Scale dependence of effective specialization: its analysis and implications for estimates of global insect species richness

Estimates of global insect species richness are sometimes based on effective specialization, a calculation used to estimate the number of insect species that is restricted to a particular tree species. Yet it is not clear how effective specialization is influenced by spatial scale or characteristics of the insect community itself (e.g. species richness). We investigated scale dependence and community predictors of effective specialization using 15,907 beetles (583 species) collected by insecticide fogging from the crowns of 96 trees (including 32 Quercus trees) located in Ohio and Indiana. Trees were distributed across 24 forest stands (∼1 ha) nested within six sites (∼10–100 km²) and two ecoregions (> 1000 km²). Using paired-sample randomization tests, we found that effective specialization ( f _k ) exhibited negative scale-dependence in early (May–June 2000) and late (August–September 2000) sampling periods. Our average effective specialization ( F ) values — those that are comparable to Erwin's (1982) estimates — ranged from 19% to 97%, and increased as spatial scale decreased. We also found that beetle species richness and the number of shared beetle species across host trees were significant and consistent negative predictors of F . This shows that increases in spatial scale, species richness, and the number of trees (and/or tree species) all coincide with decreases in effective specialization. Collectively, our results indicate that estimates of global insect species richness based on effective specialization at a single spatial scale are overestimating the magnitude of global insect species richness. We propose that scale dependence should be promoted to a central concept in the research program on global estimates of species richness.     

中国北方典型草地物种丰富度与生产力的关系

利用2002–2004年内蒙古和甘肃南部几种典型草地的实测资料,研究了不同尺度物种丰富度与生产力的关系,并初步探讨了其形成机制。结果显示,温带草地的物种丰富度随生产力的增加而增加,但受空间尺度影响。在群落尺度(同一群落),在7种样方数大于15的群落中,仅沙生针茅(Stipaglareosa)群落物种丰富度与生产力呈现单峰型关系,其余均呈现线性正相关关系;在植被类型尺度,物种丰富度–生产力之间表现为显著的正相关关系;在研究区尺度,物种丰富度随生产力的增加而显著增加。研究还表明,研究区群落生产力的变化范围为13–368g·m–2·yr–1,物种丰富度为4–35种;生产力从高到低的顺序为:高寒草甸>草甸草原>典型草原>荒漠草原。     

Estimating species richness from quadrat sampling data: a general approach

We consider the problem of estimating the number of species (denoted by S) of a biological community located in a region divided into n quadrats. To address this question, different hierarchical parametric approaches have been recently developed. Despite a detailed modeling of the underlying biological processes, they all have some limitations. Indeed, some assume that n is theoretically infinite; as a result, n and the sampling fraction are not a part of such models. Others require some prior information on S to be efficiently implemented. Our approach is more general in that it applies without limitation on the size of n, and it can be used in the presence, as well as in the absence, of prior information on S. Moreover, it can be viewed as an extension of the approach of Dorazio and Royle (2005, Journal of the American Statistical Association 100, 389-398) in that n is a part of the model and a prior distribution is placed on S. Despite serious computational difficulties, we have perfected an efficient Markov chain Monte Carlo algorithm, which allows us to obtain the Bayesian estimate of S. We illustrate our approach by estimating the number of species of a bird community located in a forest.     

Size-dependent species richness: trends within plant communities and across latitude

We examine how species richness and species‐specific plant density (number of species and number of individuals per species, respectively) vary within community size frequency distributions and across latitude. Communities from Asia, Africa, Europe, and North, Central and South America were studied (60°4′N–41°4′S latitude) using the Gentry data base. Log–log linear stem size (diameter) frequency distributions were constructed for each community and the species richness and species‐specific plant density within each size class were determined for each frequency distribution. Species richness in the smallest stem size class correlated with the Y‐intercepts (β‐values) of the regression curves describing each log–log linear size distributions. Two extreme community types were identified (designated as type A and type B). Type A communities had steep size distributions (i.e. large β‐values), log–log linear species‐richness size distributions, low species‐specific plant density distributions, and a small size class (2–4 cm) containing the majority of all species but rarely conspecifics of the dominant tree species. Type B communities had shallow size distributions (i.e. small β‐values), more or less uniform (and low) size class species‐ richness and species‐specific density distributions and size‐dominant species resident in the smallest size class. Type A communities were absent in the higher latitudes but increased in number towards the equator, i.e. in the smallest size class, species richness increased (and species‐specific density decreased) towards the tropics. Based on our survey of type A and type B communities (and their intermediates), species richness evinces size‐dependent and latitudinal trends, i.e. species richness increased with decreasing body size and most species increasingly reside in the smallest plant size class towards the tropics. Across all latitudes, a trade‐off exists between the number of species and the number of individuals per species residing in the smaller size classes.     

Habitat patchiness and plant species richness

The pattern of woody species richness decline with a decrease in woody vegetation cover was studied within a tallgrass prairie. The decline in species richness is highly non-linear, with a well-defined threshold below which species richness collapses. This relationship can be understood after considering information on how landscape structure changes with woody vegetation cover, and how species richness is related to landscape structure.     

Sampling grain influences trends in vegetation composition and diversity with time since fire in Australian heathland

Scale‐dependency of pattern and process is well‐understood for many ecological communities; however, the influence of spatial scale (sampling grain) in detecting temporal change in communities is less well‐understood. The temperate lowland heathlands of south‐east Australia are one of the most fire‐prone ecosystems on earth. Despite the extensive literature documenting the effect of time since fire on heathlands, we know little about how sampling grain influences trends in vegetation variables over time, and whether these trends are scale‐dependent. Using 3500 ha of heathland in the Gippsland Lakes Coastal Park, south‐east Australia, we investigated how above‐ground species composition and diversity, and trends in these variables with increasing time since fire, were influenced by sampling grain (1 m², 10 m², 100 m², 900 m², 1 ha, 4 ha). Sampling grain influenced patterns detected in vegetation variables and in some instances, significantly affected their relationship with time since fire. Richness decreased with time since fire, with mean richness decreasing at three of the four grains, while total richness decreased at half of the sampled grains. Evenness (J) decreased with increasing time since fire for all grains except 1 m². The decline in diversity (H) with time since fire appeared to be independent of scale, as all grains decreased significantly with increasing time since fire. Community heterogeneity demonstrated a weak response to time since fire across most grains. Changes in composition among young (0–6 years since fire), intermediate (9–19 years) and old (23–27 years) sites were dependent on sampling grain, with all grains exhibiting significant differences in composition, apart from the 1 m² grain and the 100 m² grain (presence/absence data). Overall, species composition, richness, evenness, diversity and community heterogeneity were dependent on the scale at which the vegetation was sampled. In addition, trends in many of these vegetation variables with increasing time since fire were scale‐dependent. This work provides strong evidence that sampling at multiple grains contributes substantially to understanding pattern and process in heathlands.     

Neutral community dynamics, the mid-domain effect and spatial patterns in species richness

The mid‐domain effect (MDE) aims to explain spatial patterns in species richness invoking only stochasticity and geometrical constraints. In this paper, we used simulations to show that its main qualitative prediction, a hump‐shaped pattern in species richness, converges to the expectation of a spatially bounded neutral model when communities are linked by short‐distance migration. As these two models can be linked under specific situations, neutral theory may provide a mechanistic population level basis for MDE. This link also allows establishing in which situations MDE patterns are more likely to be found. Also, in this situation, MDE models could be used as a first approximation to understand the role of both stochastic (ecological drift and migration) and deterministic (adaptation to environmental conditions) processes driving the spatial structure of species richness.     

Assessing species richness trends: Declines of bees and bumblebees in the Netherlands since 1945

Estimating and predicting temporal trends in species richness is of general importance, but notably difficult because detection probabilities of species are imperfect and many datasets were collected in an opportunistic manner. We need to improve our capabilities to assess richness trends using datasets collected in unstandardized procedures with potential collection bias. Two methods are proposed and applied to estimate richness change, which both incorporate models for sampling effects and detection probability: (a) nonlinear species accumulation curves with an error variance model and (b) Pradel capture–recapture models. The methods are used to assess nationwide temporal trends (1945–2018) in the species richness of wild bees in the Netherlands. Previously, a decelerating decline in wild bee species richness was inferred for part of this dataset. Among the species accumulation curves, those with nonconstant changes in species richness are preferred. However, when analyzing data subsets, constant changes became selected for non‐Bombus bees (for samples in collections) and bumblebees (for spatial grid cells sampled in three periods). Smaller richness declines are predicted for non‐Bombus bees than bumblebees. However, when relative losses are calculated from confidence intervals limits, they overlap and touch zero loss. Capture–recapture analysis applied to species encounter histories infers a constant colonization rate per year and constant local species survival for bumblebees and other bees. This approach predicts a 6% reduction in non‐Bombus species richness from 1945 to 2018 and a significant 19% reduction for bumblebees. Statistical modeling to detect species richness time trends should be systematically complemented with model checking and simulations to interpret the results. Data inspection, assessing model selection bias, and comparisons of trends in data subsets were essential model checking strategies in this analysis. Opportunistic data will not satisfy the assumptions of most models and this should be kept in mind throughout.     

Effects of macroalgal species identity and richness on primary production in benthic marine communities

Plant biodiversity can enhance primary production in terrestrial ecosystems, but biodiversity effects are largely unstudied in the ocean. We conducted a series of field and mesocosm experiments to measure the relative effects of macroalgal identity and richness on primary productivity (net photosynthetic rate) and biomass accumulation in hard substratum subtidal communities in North Carolina, USA. Algal identity consistently and strongly affected production; species richness effects, although often significent, were subtle. Partitioning of the net biodiversity effect indicated that complementarity effects were always positive and species were usually more productive in mixtures than in monoculture. Surprisingly, slow growing species performed relatively better in the most diverse treatments than the most productive species, thus selection effects were consistently negative. Our results suggest that several basic mechanisms underlying terrestrial plant biodiversity effects also operate in algal-based marine ecosystems, and thus may be general.     

Disparity between range map- and survey-based analyses of species richness: patterns, processes and implications

Species richness patterns are characterized either by overlaying species range maps or by compiling geographically extensive survey data for multiple local communities. Although, these two approaches are clearly related, they need not produce identical richness patterns because species do not occur everywhere in their geographical range. Using North American breeding birds, we present the first continent‐wide comparison of survey and range map data. On average, bird species were detected on 40.5% of the surveys within their range. As a result of this range porosity, the geographical richness patterns differed markedly, with the greatest disparity in arid regions and at higher elevations. Environmental productivity was a stronger predictor of survey richness, while elevational heterogeneity was more important in determining range map richness. In addition, range map richness exhibited greater spatial autocorrelation and lower estimates of spatial turnover in species composition. Our results highlight the fact that range map richness represents species coexistence at a much coarser scale than survey data, and demonstrate that the conclusions drawn from species richness studies may depend on the data type used for analyses.     

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.     

Inferring species richness using multispecies occupancy modeling: Estimation performance and interpretation

Multispecies occupancy models can estimate species richness from spatially replicated multispecies detection/non‐detection survey data, while accounting for imperfect detection. A model extension using data augmentation allows inferring the total number of species in the community, including those completely missed by sampling (i.e., not detected in any survey, at any site). Here we investigate the robustness of these estimates. We review key model assumptions and test performance via simulations, under a range of scenarios of species characteristics and sampling regimes, exploring sensitivity to the Bayesian priors used for model fitting. We run tests when assumptions are perfectly met and when violated. We apply the model to a real dataset and contrast estimates obtained with and without predictors, and for different subsets of data. We find that, even with model assumptions perfectly met, estimation of the total number of species can be poor in scenarios where many species are missed (>15%–20%) and that commonly used priors can accentuate overestimation. Our tests show that estimation can often be robust to violations of assumptions about the statistical distributions describing variation of occupancy and detectability among species, but lower‐tail deviations can result in large biases. We obtain substantially different estimates from alternative analyses of our real dataset, with results suggesting that missing relevant predictors in the model can result in richness underestimation. In summary, estimates of total richness are sensitive to model structure and often uncertain. Appropriate selection of priors, testing of assumptions, and model refinement are all important to enhance estimator performance. Yet, these do not guarantee accurate estimation, particularly when many species remain undetected. While statistical models can provide useful insights, expectations about accuracy in this challenging prediction task should be realistic. Where knowledge about species numbers is considered truly critical for management or policy, survey effort should ideally be such that the chances of missing species altogether are low.     

Bayesian estimation of species richness from quadrat sampling data in the presence of prior information

We consider the problem of estimating the number of species of an animal community. It is assumed that it is possible to draw up a list of species liable to be present in this community. Data are collected from quadrat sampling. Models considered in this article separate the assumptions related to the experimental protocol and those related to the spatial distribution of species in the quadrats. Our parameterization enables us to incorporate prior information on the presence, detectability, and spatial density of species. Moreover, we elaborate procedures to build the prior distributions on these parameters from information furnished by external data. A simulation study is carried out to examine the influence of different priors on the performances of our estimator. We illustrate our approach by estimating the number of nesting bird species in a forest.     

The relationships between local and regional species richness and spatial turnover

Aim To determine the empirical relationships between species richness and spatial turnover in species composition across spatial scales. These have remained little explored despite the fact that such relationships are fundamental to understanding spatial diversity patterns. Location South‐east Scotland. Methods Defining local species richness simply as the total number of species at a finer resolution than regional species richness and spatial turnover as turnover in species identity between any two or more areas, we determined the empirical relationships between all three, and the influence of spatial scale upon them, using data on breeding bird distributions. We estimated spatial turnover using a measure independent of species richness gradients, a fundamental feature which has been neglected in theoretical studies. Results Local species richness and spatial turnover exhibited a negative relationship, which became stronger as larger neighbourhood sizes were considered in estimating the latter. Spatial turnover and regional species richness did not show any significant relationship, suggesting that spatial species replacement occurs independently of the size of the regional species pool. Local and regional species richness only showed the expected positive relationship when the size of the local scale was relatively large in relation to the regional scale. Conclusions Explanations for the relationships between spatial turnover and local and regional species richness can be found in the spatial patterns of species commonality, gain and loss between areas.     

Spatial nonstationarity and scale-dependency in the relationship between species richness and environmental determinants for the sub-Saharan endemic avifauna

Aim This article aims to test for and explore spatial nonstationarity in the relationship between avian species richness and a set of explanatory variables to further the understanding of species diversity variation. Location Sub‐Saharan Africa. Methods Geographically weighted regression was used to study the relationship between species richness of the endemic avifauna of sub‐Saharan Africa and a set of perceived environmental determinants, comprising the variables of temperature, precipitation and normalized difference vegetation index. Results The relationships between species richness and the explanatory variables were found to be significantly spatially variable and scale‐dependent. At local scales > 90% of the variation was explained, but this declined at coarser scales, with the greatest sensitivity to scale variation evident for narrow ranging species. The complex spatial pattern in regression model parameter estimates also gave rise to a spatial variation in scale effects. Main conclusions Relationships between environmental variables are generally assumed to be spatially stationary and conventional, global, regression techniques are therefore used in their modelling. This assumption was not satisfied in this study, with the relationships varying significantly in space. In such circumstances the average impression provided by a global model may not accurately represent conditions locally. Spatial nonstationarity in the relationship has important implications, especially for studies of species diversity patterns and their scaling.     

Regional patterns of diversity and estimates of global insect species richness

The total number of insect species in the world is an important if elusive figure. We use a fresh approach to estimate global insect species richness, based on biogeographic patterns of diversity of well or better documented taxa. Estimates generated by various calculations, all variations on a theme, largely serve to substantiate suggestions that insect species are likely to number around 10 million or less.