all seven times steps allows for a rapid visual assessment of the distinct pattern of typical land cover development trends since 1972. This reveals that parts of Kakamega Forest have experienced severe forest loss while others, especially in the north‐east, show signs of succession.     

Evaluation of methods for estimating macroinvertebrate species richness using individual stones in tropical streams

1. The most straightforward way to assess diversity in a site is the species count. However, a relatively large sample is needed for a reliable result because of the presence of many rare species in rich assemblages. The use of richness estimation methods is suggested by many authors as a solution for this problem in many cases.
2. We examined the performance of 13 methods for estimating richness of stream macroinvertebrates inhabiting riffles both at local (stream) and regional (catchment) scales. The evaluation was based on (1) the smallest sub-sample size needed to estimate total richness in the sample, (2) constancy of this size, (3) lack of erratic behaviour in curve shape and (4) similarity in curve shape through different data sets. Samples were from three single stream sites (local) and three from several streams within the same catchment basin (regional). All collections were made from protected forest areas in south-east Brazil.
3. All estimation methods were dependent on sub-sample size, producing higher estimates when using larger sub-sample sizes. The Stout and Vandermeer method estimated total richness in the samples with the smallest sub-sample size, but showed some erratic behaviour at small sub-sample sizes, and the estimated curves were not similar among the six samples. The Bootstrap method was the best estimator in relation to constancy of sub-sample sizes, but needed an unacceptably large sub-sample to estimate total richness in the samples. The second order Jackknife method was the second best estimator both for minimum sub-sample size and constancy of this size and we suggest its use in future studies of diversity in tropical streams. Despite the inferior performance of several other methods, some produced acceptable results. Comments are made on the utility of using these estimators for predicting species richness in an area and for comparative purposes in diversity studies.     

Evaluating the performance of species richness estimators: sensitivity to sample grain size

1. Fifteen species richness estimators (three asymptotic based on species accumulation curves, 11 nonparametric, and one based in the species-area relationship) were compared by examining their performance in estimating the total species richness of epigean arthropods in the Azorean Laurisilva forests. Data obtained with standardized sampling of 78 transects in natural forest remnants of five islands were aggregated in seven different grains (i.e. ways of defining a single sample): islands, natural areas, transects, pairs of traps, traps, database records and individuals to assess the effect of using different sampling units on species richness estimations. 2. Estimated species richness scores depended both on the estimator considered and on the grain size used to aggregate data. However, several estimators (ACE, Chao 1, Jackknifel and 2 and Bootstrap) were precise in spite of grain variations. Weibull and several recent estimators [proposed by Rosenzweig et al. (Conservation Biology, 2003, 17, 864-874), and Ugland et al. (Journal of Animal Ecology, 2003, 72, 888-897)] performed poorly. 3. Estimations developed using the smaller grain sizes (pair of traps, traps, records and individuals) presented similar scores in a number of estimators (the above-mentioned plus ICE, Chao2, Michaelis-Menten, Negative Exponential and Clench). The estimations from those four sample sizes were also highly correlated. 4. Contrary to other studies, we conclude that most species richness estimators may be useful in biodiversity studies. Owing to their inherent formulas, several nonparametric and asymptotic estimators present insensitivity to differences in the way the samples are aggregated. Thus, they could be used to compare species richness scores obtained from different sampling strategies. Our results also point out that species richness estimations coming from small grain sizes can be directly compared and other estimators could give more precise results in those cases. We propose a decision framework based on our results and on the literature to assess which estimator should be used to compare species richness scores of different sites, depending on the grain size of the original data, and of the kind of data available (species occurrence or abundance data).     

Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness

Species richness is a fundamental measurement of community and regional diversity, and it underlies many ecological models and conservation strategies. In spite of its importance, ecologists have not always appreciated the effects of abundance and sampling effort on richness measures and comparisons. We survey a series of common pitfalls in quantifying and comparing taxon richness. These pitfalls can be largely avoided by using accumulation and rarefaction curves, which may be based on either individuals or samples. These taxon sampling curves contain the basic information for valid richness comparisons, including category–subcategory ratios (species-to-genus and species-to-individual ratios). Rarefaction methods – both sample-based and individual-based – allow for meaningful standardization and comparison of datasets. Standardizing data sets by area or sampling effort may produce very different results compared to standardizing by number of individuals collected, and it is not always clear which measure of diversity is more appropriate. Asymptotic richness estimators provide lower-bound estimates for taxon-rich groups such as tropical arthropods, in which observed richness rarely reaches an asymptote, despite intensive sampling. Recent examples of diversity studies of tropical trees, stream invertebrates, and herbaceous plants emphasize the importance of carefully quantifying species richness using taxon sampling curves.     

Performance of nonparametric species richness estimators in a high diversity plant community

Abstract. The efficiency of four nonparametric species richness estimators — first‐order Jackknife, second‐order Jackknife, Chao2 and Bootstrap — was tested using simulated quadrat sampling of two field data sets (a sandy ‘Dune’ and adjacent ‘Swale’) in high diversity shrublands (kwongan) in south‐western Australia. The data sets each comprised > 100 perennial plant species and > 10 000 individuals, and the explicit (x‐y co‐ordinate) location of every individual. We applied two simulated sampling strategies to these data sets based on sampling quadrats of unit sizes 1/400th and 1/100th of total plot area. For each site and sampling strategy we obtained 250 independent sample curves, of 250 quadrats each, and compared the estimators’ performances by using three indices of bias and precision: MRE (mean relative error), MSRE (mean squared relative error) and OVER (percentage overestimation). The analysis presented here is unique in providing sample estimates derived from a complete, field‐based population census for a high diversity plant community. In general the true reference value was approached faster for a comparable area sampled for the smaller quadrat size and for the swale field data set, which was characterized by smaller plant size and higher plant density. Nevertheless, at least 15–30% of the total area needed to be sampled before reasonable estimates of S_t (total species richness) were obtained. In most field surveys, typically less than 1% of the total study domain is likely to be sampled, and at this sampling intensity underestimation is a problem. Results showed that the second‐order Jackknife approached the actual value of S_t more quickly than the other estimators. All four estimators were better than S_obs (observed number of species). However, the behaviour of the tested estimators was not as good as expected, and even with large sample size (number of quadrats sampled) all of them failed to provide reliable estimates. First‐ and second‐order Jackknives were positively biased whereas Chao2 and Bootstrap were negatively biased. The observed limitations in the estimators’ performance suggests that there is still scope for new tools to be developed by statisticians to assist in the estimation of species richness from sample data, especially in communities with high species richness.     

The effects of pitfall trap diameter on ant species richness (Hymenoptera: Formicidae) and species composition of the catch in a semi-arid eucalypt woodland

Abstract Ants play an important role in Australian biodiversity and environmental impact assessments, with pitfall-trapping being the principal sampling method. However, the relationship between trap diameter and ant species catch has not been investigated in the context of survey design. Using four different trap diameters, each at a density of one trap per 100 m2, the present study asks three questions: (i) given an equal number of traps, do traps with larger diameters catch more species than smaller-diameter traps?; (ii) do traps with small diameters bias against large or rare species?; (iii) for equal area of the trap mouth, do small but more numerous traps catch more species than fewer but large traps? A total of 84 species were sampled within the 1600 m2 study site, with numbers of species for trap diameters of: 18mm (46 species), 42mm (56 species), 86mm (62 species) and 135mm (64 species). At equal trap density, 18 mm traps caught significantly fewer species than larger traps. Traps of 86 mm and 135mm were no more efficient than 42mm traps. Only 86mm and 135mm traps caught all species > 10mm in length (6 species). For equal area of the trap mouth, small traps were more efficient than large traps. Differences in the catch of the different-sized traps were due primarily to different capture rates of the rare species (40 species): 18mm traps caught 25% of rare species, 42 mm caught 41%, 86 mm caught 44% and 135 mm caught 52%. The role of rare ant species in environmental impact studies is discussed.     

Multi-scale altitudinal patterns in species richness of land snail communities in south-eastern France

Aim Species richness is an important feature of communities that varies along elevational gradients. Different patterns of distribution have been described in the literature for various taxonomic groups. This study aims to distinguish between species density and species richness and to describe, for land snails in south‐eastern France, the altitudinal patterns of both at different spatial scales. Location The study was conducted on five calcareous mountains in south‐eastern France (Etoile, Sainte Baume, Sainte Victoire, Ventoux and Queyras). Methods Stratified sampling according to vegetation and altitude was undertaken on five mountains, forming a composite altitudinal gradient ranging from 100 to 3100 m. Visual searching and analysis of turf samples were undertaken to collect land snail species. Species density is defined as the number of species found within quadrats of 25 m². Species richness is defined as the number of species found within an elevation zone. Different methods involving accumulation curves are used to describe the patterns in species richness. Elevation zones of different sizes are studied. Results Eighty‐seven species of land snails were recovered from 209 samples analysed during this study. Land snail species density, which can vary between 29 and 1 species per 25 m², decreases logarithmically with increasing altitude along the full gradient. However, on each mountain separately, only a linear decrease is observable. The climatic altitudinal gradient can explain a large part of this pattern, but the great variability suggests that other factors, such as heterogeneity of ground cover, also exert an influence on species density. The altitudinal pattern of species richness varies depending on the spatial resolution of the study. At fine resolution (altitudinal zones of 100 m) land snail species richness forms a plateau at altitudes below 1000 m, before decreasing with increasing altitude. At coarse resolution (altitudinal zones of 500 and 1000 m) the relationship becomes linear. Main conclusions This study reveals that land snail species density and land snail species richness form two different altitudinal patterns. Species density exhibits strong variability between sites of comparable altitude. A large number of samples seem necessary to study altitudinal patterns of species density. Species density decreases logarithmically with increasing altitude. Above a critical altitudinal threshold, this decrease lessens below the rate seen in the first 1500 m. Different methods exist to scale‐up species density to species richness but these often produce different patterns. In this study, the use of accumulation curves has yielded a pattern of species richness showing a plateau at low altitude, whereas simple plotting of known altitudinal ranges from single mountains would have produced stronger mid‐altitudinal peaks. This study shows that not only factors such as temperatures and habitat heterogeneity, but also an ecotone effect, are responsible for the observed patterns.     

森林生态系统生物多样性的遥感评估

目前的评估由于方法的原因,常常会遗留下一些含糊不清的地方。遥感则可以作一种观察生态系统多样性和单个生态系统中各种结构侧面的重要工具。它提供了一种能够跨越几个不同空间尺度实施评估的手段,并且对于评估生态系统格局随时间的变化也是必不可少。现在许多不同的遥感技术已经被应用在生态学研究中。大多数工作所用的数据主要是来自机载和星载平台提供的摄影和数字光学图像,目前则越来越强调激光扫描和合成孔径雷达数据的应用。这些技术手段为从景观到林分规模的不同现象的评估提供了机会。遥感提供了可用于确定森林生态系统中生物多样性景观尺度的元素最有效的工具。例如基质和斑块的相对百分比以及它们的配置。在中间尺度,遥感为评价廊道的存在和边界的特性提供了理想的工具。在林分尺度,遥感技术可用来获取关于森林分结构属性的信息,例如冠层表面的特性,是否存在冠内分层等等。随着的发展,遥感将更广泛地应用于生态学研究。     

The second Southern African Bird Atlas Project: Causes and consequences of geographical sampling bias

Using the Southern African Bird Atlas Project (SABAP2) as a case study, we examine the possible determinants of spatial bias in volunteer sampling effort and how well such biased data represent environmental gradients across the area covered by the atlas. For each province in South Africa, we used generalized linear mixed models to determine the combination of variables that explain spatial variation in sampling effort (number of visits per 5′ × 5′ grid cell, or “pentad”). The explanatory variables were distance to major road and exceptional birding locations or “sampling hubs,” percentage cover of protected, urban, and cultivated area, and the climate variables mean annual precipitation, winter temperatures, and summer temperatures. Further, we used the climate variables and plant biomes to define subsets of pentads representing environmental zones across South Africa, Lesotho, and Swaziland. For each environmental zone, we quantified sampling intensity, and we assessed sampling completeness with species accumulation curves fitted to the asymptotic Lomolino model. Sampling effort was highest close to sampling hubs, major roads, urban areas, and protected areas. Cultivated area and the climate variables were less important. Further, environmental zones were not evenly represented by current data and the zones varied in the amount of sampling required representing the species that are present. SABAP2 volunteers' preferences in birding locations cause spatial bias in the dataset that should be taken into account when analyzing these data. Large parts of South Africa remain underrepresented, which may restrict the kind of ecological questions that may be addressed. However, sampling bias may be improved by directing volunteers toward undersampled regions while taking into account volunteer preferences.     

Qualitative distinction of congeneric and introgressive mangrove species in mixed patchy forest assemblages using high spatial resolution remotely sensed imagery (IKONOS)

This paper is a preliminary report of the ability of IKONOS multispectral satellite imagery with a very high spatial resolution of 1 metre to distinguish two mangrove species in Sri Lanka belonging to the same genus (Rhizophora apiculata and R. mucronata). Not only is this an advancement for the monitoring of forests, it is even more important considering their patchy nature in Sri Lankan mangroves (in contrast to classically zoned forests). Apart from congeneric distinction, intro‐gressive species (Acrostichum aureum) can also be detected from IKONOS imagery, which is important in the early warning for cryptic ecological changes that may affect mangrove species composition (both floral and faunal) and functioning. The results tabulate the usage of various image composites, transformations and classifications, and indicate the danger of too much detail in remote sensing, and the need to apply an optimum resolution. We also highlight that the highest resolutions (as in pansharpened multispectral composites) remain invaluable for visual ecological investigations, which are not at all outdated by new digital satellite images of (sub)metre spatial resolution and their possibility for computer‐aided analysis.     

Comparison of leaf area index inversion for grassland vegetation through remotely sensed spectra by unmanned aerial vehicle and field-based spectroradiometer

Aims Remote sensing technology has been proved useful in mapping grassland vegetation properties. Spectral features of vegetation cover can be recorded by optical sensors on board of different platforms. With increasing popularity of applying unmanned aerial vehicle (UAV) to mapping plant cover, the study aims to investigate the possible applications and potential issues related to mapping leaf area index (LAI) through integration of remote sensing imagery collected by multiple sensors.     

Measuring species diversity while counting large mammals: comparison of methods using species-accumulation curves

With a growing need for wildlife conservation and management in the communal lands of Africa, comprehensive ecological monitoring tools need to be developed and evaluated. While wildlife census methods are often compared in terms of precision and accuracy to estimate the population size of various target species, little attention has been paid to the measure of species diversity in mammal communities. A combined measure of abundance and community composition is, however, a crucial source of information in determining conservation priorities and to evaluate the ecosystem responses to management activities. In this study, we present five census methods of large to medium‐sized mammals and compare their efficacy in measuring species diversity. A species accumulation curve analysis is used with a predictive model to estimate the local species richness, the level of completeness of our censuses as well as the effort required to carry out a census. Advantages and limits of each method are discussed through comparison of their respective measure of species richness and their species accumulation rate. Results illustrate a large difference between methods in the ability for species detection, with censuses completed by bicycle offering the best option within the context of a unprotected area.     

Diversity reduces invasibility in experimental plant communities: the role of plant species

Several studies have presented experimental evidence that diversity reduces invasibility in grassland communities. The interpretation of these results has been disputed recently and it was proposed that sampling effects were responsible for the observed decrease of invasibility with diversity. The experiments performed to date were not designed to adequately separate sampling from diversity effects. Using the establishment of native plant species in experimental plant communities as a model of invasibility, we show that the number of invaders decreased with increasing diversity. When the presence of particular species is included, their effects are dominant. Centaurea jacea showed a strong effect at low diversity, whereas Leucanthemum vulgare showed a very strong negative impact at each diversity level. The negative effect of the latter might be related to root‐feeding nematodes that showed far higher abundance in plots with Leucanthemum. However, diversity remained a significant factor in determining the number of invading species and the numbers of an abundant invader.     

基于多源遥感数据的植物物种分类与识别: 研究进展与展望

物种分类与识别是生物多样性监测的基础, 明确物种的类别及其分布是解决几乎所有生态学问题的前提。为深入了解基于多源遥感数据的植物物种分类与识别相关研究的发展现状和存在的问题, 本文对2000年以来该领域的研究进行了总结分析, 发现: 当前大多数研究集中在欧洲和北美地区的温带或北方森林以及南非的热带稀树草原; 使用最多的遥感数据是机载高光谱数据, 而激光雷达作为补充数据, 通过单木分割及提供单木的三维垂直结构信息, 显著提高了分类精度; 支持向量机和随机森林作为应用最广的非参数分类算法, 平均分类精度达80%; 随着计算机技术及机器学习领域的不断成熟, 人工神经网络在物种识别领域得以迅速发展。基于此, 本文对目前基于遥感数据的植物物种分类与识别中在分类对象复杂性、多源遥感数据整合、植物物候与纹理特征整合和分类算法技术等方面面临的挑战进行了总结, 并建议通过整合多时相监测数据、高光谱和激光雷达数据、短波红外等特定波谱信息、采用深度学习等方法来提高分类精度。