Scale‐dependent biases in species counts in a grassland

Abstract. Numbers of plant species were recorded in species‐rich meadows in the Bílé Karpaty Mts., SE Czech Republic, with the aim to evaluate the sampling error made by well‐trained observers. Five observers recorded vascular plants in seven plots ranging from 9.8 cm² to 4 m² independently and were not time‐limited. In larger plots a discrepancy of 10–20% was found between individual estimates, in smaller plots discrepancy increased to 33%, on average. The gain in observed species richness by combining records of individual observers (in comparison with the mean numbers estimated by single observers) decreased from the smallest plot (27–82% for two to five observers) to the largest one (13–25%). However, after misidentified and suspicious records were eliminated, the gain was much lower and became scale‐independent; two observers added 12% species, on average, and the increase by combining species lists made by three or more observers was negligible (3% more on average). It is concluded that most discrepancies between individual observers were caused by misidentification of rare seedlings and young plants. We suggest that in species‐rich meadows plants should be recorded by at least three observers together and that they should consult all problematic plant specimens together in the field, to minimize errors.     

A nested-intensity design for surveying plant diversity

Managers of natural landscapes need cost-efficient, accurate, and precise systems to inventory plant diversity. We investigated a nested-intensity sampling design to assess local and landscape-scale heterogeneity of plant species richness in aspen stands in southern Colorado, USA. The nested-intensity design used three vegetation sampling techniques: the Modified-Whittaker, a 1000-m² multiple-scale plot (n = 8); a 100-m² multiple-scale Intensive plot (n = 15); and a 100-m² single-scale Extensive plot (n = 28). The large Modified-Whittaker plot (1000 m²) recorded greater species richness per plot than the other two sampling techniques (P < 0.001), estimated cover of a greater number of species in 1-m² subplots (P < 0.018), and captured 32 species missed by the smaller, more numerous 100-m² plots of the other designs. The Intensive plots extended the environmental gradient sampled, capturing 17 species missed by the other techniques, and improved species–area calculations. The greater number of Extensive plots further expanded the gradient sampled, and captured 18 additional species. The multi-scale Modified-Whittaker and Intensive designs allowed quantification of the slopes of species–area curves in the single-scale Extensive plots. Multiple linear regressions were able to predict the slope of species–area curves (adj R ² = 0.64, P < 0.001) at each Extensive plot, allowing comparison of species richness at each sample location. Comparison of species–accumulation curves generated with each technique suggested that small, single-scale plot techniques might be very misleading because they underestimate species richness by missing locally rare species at every site. A combination of large and small multi-scale and single-scale plots greatly improves our understanding of native and exotic plant diversity patterns.     

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.     

Investigating detection success: lessons from trials using decoy rare plants

Imperfect detection leads to underestimates of species presence and decreases the reliability of survey data. Imperfect detection has not been examined in detail for boreal forest understory plants, despite widespread use of surveys for rare plants prior to development. We addressed this issue using detectability trials conducted in Alberta, Canada with decoy vascular plants. Volunteer observers searched in survey plots for species while unaware of their true presence or abundance. Our findings indicate that the detection of cryptic species is very low when abundance is low (0–35%) and plot size is large (<?50% in?≥?100 m²). Plant density (individuals per unit area) was the most important determinant of detection probability, where more abundant species were detected more often and with less survey effort. When abundance was held constant, diffusely arranged species were twice as likely to be detected compared to those in clumps. Detection of cryptic species can be low even when individuals are flowering, and even morphologically distinct species can go unnoticed in small plots. We suggest that future decoy trials investigate search strategies that could improve detection and that field surveys for vascular plants address imperfect detection through careful consideration of plot size, characteristics of the target species, and survey effort, both in terms of time expenditure within an area and the number of observers employed.     

Estimating species – area relationships by modeling abundance and frequency subject to incomplete sampling

Models and data used to describe species–area relationships confound sampling with ecological process as they fail to acknowledge that estimates of species richness arise due to sampling. This compromises our ability to make ecological inferences from and about species–area relationships. We develop and illustrate hierarchical community models of abundance and frequency to estimate species richness. The models we propose separate sampling from ecological processes by explicitly accounting for the fact that sampled patches are seldom completely covered by sampling plots and that individuals present in the sampling plots are imperfectly detected. We propose a multispecies abundance model in which community assembly is treated as the summation of an ensemble of species‐level Poisson processes and estimate patch‐level species richness as a derived parameter. We use sampling process models appropriate for specific survey methods. We propose a multispecies frequency model that treats the number of plots in which a species occurs as a binomial process. We illustrate these models using data collected in surveys of early‐successional bird species and plants in young forest plantation patches. Results indicate that only mature forest plant species deviated from the constant density hypothesis, but the null model suggested that the deviations were too small to alter the form of species–area relationships. Nevertheless, results from simulations clearly show that the aggregate pattern of individual species density–area relationships and occurrence probability–area relationships can alter the form of species–area relationships. The plant community model estimated that only half of the species present in the regional species pool were encountered during the survey. The modeling framework we propose explicitly accounts for sampling processes so that ecological processes can be examined free of sampling artefacts. Our modeling approach is extensible and could be applied to a variety of study designs and allows the inclusion of additional environmental covariates.     

Effects of sampling time,species richness and observer on the exhaustiveness of plant censuses

Question: How may sampling time affect exhaustiveness of vegetation censuses in interaction with observer effect and quadrat species richness? Location: French lowland forests. Methods: Two data sets comprised of 75 timed, one‐hour censuses of vascular plants carried out by five observers on 24 400‐m² forest quadrats were analysed using mixed‐effect models. Results: The level of exhaustiveness increased in a semi‐logarithmic way with sampling time and decreased with quadrat species richness. After one hour, 20 to 30% of the species remained undetected by single observers. This proportion varied among observers and the discrepancy increased with increasing sampling time. Fixing the sampling time may make richness estimates vary less between observers but the time limit should be at least 30 min to reduce the bias in exhaustiveness between rich and poor quadrats. Conclusions We advocate the use of sampling methods based on spatially or temporally‐replicated censuses and statistical analyses that correct for the lack of census exhaustiveness in vegetation studies.     

Sampling effort and species richness assessment: a case study on Brazilian spiders

The knowledge on the geographical distribution of species is essential for building biogeographical and macroecological hypotheses. However, information on this regard is not distributed uniformly in space and usually come from biased sampling. The aim of this study is to quantify the influence of spatial distribution of sampling effort on the assessment of spider species richness in Brazil. We used a database of spider distribution records in Brazil, based on the taxonomic and biodiversity survey literature. The results show that the Atlantic Forest was better sampled and had the highest spider species richness among the Brazilian biomes. The Amazon, though having large collecting gaps and high concentration of records around major cities and rivers, showed the second highest number of species. The Pampa had a large number of records, but these are concentrated near a major city in the transition zone with the Atlantic Forest. The Cerrado, Caatinga and Pantanal had shown to be poorly sampled and, consequently, were among the lesser known biomes regarding the spider fauna. A linear regression analysis showed that the spider species richness in Brazil is strongly correlated to the number of records. However, we have identified areas potentially richest in species, which strongly deviate from the predicted by our analyses. Our results show that it is possible to access the spatial variation in species richness, as long as the variation in sampling effort is taken into account.     

Are Plant Censuses Carried Out on Small Quadrats More Reliable than on Larger Ones?

Plant censuses are known to be significantly affected by observers’ biases. In this study, we checked whether the magnitude of observer effects (defined as the % of total variance) varied with quadrat size: we expected the census repeatability (% of the total variance that is not due to measurement errors) to be higher for small quadrats than for larger ones. Variations according to quadrat size of the repeatability of species richness, Simpson equitability and reciprocal diversity indices, Ellenberg indicator values, plant cover and plant frequency were assessed using 359 censuses of vascular plants. These were carried out independently by four professional botanists during spring 2002 on the same 18 forest plots, each comprising one 400-m² quadrat, four 4-m² and four 2-m² quadrats. Time expenditure was controlled for. General Linear Models using random effects only were applied to the ecological indices to estimate variance components and magnitude of the following effects (if possible): plot, quadrat, observer, plant species and two-way interactions. High repeatability was obtained for species richness and Ellenberg indicator values. Species richness and Ellenberg indicator values were generally more accurate but also more biased in large quadrats. Simpson reciprocal diversity and equitability indices were poorly repeatable (especially equitability) probably because plant cover estimates varied widely among observers, irrespective of quadrat size. Grouping small quadrats usually increased the repeatability of the variable considered (e.g. species richness, Simpson diversity, plant cover) but the number of plant species found on those pooled 16 m² was much lower than if large plots were sampled. We therefore recommend to use large, single quadrats for forest vegetation monitoring.     

Characterising the phytophagous arthropod fauna of a single host plant species: assessing survey completeness at continental and local scales

Quantifying survey completeness is a key step in designing and interpreting biodiversity assessments. To date this has only been examined either at a local scale through repetitive sampling, or across broader geographic areas through multiple survey sites. In this paper, we determine the completeness of sampling at both local and continental scales, of the phytophagous arthropod assemblage on the Neotropical shrub Parkinsonia aculeata (Leguminosae). We used survey gap analysis (SGA) to determine whether existing surveys adequately sampled the diversity of environments and geographic space covered by the plant. Within defined geographic regions, we determined survey completeness at a local scale with species accumulation curves. SGA identified the highest priority sites for future sampling in the Sonoran Desert and the Pacific Coast of South America. The arthropods sampled on P. aculeata differed significantly between seasons, highlighting the importance of including surveys throughout the year. At the local scale, surveys in most regions were estimated to have sampled <50 % of all species. Only the Mexican Gulf, following 84 samples including 902 individuals, had a reasonably complete sample of all species (more than 50 %). As in other studies, rare species will continue to be detected even after extensive surveying, and it is likely that close to 100 samples or 1,000 individuals will be needed to attain 50 % survey completeness in a region. However, if the objective is to document close “host-associations” then effort may be better directed at surveying ecologically distinct new areas rather than exhaustive sampling in existing ones. Methods such as SGA can direct such surveys, and in conjunction with species-richness estimates, can be used to assess the adequacy of existing surveys.     

Species richness estimation and determinants of species detectability in butterfly monitoring programmes

Abstract 1. Species richness is the most widely used biodiversity index, but can be hard to measure. Many species remain undetected, hence raw species counts will often underestimate true species richness. In contrast, capture–recapture methods estimate true species richness and correct for imperfect and varying detectability. 2. Detectability is a crucial quantity that provides the link between a species count and true species richness. For insects, it has hardly ever been estimated, although this is required for the interpretation of species counts. 3. In the Swiss butterfly monitoring programme about 100 transect routes are surveyed seven times a year using a highly standardised protocol. In July 2003, control observers made two additional surveys on 38 transects. Data from these 38 quadrats were analysed to see whether currently available capture–recapture models can provide quadrat‐specific estimates of species richness, and to estimate species detectability in relation to transect, observer, survey, region, and abundance. 4. Species richness over the entire season cannot be estimated using current capture–recapture methods. The species pool was open, preventing use of closed population models, and detectability varied by species, preventing use of current open population models. Assuming a closed species pool during two mid‐season (July) surveys, a Jackknife capture–recapture method was used that accounts for heterogeneity to estimate mean detectability and species richness. 5. In every case, more species were present than were counted. Mean species detectability was 0.61 (SE 0.01) with significant differences between observers (range 0.37–0.83). Species‐specific detection at time t+ 1 was then modelled for those species seen at t for three mid‐season surveys. Detectability averaged 0.50 (range 0.17–0.81) for individual species and 0.65, 0.44, and 0.42 for surveys. Abundant species were detected more easily, although this relationship explained only 5% of variation in species detectability. 6. These are important, although not entirely unexpected, results for species richness estimation of short‐lived animals. Raw counts of species may be misleading species richness indicators unless many surveys are conducted. Monitoring programmes should be calibrated, i.e. the assumption of constant detectability over dimensions of interest needs to be tested. The development of capture–recapture or similar models that can cope with both open populations and heterogeneous species detectability to estimate species richness should be a research priority.     

Detection and Plant Monitoring Programs: Lessons from an Intensive Survey of Asclepias meadii with Five Observers

Monitoring programs, where numbers of individuals are followed through time, are central to conservation. Although incomplete detection is expected with wildlife surveys, this topic is rarely considered with plants. However, if plants are missed in surveys, raw count data can lead to biased estimates of population abundance and vital rates. To illustrate, we had five independent observers survey patches of the rare plant Asclepias meadii at two prairie sites. We analyzed data with two mark-recapture approaches. Using the program CAPTURE, the estimated number of patches equaled the detected number for a burned site, but exceeded detected numbers by 28% for an unburned site. Analyses of detected patches using Huggins models revealed important effects of observer, patch state (flowering/nonflowering), and patch size (number of stems) on probabilities of detection. Although some results were expected (i.e. greater detection of flowering than nonflowering patches), the importance of our approach is the ability to quantify the magnitude of detection problems. We also evaluated the degree to which increased observer numbers improved detection: smaller groups (3–4 observers) generally found 90 – 99% of the patches found by all five people, but pairs of observers or single observers had high error and detection depended on which individuals were involved. We conclude that an intensive study at the start of a long-term monitoring study provides essential information about probabilities of detection and what factors cause plants to be missed. This information can guide development of monitoring programs.     

Bias in vegetation databases? A comparison of stratified‐random and preferential sampling

Aim: Vegetation plots collected since the early 20th century and stored in large vegetation databases are an important source of ecological information. These databases are used for analyses of vegetation diversity and estimation of vegetation parameters, however such analyses can be biased due to preferential sampling of the original data. In contrast, modern vegetation survey increasingly uses stratified‐random instead of preferential sampling. To explore how these two sampling schemes affect vegetation analyses, we compare parameters of vegetation diversity based on preferentially sampled plots from a large vegetation database with those based on stratified‐random sampling. Location: Moravian Karst and Silesia, Czech Republic. Methods: We compared two parallel analyses of forest vegetation, one based on preferentially sampled plots taken from a national vegetation database and the other on plots sampled in the field according to a stratified‐random design. We repeated this comparison for two different regions in the Czech Republic. We focussed on vegetation properties commonly analysed using data from large vegetation databases, including alpha (within‐plot) diversity, cover and participation of different species groups, such as endangered and alien species within plots, total species richness of data sets, beta diversity and ordination patterns. Results: The preferentially sampled data sets obtained from the database contained more endangered species and had higher beta diversity, whereas estimates of alpha diversity and representation of alien species were not consistently different between preferentially and stratified‐randomly sampled data sets. In ordinations, plots from the preferential samples tended to be more common at margins of plot scatters. Conclusions: Vegetation data stored in large databases are influenced by researcher subjectivity in plot positioning, but we demonstrated that not all of their properties necessarily differ from data sets obtained by stratified‐random sampling. This indicates the value of vegetation databases for use in biodiversity studies; however, some analyses based on these databases are clearly biased and their results must be interpreted with caution.     

Accounting for the capacity of common and rare species to contribute to diversity spatial patterns: Is it a sampling issue or a biological effect?

The contribution of common species to overall species richness in many cases is greater than that of rare species. However, the explanation of this phenomenon remains vague. One hypothesis is that this is a sampling issue and not a biological one. Therefore standardization methods like the information index and empirical variance have been proposed. But, these standardizations do not explicitly compare the significance of the dataset size of the common and rare sub-assemblage. Here, we investigate the role of dataset size in accounting for the capacity of common and rare species to contribute to diversity spatial patterns. We used a dataset of 5148 vascular plant species recorded in 16,439 sample plots in the Greek Natura 2000 network. Species were ranked according to the number of sample plots they occupied in ascending (rare to common), descending (common to rare) and random order. We analyzed the correlation between the richness of each sub-assemblage and total species richness. When comparing among sub-assemblages with equal number of species, common species are clearly the better predictors of total species richness. But, when comparing among sub-assemblages with equal number of occurrence records, the patterns changed. Common and rare species contribution to the overall richness pattern was comparable, with rare species contributing slightly less than widespread species in some cases and the opposite in other cases. However in all cases, sub-assemblages of random species remarkably outperformed the equal sized sub-assemblages of common or rare species. Our results suggest that common and rare species are biased samples of the community and that equal sized random samples are more representative; thus the greater contribution of common species than rare species to biodiversity patterns might be more a sampling issue than a biological effect of commonness or rarity.     

The contribution of common and rare species to plant species richness patterns: the effect of habitat type and size of sampling unit

Understanding how overall patterns of spatial variation in species richness are affected by distributional patterns of species has been an area of growing concern. In the present study, we investigated the relative importance of common and rare species as contributors in overall plant species richness. We further examined if the effects of common or rare species in richness patterns are affected by the size of the sampling units and if the observed patterns hold at different habitats. We used a dataset of 5,148 higher plant species distributed across 16,114 sampling plots located in 240 sites of the NATURA 2000 network of Greece. We ranked all species based on the number of sites they occupied and we developed a common to rare and a rare to common sequence. We correlated those sequences with cumulative species distributions. We performed this analysis in nine different sizes of sampling units and in three different datasets referring to (a) all habitat types together, (b) coniferous habitats only and (c) alpine habitats only. Our analysis showed that despite the proportionally higher numbers of restricted species, widespread species make a greater contribution to overall richness patterns and that this observed pattern does not depend on the size of the sampling units. Moreover, the observed pattern stands for different habitat types. Our findings support the generality of this pattern and highlight the importance of widespread species as adequate indicators of biodiversity patterns at various habitat types. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Spatio‐temporal scaling of biodiversity and the species–time relationship in a stream fish assemblage

1. The increase of species richness with the area of the habitat sampled, that is the species–area relationship, and its temporal analogue, the species–time relationship (STR), are among the few general laws in ecology with strong conservation implications. However, these two scale‐dependent phenomena have rarely been considered together in biodiversity assessment, especially in freshwater systems. 2. We examined how the spatial scale of sampling influences STRs for a Central‐European stream fish assemblage (second‐order Bernecei stream, Hungary) using field survey data in two simulation‐based experiments. 3. In experiment one, we examined how increasing the number of channel units, such as riffles and pools (13 altogether), and the number of field surveys involved in the analyses (12 sampling occasions during 3 years), influence species richness. Complete nested curves were constructed to quantify how many species one observes in the community on average for a given number of sampling occasions at a given spatial scale. 4. In experiment two, we examined STRs for the Bernecei fish assemblage from a landscape perspective. Here, we evaluated a 10‐year reach level data set (2000–09) for the Bernecei stream and its recipient watercourse (third‐order Kemence stream) to complement results on experiment one and to explore the mechanisms behind the observed patterns in more detail. 5. Experiment one indicated the strong influence of the spatial scale of sampling on the accumulation of species richness, although time clearly had an additional effect. The simulation methodology advocated here helped to estimate the number of species in a diverse combination of spatial and temporal scale and, therefore, to determine how different scale combinations influence sampling sufficiency. 6. Experiment two revealed differences in STRs between the upstream (Bernecei) and downstream (Kemence) sites, with steeper curves for the downstream site. Equations of STR curves were within the range observed in other studies, predominantly from terrestrial systems. Assemblage composition data suggested that extinction–colonisation dynamics of rare, non‐resident (i.e. satellite) species influenced patterns in STRs. 7. Our results highlight that the determination of species richness can benefit from the joint consideration of spatial and temporal scales in biodiversity inventory surveys. Additionally, we reveal how our randomisation‐based methodology may help to quantify the scale dependency of diversity components (α, β, γ) in both space and time, which have critical importance in the applied context.     

Biodiversity Promotes Tree Growth during Succession in Subtropical Forest

Losses of plant species diversity can affect ecosystem functioning, with decreased primary productivity being the most frequently reported effect in experimental plant assemblages, including tree plantations. Less is known about the role of biodiversity in natural ecosystems, including forests, despite their importance for global biogeochemical cycling and climate. In general, experimental manipulations of tree diversity will take decades to yield final results. To date, biodiversity effects in natural forests therefore have only been reported from sample surveys or meta-analyses with plots not initially selected for diversity. We studied biomass and growth of subtropical forests stands in southeastern China. Taking advantage of variation in species recruitment during secondary succession, we adopted a comparative study design selecting forest plots to span a gradient in species richness. We repeatedly censored the stem diameter of two tree size cohorts, comprising 93 species belonging to 57 genera and 33 families. Tree size and growth were analyzed in dependence of species richness, the functional diversity of growth-related traits, and phylogenetic diversity, using both general linear and structural equation modeling. Successional age covaried with diversity, but differently so in the two size cohorts. Plot-level stem basal area and growth were positively related with species richness, while growth was negatively related to successional age. The productivity increase in species-rich, functionally and phylogenetically diverse plots was driven by both larger mean sizes and larger numbers of trees. The biodiversity effects we report exceed those from experimental studies, sample surveys and meta-analyses, suggesting that subtropical tree diversity is an important driver of forest productivity and re-growth after disturbance that supports the provision of ecological services by these ecosystems.     

Sampling large landscapes with small-scale stratification

This study, carried out for the United States Bureau of Reclamation (BOR), demonstrated methods for surveying large landscapes using small-scale, habitat-based stratification, a common problem that has heretofore received little attention. The goal was to design a sampling plan for detecting change in the density of breeding birds of 6 species occurring along the Colorado River around and south of Lake Mead in the southwestern United States. The main problem in designing the study was that the focal species were concentrated in small, irregularly shaped patches of habitat. We partitioned the study area into >15,000 plots configured to enclose the high-quality habitat in the fewest possible plots with the constraint that plots could be surveyed in 1 morning by 1 person. Because of the irregular plot shapes and extremely dense vegetation, we used area searches to carry out the surveys. We used double-sampling, including a large sample of plots surveyed with a rapid method and a subsample of plots surveyed intensively, to estimate detection ratios. A simulation study helped allocate effort between rapid and intensive surveys and indicated that conducting 80 surveys per year would achieve high power to detect a 50% decline occurring during 20 years. This is one of the first studies to show how large landscapes can be sampled using small-scale stratification so that effort can be concentrated in the habitats of greatest interest. © 2012 The Wildlife Society.     

Biophysical and human influences on plant species richness in grasslands: Comparing variegated landscapes in subtropical and temperate regions

A survey of grassy woodlands in the Queensland subtropics was conducted, recording herbaceous species richness at 212 sites on three properties (2756 ha). A range of habitats typical of cattle grazing enterprises was sampled and site variables included lithology, slope position, tree density, soil disturbance, soil enrichment and grazing. Results were compared with a previously published survey of temperate grasslands. Lithology, slope position and tree density had relatively minor effects on plant species richness, although in both surveys there was some evidence of lower species richness on the more fertile substrates. Soil disturbance and soil enrichment significantly reduced the richness of native species in both surveys, while exotic species were insensitive (subtropics) or increased (temperate) with disturbance. Rare native species were highly sensitive to disturbances, including grazing, in the temperate study. Although some trends were similar for rare species in the subtropics, the results were not significant and there were complex interactions between grazing, lithology and slope position. Grazing did not have a negative effect on native species richness, except in the closely grazed patches within pastures, and then only on the most intensively developed property. At the scale recorded (30 m²), the native pastures, roadsides and stock routes sampled in the subtropics appear to be among the most species‐rich grasslands ever reported, both nationally and globally. Native species richness was approximately 50% higher than the temperate survey figures across all the comparable habitats. While there are no clear reasons for this result, potential explanations are proposed.     

The relationship between psyllid leaf galls and redbay (Persea borbonia) fitness traits in sun and shade

Spatial autocorrelation in vegetation has been discussed extensively, but little is yet known about how standard plant sampling methods perform when confronted with varying levels of patchiness. Simulated species maps with a range of total abundance and spatial autocorrelation (patchiness) were sampled using four methods: strip transect, randomly located quadrats, the non-nested multiscale modified Whittaker plot and the nested multiscale North Carolina Vegetation Survey (NCVS) plot. Cover and frequency estimates varied widely within and between methods, especially in the presence of high patchiness and for species with moderate abundances. Transect sampling showed the highest variability, returning estimates of 19–94% cover for a species with an actual cover of 50%. Transect and random methods were likely to miss rare species entirely unless large numbers of quadrats were sampled. NCVS plots produced the most accurate cover estimates because they sampled the largest area. Total species richness calculated using semilog species-area curves was overestimated by transect and random sampling. Both multiscale methods, the modified Whittaker and the NCVS plots, overestimated species richness when patchiness was low, and underestimated it when patchiness was high. There was no clear distinction between the nested NCVS or the non-nested modified Whittaker plot for any of the measures assessed. For all sampling methods, cover and especially frequency estimates were highly variable, and depended on both the level of autocorrelation and the sampling method used. The spatial structure of the vegetation must be considered when choosing field sampling protocols or comparing results between studies that used different methods.     

Nonparametric Lower Bounds for Species Richness and Shared Species Richness under Sampling without Replacement

Summary A number of species richness estimators have been developed under the model that individuals (or sampling units) are sampled with replacement. However, if sampling is done without replacement so that no sampled unit can be repeatedly observed, then the traditional estimators for sampling with replacement tend to overestimate richness for relatively high-sampling fractions (ratio of sample size to the total number of sampling units) and do not converge to the true species richness when the sampling fraction approaches one. Based on abundance data or replicated incidence data, we propose a nonparametric lower bound for species richness in a single community and also a lower bound for the number of species shared by multiple communities. Our proposed lower bounds are derived under very general sampling models. They are universally valid for all types of species abundance distributions and species detection probabilities. For abundance data, individuals' detectabilities are allowed to be heterogeneous among species. For replicated incidence data, the selected sampling units (e.g., quadrats) need not be fully censused and species can be spatially aggregated. All bounds converge correctly to the true parameters when the sampling fraction approaches one. Real data sets are used for illustration. We also test the proposed bounds by using subsamples generated from large real surveys or censuses, and their performance is compared with that of some previous estimators.