On sampling procedures in population and community ecology

In this paper we emphasize that sampling decisions in population and community ecology are context dependent. Thus, the selection of an appropriate sampling procedure should follow directly from considerations of the objectives of an investigation. We recognize eight sampling alternatives, which arise as a result of three basic dichotomies: parameter estimation versus pattern detection, univariate versus multivariate, and a discrete versus continuous sampling universe. These eight alternative sampling procedures are discussed as they relate to decisions regarding the required empirical sample size, the selection or arrangement of sampling units, and plot size and shape. Our results indicate that the decision-making process in sampling must be viewed as a flexible exercise, dictated not by generalized recommendations but by specific objectives: there is no panacea in ecological sampling. We also point to a number of unresolved sampling problems in ecology.     

西双版纳热带雨林林窗空间分布格局及其特征数与林窗下植物多样性的相关性

林窗作为森林群落中一种重要的干扰方式, 对林下物种构成有着重要的影响。开展林窗空间格局及其特征指数与林下植物多样性关系研究对于探讨林窗对林下生物多样性的影响有重要意义, 有助于进一步了解群落动态, 在物种多样性保护方面也具有指导作用。本研究在西双版纳热带雨林地区随机选取3块大小为1 ha的热带雨林为研究样地, 采用轻小型六旋翼无人机搭载Sony ILCE-A7r可见光传感器, 分别获取各个样地的高清数字影像, 结合数字表面高程模型以及各个样地的地形数据用以确定各样区的林窗分布格局, 并进一步提取出各林窗的景观格局指数。结合地面样方基础调查数据, 对各样地各林窗下植物多样性情况进行统计, 旨在分析热带雨林林窗空间分布格局以及林窗下植物多样性对各林窗空间格局特征的响应情况。研究表明, 西双版纳州热带雨林林窗呈大而分散的空间分布, 林窗空间格局特征指数如林窗形状复杂性指数、林窗面积都与林下植物多样性呈显著正相关关系。在面积小的林窗下, 较之林窗形状复杂性因子, 林窗面积大小对林下植物多样性影响更显著; 在面积达到一定程度后, 相对于面积因子, 林窗形状复杂性指数对林下植物多样性影响更显著, 各样地林窗皆趋于向各自所处样地顶极群落发展。     

Spatial scale and sampling size affect the concordance between remotely sensed information and plant community discrimination in restoration monitoring

Remote sensing cartography and GIS are part of ordinary practice in restoration ecology in discriminating patches of habitats, defining objectives, and planning the monitoring phase, but derived information is not always consistent with field survey. We assessed the mapping process efficiency in discriminating different communities, relying on plant composition data, and considering the effect of sample size and plot dimension (grain), in a heterogeneous environment in Tuscany (central Italy). We identified four land cover classes on a land cover map produced with object-oriented technique; hence we conducted a sampling of 64 plots (4 zones × 4 classes × 4 plots), estimating vascular plant cover using a point-quadrant method. Plots were nested squares with side lengths of 0.50 m, 1 m and, limited to a sub-sample, 2 m. We evaluated the effect of sample size and grain using permutational multivariate analysis of variance (PERMANOVA), testing the simultaneous response of species composition compared to land cover classes. Results demonstrated that for a sample size of 64 plots, grain does not influence the ability of discriminating among the habitat types investigated, while for a smaller sub-sample the effect of grain is significant and communities cannot be distinguished at all plot dimensions. Outcomes corroborate the hypothesis that sampling at a series of scales of observations and an adequate sample size can improve monitoring efficiency in restoration ecology. Nomenclature: Pignatti (1982) and Conti et al (2005) for Festuca.     

Phytosociological data give biased estimates of species richness

Abstract. Large phytosociological data sets of three types of grassland and three types of forest vegetation from the Czech Republic were analysed with a focus on plot size used in phytosociological sampling and on the species‐area relationship. The data sets included 12975 relevés, sampled by different authors in different parts of the country between 1922 and 1999. It was shown that in the grassland data sets, the relevés sampled before the 1960s tended to have a larger plot size than the relevés made later on. No temporal variation in plot sizes used was detected in forest relevés. Species‐area curves fitted to the data showed unnatural shapes, with levelling‐off or even decrease in plot sizes higher than average. This distortion is explained by the subjective, preferential method of field sampling used in phytosociology. When making relevés in species‐poor vegetation, researchers probably tend to use larger plots in order to include more species. The reason for this may be that a higher number of species gives a higher probability of including presumed diagnostic species, so that the relevé can be more easily classified in the Braun‐Blanquet classification system. This attitude of phytosociologists has at least two consequences: (1) in phytosociological data bases species‐poor vegetation types are underrepresented or relevés are artificially biased towards higher species richness; (2) the suitability of phytosociological data for species richness estimation is severely limited.     

Effect of sampling strategy on estimation of fine-scale spatial genetic structure in Androsace tapete (Primulaceae), an alpine plant endemic to Qinghai-Tibetan Plateau

The fine-scale spatial genetic structure (SGS) of alpine plants is receiving increasing attention, from which seed and pollen dispersal can be inferred. However, estimation of SGS may depend strongly on the sampling strategy,including the sample size and spatial sampling scheme. Here, we examined the effects of sample size and three spatial schemes, simple-random, line-transect, and random-cluster sampling, on the estimation of SGS in Androsace tapete, an alpine cushion plant endemic to Qinghai-Tibetan Plateau. Using both real data and simulated data of dominant molecular markers, we show that: (i) SGS is highly sensitive to sample strategy especially when the sample size is small (e.g., below 100); (ii) the commonly used SGS parameter (the intercept of the autocorrelogram) is more susceptible to sample error than a newly developed Sp statistic; and (iii) the random-cluster scheme is susceptible to obvious bias in parameter estimation even when the sample size is relatively large (e.g., above 200). Overall,the line-transect scheme is recommendable, in that it performs slightly better than the simple-random scheme in parameter estimation and is more efficient to encompass broad spatial scales. The consistency between simulated data and real data implies that these findings might hold true in other alpine plants and more species should be examined in future work.     

Effect of sampling strategy on estimation of fine‐scale spatial genetic structure in Androsace tapete (Primulaceae), an alpine plant endemic to Qinghai‐Tibetan Plateau

Abstract The fine‐scale spatial genetic structure (SGS) of alpine plants is receiving increasing attention, from which seed and pollen dispersal can be inferred. However, estimation of SGS may depend strongly on the sampling strategy, including the sample size and spatial sampling scheme. Here, we examined the effects of sample size and three spatial schemes, simple‐random, line‐transect, and random‐cluster sampling, on the estimation of SGS in Androsace tapete, an alpine cushion plant endemic to Qinghai‐Tibetan Plateau. Using both real data and simulated data of dominant molecular markers, we show that: (i) SGS is highly sensitive to sample strategy especially when the sample size is small (e.g., below 100); (ii) the commonly used SGS parameter (the intercept of the autocorrelogram) is more susceptible to sample error than a newly developed Sp statistic; and (iii) the random‐cluster scheme is susceptible to obvious bias in parameter estimation even when the sample size is relatively large (e.g., above 200). Overall, the line‐transect scheme is recommendable, in that it performs slightly better than the simple‐random scheme in parameter estimation and is more efficient to encompass broad spatial scales. The consistency between simulated data and real data implies that these findings might hold true in other alpine plants and more species should be examined in future work.     

Effects of flower and seed predators and pollinators on fruit production in two sequentially flowering congeners

We examined spatial and temporal variability of understory herbaceousvegetation on opposing north- and south-facing slopes in an eastern deciduousold-growth forest in southeastern Ohio, USA. Secondly, we explored theinfluenceof sampling scale and analytical technique on our assessment of diversitypatterns. The influence of aspect and seasonality were examined at varyingsampling scales using observed richness, evenness, andH diversity measures, non-parametric richnessestimators, species-area curves, and SHE analysis. Herb layer composition,abundance, and diversity were strongly influenced by location (north slope vs.south slope), seasonal sampling period (April, June, August), and plot size(micro (2 m²)- vs. meso (70m²)-scale samples). Although north and south plots werecompositionally distinct, they followed similar courses of change through thegrowing season. Richness, evenness, and H diversitywere generally greater on the south plot whereas herbaceous abundance wasgreater on the north plot. Species composition and diversity showed markedphenological (temporal) changes, and comparison of diversity measures at micro-and meso-scales produced markedly different results. Minimum sample areas of150–200 m² were needed to evaluate micro-scalerichness in these species rich communities, suggesting that forest understoriesmay be frequently undersampled in ecological studies. Comparison of observedandestimated meso-scale richness also suggested underestimation of richness in thenorth plot, particularly earlier in the growing season. Thus, sample size,area,and time of sampling appear critical to assessment of diversity in spatiallyandtemporally variable communities such as herbaceous forest understories.     

A neutral sampling formula for multiple samples and an 'exact' test of neutrality

As the utility of the neutral theory of biodiversity is increasingly being recognized, there is also an increasing need for proper tools to evaluate the relative importance of neutral processes (dispersal limitation and stochasticity). One of the key features of neutral theory is its close link to data: sampling formulas, giving the probability of a data set conditional on a set of model parameters, have been developed for parameter estimation and model comparison. However, only single local samples can be handled with the currently available sampling formulas, whereas data are often available for many small spatially separated plots. Here, I present a sampling formula for multiple, spatially separated samples from the same metacommunity, which is a generalization of earlier sampling formulas. I also provide an algorithm to generate data sets with the model and I introduce a general test of neutrality that does not require an alternative model; this test compares the probability of the observed data (calculated using the new sampling formula) with the probability of model-generated data sets. I illustrate this with tree abundance data from three large Panamanian neotropical forest plots. When the test is performed with model parameters estimated from the three plots, the model cannot be rejected; however, when parameter estimates previously reported for BCI are used, the model is strongly rejected. This suggests that neutrality cannot explain the structure of the three Panamanian tree communities on the local (BCI) and regional (Panama Canal Zone) scale simultaneously. One should be aware, however, that aspects of the model other than neutrality may be responsible for its failure. I argue that the spatially implicit character of the model is a potential candidate.     

Sampling Techniques Influence Understory Plant Trajectories After Restoration: An Example from Ponderosa Pine Restoration

Abstract Although there is no one correct technique for sampling vegetation, the sampling design chosen may greatly influence the conclusions researchers can draw from restoration treatments. Considerations when designing vegetation sampling protocol include determining what sampling attributes to measure, the size and shape of the sampling plot, the number of replicates and their location within the study area, and the frequency of sampling. We installed 20 point‐intercept transects (50‐m long), 8 belt transects (10 × 50 m), 10 adapted Daubenmire transects (four 0.5 × 2‐m plots), and 4 modified‐Whittaker plots (20 × 50 m with smaller nested plots) in treatment and control units to measure understory herbaceous response in a forest restoration experiment that tested different treatments. Point‐intercept transects on average recorded at least twice as much plant cover as did adapted Daubenmire transects and modified‐Whittaker plots taken at the same location for all control and treatment units. Point‐intercept transects and adapted Daubenmire plots on average captured fewer rare and exotic species in the control and treatment units in comparison with the belt transects and modified‐Whittaker plots. Modified‐Whittaker plots captured the highest species richness in all units. Early successional understory response to restoration treatments was likely masked by the response of the herbaceous community to yearly climatic variation (dry vs. wet years). Species richness and abundance were higher in wet years than dry years for all control and treatment units. Our results illustrate that sampling techniques can greatly influence perceptions of understory plant trajectories and therefore the interpretation of whether restoration goals have been achieved. In addition, our results suggest that restoration monitoring needs to be conducted for a sufficient length of time so that restoration treatment responses can be detected.     

Conservation value of timber quality versus associated non-timber quality stands for understory diversity in Nothofagus forests

Conservation strategies of forested landscapes must consider biodiversity of the included site types, i.e. timber-quality forests and associated non-timber-quality stands. The objectives were to characterize forest overstory structure in timber-quality versus associated non-timber-quality stands; and to compare their understory communities. Six forest types were sampled in Nothofagus forests of Tierra del Fuego (Argentina): two timber-quality N. pumilio forests, and four associated non-timber-quality stands (edge, N. antarctica, wetlands and streamside forests). Overstory structure and understory vegetation (species richness, frequencies, cover and biomass) were characterized during spring and summer seasons. Analysis of variance and multivariates were carried out. Overstory structure differed across the site types, with higher tree size, canopy closure and tree volume in timber-quality stands. Fifty-one understory plant species were observed, but understory variables varied with site types, especially wetlands (highest native and exotic richness, cover and biomass, and 25% of exclusive species). Site types were grouped in three: N. antarctica stands, streamside stands and the other N. pumilio forests according to multivariate analysis. Forty three percent of plants were distributed in all site types, and all timber-quality forest understory species were present in some associated non-timber-quality stands. Timber-quality N. pumilio forests have a marginal value for understory conservation compared to associated non-timber-quality stands, because these last include all the plants observed in timber-quality forests and also possess many exclusive species. Therefore, protection of associated non-timber-quality stands during forest management planning could increase understory conservation at landscape level, and these could be better reserves of understory diversity than retentions of timber-quality stands.     

In and out: Effects of shoot- vs. rooted-presence sampling methods on plant diversity measures in mountain grasslands

Plant diversity measures (e.g., alpha- and beta-diversity) provide the basis for a number of ecological indication and monitoring methods. These measures are based on species counts in sampling units (plots or quadrats). However, there are two alternative conventions for defining a vascular plant species as “present” in a plot, i.e. “shoot presence” (a species is recorded if the vertical projection of any above-ground part falls within the plot) and “rooted presence” (a species is recorded only when an individual is rooted inside the plot). Very few studies addressed the effects of the two sampling conventions on species richness and diversity indices. We sampled mountain dry grasslands in Italy across different plot sizes and vegetation types to assess how large is the difference in alpha- and beta-diversity values and in sample-based rarefaction curves between the two methods. We found that the difference is greatly dependent on plot size, being more relevant, both in absolute and percentage values, at smaller grain; it is also dependent on habitat type, being larger in shallow-soil communities, as they have a sparser vegetation structure and host life-form types with a larger lateral spread. At fine spatial scales (<1 m²) the difference is large enough to bias statistical inference, and we conclude that at such scales one should not attempt to compare plant diversity indices if they were not obtained with the same sampling convention.     

Post‐fire pickings: Large herbivores alter understory vegetation communities in a coastal eucalypt forest

Fire and herbivores alter vegetation structure and function. Future fire activity is predicted to increase, and quantifying changes in vegetation communities arising from post‐fire herbivory is needed to better manage natural environments. We investigated the effects of post‐fire herbivory on understory plant communities in a coastal eucalypt forest in southeastern Australia. We quantified herbivore activity, understory plant diversity, and dominant plant morphology following a wildfire in 2017 using two sizes of exclosures. Statistical analysis incorporated the effect of exclusion treatments, time since fire, and the effect of a previous prescribed burn. Exclusion treatments altered herbivore activity, but time since fire did not. Herbivory reduced plant species richness, diversity, and evenness and promoted the dominance of the most abundant plants within the understory. Increasing time since fire reduced community diversity and evenness and influenced morphological changes to the dominant understory plant species, increasing size and dead material while decreasing abundance. We found the legacy effects of a previous prescribed burn had no effect on herbivores or vegetation within our study. Foraging by large herbivores resulted in a depauperate vegetation community. As post‐fire herbivory can alter vegetation communities, we postulate that management burning practices may exacerbate herbivore impacts. Future fire management strategies to minimize herbivore‐mediated alterations to understory vegetation could include aggregating management burns into larger fire sizes or linking fire management with herbivore management. Restricting herbivore access following fire (planned or otherwise) can encourage a more diverse and species‐rich understory plant community. Future research should aim to determine how vegetation change from post‐fire herbivory contributes to future fire risk.     

用R法估计方差组分的原理、方法和应用

综述了R法估计方差组分的原理、方法和应用,目的是使该方法能够得到合理应用。R法是通过计算全数据集对亚数据集随机效应的回归因子（R）来估计方差组分的。利用一种基于一个变换矩阵的多变量迭代算法,结合先决条件的共扼梯度法求解混合模型方程组使R法的计算效率大为改善。R法的主要优点是计算成本低,同时可以得到方差组分估值的抽样误差和近似置信区间。其缺点是对于同样的数据,R法较其他方法的抽样误差大,而且在小样本中估计值往往有偏。做为一种可选方法,R法可以应用到大数据集的方差组分估计中,同时应进一步研究其理论特性,拓宽其应用范围。Abstract: Theory, method and application of Method R on estimation of (co)variance components were reviewed in order to make the method be reasonably used. Estimation requires R values,which are regressions of predicted random effects that are calculated using complete dataset on predicted random effects that are calculated using random subsets of the same data. By using multivariate iteration algorithm based on a transformation matrix,and combining with the preconditioned conjugate gradient to solve the mixed model equations, the computation efficiency of Method R is much improved. Method R is computationally inexpensive,and the sampling errors and approximate credible intervals of estimates can be obtained. Disadvantages of Method R include a larger sampling variance than other methods for the same data,and biased estimates in small datasets. As an alternative method, Method R can be used in larger datasets. It is necessary to study its theoretical properties and broaden its application range further.     

Species diversity in spatial and temporal dimensions of fruit-feeding butterflies from two Ecuadorian rainforests

To test the hypotheses that butterflies in an intact lowland rainforest are randomly distributed in space and time, a guild of nymphalid butterflies was sampled at monthly intervals for one year by trapping 883 individuals of 91 species in the canopy and understory of four contiguous, intact forest plots and one naturally occurring lake edge. The overall species abundance distribution was well described by a log-normal distribution. Total species diversity (γ-diversity) was partitioned into additive components within and among community subdivisions (α-diversity and β-diversity) in vertical, horizontal and temporal dimensions. Although community subdivisions showed high similarity (l-β-diversity/γ-diversity), significant β-diversity existed in each dimension. Individual abundance and observed species richness were lower in the canopy man in the understory, but rarefaction analysis suggested that the underlying species richness was similar in both canopy and understory. Observed species richness varied among four contiguous forest plots, and was lowest in the lake edge plot. Rarefaction and species accumulation curves showed that one forest plot and the lake edge had significantly lower species richness than other forest plots. Within any given month, only a small fraction of total sample species richness was represented by a single plot and height (canopy or understory). Comparison of this study to a similar one done in disturbed forest showed diat butterfly diversity at a naturally occurring lake edge differed strongly from a pasture-forest edge. Further comparison showed that species abundance distributions from intact and disturbed forest areas had variances that differed significandy, suggesting mat in addition to extrapolation, rarefaction and species accumulation techniques, the shapes of species abundance distributions are fundamental to assessing diversity among sites. This study shows the necessity for long-term sampling of diverse communities in space and time to assess tropical insect diversity among different areas, and the need of such studies is discussed in relation to tropical ecology and quick surveys in conservation biology.     

Monitoring an Arizona Ponderosa Pine Restoration: Sampling Efficiency and Multivariate Analysis of Understory Vegetation

As monitoring plans for the restoration of Pinus ponderosa forests in the southwestern United States evolve toward examining multifactor ecosystem responses to ecological restoration, designing efficient sampling procedures for understory vegetation will become increasingly important. The objective of this study was to compare understory composition and diversity among thin/burn and control treatments in a P. ponderosa restoration, while simultaneously examining the effects of sampling design and multivariate analyses on which conclusions were based. Using multi‐response permutation procedures (MRPP), we tested the null hypothesis of no difference in understory species composition among treatments using different data matrices (e.g., frequency and cover) for two different sampling methods. Treatment differences were subtle and were detected by an intensive 50, 1‐m² subplot sampling method for all data matrices but were not detected by a less intensive point‐intercept sampling method for any matrix. Sampling methods examined in this study controlled results of multivariate analyses more than the data matrices used to summarize data generated by a sampling method. We partitioned data into plant life form and native/exotic species categories for MRPP, and this partitioning isolated plant groups most responsible for treatment differences. We also examined the effects of number of 1‐m² subplots sampled on mean‐species‐richness/m² estimates and found that estimates based on 10 subplots and based on 50 subplots were highly correlated (r = 0.99). Species–area curves indicated that the 50, 1‐m² subplot sampling method detected the common species of sites but failed to detect the majority of rare species. Additional sampling‐design studies are needed to develop single sampling designs that produce multifactor data on plant composition, diversity, and spatial patterns amenable to multivariate analyses as part of monitoring plans of vegetation responses to ecological restoration.     

Marginal regression analysis of a multivariate binary response

We propose the use of the mean parameter for regression analysisof a multivariate binary response. We model the associationusing dependence ratios defined in terms of the mean parameter,the components of which are the joint success probabilitiesof all orders. This permits flexible modelling of higher-orderassociations, using maximum likelihood estimation. We reanalysetwo data sets, one with variable cluster size and the othera longitudinal data set with constant cluster size.     

Predicting Forest Microclimate in Heterogeneous Landscapes

Forest microclimate plays an integral role in ecosystem processes, yet a predictive understanding of its spatial and temporal variability in heterogeneous landscapes is largely lacking. In this study, we used regression kriging (RK) to analyze the degree to which physiographic versus ecological variables influence spatio-temporal variation in understory microclimate conditions. We monitored understory temperature in 200 forest plots within a 274 km² environmentally heterogeneous region in northern California (0.55 obs/km²). For each plot location, we measured four physiographic influences (elevation, coastal proximity, potential solar radiation, topographic wetness index) and three ecological drivers (forest patch size, proximity to forest edge, tree abundance). Temperature observations were aggregated to three time scales (hourly, daily, and monthly) to examine temporal variability in microclimate dynamics and its effect on spatial prediction. The obtained prediction models included both physiographic and vegetative effects, although the relative importance of individual effects varied greatly between the different models. Across time scales, elevation and coastal proximity had the most consistent physiographic effects on temperature, followed by the vegetative effects of forest patch size and distance to forest edge. RK captured significantly more landscape-scale variability in understory temperature than a regression-only approach with considerably better model performance at hourly and daily time scales than at a monthly scale. Using varied sampling density scenarios our results also suggest that predictive accuracy drops considerably at densities less than 0.34 obs/km². This research illustrates how geospatial and statistical modeling can be used to distinguish physiographic versus ecological effects on microclimate dynamics and elucidates the spatial and temporal scales that these processes operate.     

On the estimation of species richness based on the accumulation of previously unrecorded species

Estimation of species richness of local communities has become an important topic in community ecology and monitoring. Investigators can seldom enumerate all the species present in the area of interest during sampling sessions. If the location of interest is sampled repeatedly within a short time period, the number of new species recorded is typically largest in the initial sample and decreases as sampling proceeds, but new species may be detected if sampling sessions are added. The question is how to estimate the total number of species. The data collected by sampling the area of interest repeatedly can be used to build species accumulation curves: the cumulative number of species recorded as a function of the number of sampling sessions (which we refer to as “species accumulation data”). A classic approach used to compute total species richness is to fit curves to the data on species accumulation with sampling effort. This approach does not rest on direct estimation of the probability of detecting species during sampling sessions and has no underlying basis regarding the sampling process that gave rise to the data. Here we recommend a probabilistic, nonparametric estimator for species richness for use with species accumulation data. We use estimators of population size that were developed for capture‐recapture data, but that can be used to estimate the size of species assemblages using species accumulation data. Models of detection probability account for the underlying sampling process. They permit variation in detection probability among species. We illustrate this approach using data from the North American Breeding Bird Survey (BBS). We describe other situations where species accumulation data are collected under different designs (e.g., over longer periods of time, or over spatial replicates) and that lend themselves to of use capture‐recapture models for estimating the size of the community of interest. We discuss the assumptions and interpretations corresponding to each situation.     

The effects of forest conversion to oil palm on ground‐foraging ant communities depend on beta diversity and sampling grain

Beta diversity – the variation in species composition among spatially discrete communities – and sampling grain – the size of samples being compared – may alter our perspectives of diversity within and between landscapes before and after agricultural conversion. Such assumptions are usually based on point comparisons, which do not accurately capture actual differences in total diversity. Beta diversity is often not rigorously examined. We investigated the beta diversity of ground‐foraging ant communities in fragmented oil palm and forest landscapes in Sabah, Malaysia, using diversity metrics transformed from Hill number equivalents to remove dependences on alpha diversity. We compared the beta diversities of oil palm and forest, across three hierarchically nested sampling grains. We found that oil palm and forest communities had a greater percentage of total shared species when larger samples were compared. Across all grains and disregarding relative abundances, there was higher beta diversity of all species among forest communities. However, there were higher beta diversities of common and very abundant (dominant) species in oil palm as compared to forests. Differences in beta diversities between oil palm and forest were greatest at the largest sampling grain. Larger sampling grains in oil palm may generate bigger species pools, increasing the probability of shared species with forest samples. Greater beta diversity of all species in forest may be attributed to rare species. Oil palm communities may be more heterogeneous in common and dominant species because of variable community assembly events. Rare and also common species are better captured at larger grains, boosting differences in beta diversity between larger samples of forest and oil palm communities. Although agricultural landscapes support a lower total diversity than natural forests, diversity especially of abundant species is still important for maintaining ecosystem stability. Diversity in agricultural landscapes may be greater than expected when beta diversity is accounted for at large spatial scales.     

Zooplankton spatial patterns in two lakes with contrasting fish community structure

Horizontal and temporal patterns in crustacean zooplankton communities were analyzed in two small, oligotrophic lakes which were morphologically and chemically similar, but had contrasting fish communities. Ranger Lake was dominated by two bass species and the planktivores numbered < 25 ind. ha^–1. Mouse Lake had no large piscivores and planktivores numbered > 1200 ind. ha^–1. There were significant differences in the distribution of zooplankton taxa and size classes between sampling stations. In Ranger Lake, the smallest size classes were more abundant at the deeper stations and the larger individuals were more abundant at the shallower stations. In Mouse Lake, the smaller individuals were more common at the shallow stations and the larger individuals were more common at the deeper stations. These differences suggest medium scale patterns induced by vectorial forces, but modified by species specific migration patterns. We tested the hypothesis that horizontal heterogeneity should be influenced by planktivore density and found that none of the taxa showed significant between-lake differences in the variance-mean regressions. We also tested the hypothesis that larger taxa should be more heterogeneous and we found that cladocerans were more heterogeneous than copepods and nauplii. In terms of sampling methodology our data suggest that the between-station variability was so high that a single mid-lake sample would certainly lead to completely unacceptable errors in the estimation of population densities and biomasses.