The influence of productivity on the species richness of plants: a critical assessment

Despite much scrutiny the relationship between productivity and species richness remains controversial, and there is little agreement about causal processes. We present the results of a survey of 159 productivity-plant species richness (P-PSR) relationships from 131 published studies. We critically assessed each study with respect to experimental design and for the appropriateness of the surrogates used for productivity. We were able to accept only 60 of the reported relationships as robust tests of the P-PSR relationship and a further 18 as robust tests of the biomass species richness relationship. Previous analyses have found that unimodal P-PSR relationships predominate. In contrast, we found that, in studies that used data of continental to global extent, all P-PSR relationships were positive regardless of grain, that almost all were also positive in data sets of regional extent, and that unimodal relationships were not dominant even in studies of fine grain or small spatial extent. Our results differ substantially from previous meta-analyses because previous studies have included a large number of studies that do not meet basic experimental design criteria for objectively testing P-PSR relationships. These results have important implications for theory that attempts to explain species richness patterns. We critically review four dominant theories in light of our results and develop new falsifiable predictions of relationship from these theories at both small and large spatial scales.     

Effects of productivity on biodiversity in forest ecosystems across the United States and China

In the global campaign against biodiversity loss in forest ecosystems, land managers need to know the status of forest biodiversity, but practical guidelines for conserving biodiversity in forest management are lacking. A major obstacle is the incomplete understanding of the relationship between site primary productivity and plant diversity, due to insufficient ecosystem‐wide data, especially for taxonomically and structurally diverse forest ecosystems. We investigated the effects of site productivity (the site's inherent capacity to grow timber) on tree species richness across 19 types of forest ecosystems in North America and China through 3 ground‐sourced forest inventory data sets (U.S. Forest Inventory and Analysis, Cooperative Alaska Forest Inventory, and Chinese Forest Management Planning Inventory). All forest types conformed to a consistent and highly significant (P < 0.001) hump‐shaped unimodal relationship, of which the generalized coefficients of determination averaged 20.5% over all the forest types. That is, tree species richness first increased as productivity increased at a progressively slower rate, and, after reaching a maximum, richness started to decline. Our consistent findings suggest that forests of high productivity would sustain few species because they consist mostly of flat homogeneous areas lacking an environmental gradient along which a diversity of species with different habitats can coexist. The consistency of the productivity–biodiversity relationship among the 3 data sets we examined makes it possible to quantify the expected tree species richness that a forest stand is capable of sustaining, and a comparison between the actual species richness and the sustainable values can be useful in prioritizing conservation efforts.     

Quality of basic data and method to identify shape affect richness-altitude relationships in meta-analysis

We compiled 109 species richness-altitude (SRA) relationships in arthropods to test the hypothesis that identification of shape and robustness of pattern are contingent on the selection of studies included in meta-analysis. We used attributes of their sampling design to distinguish three subsets of data according to stringent, intermediate, and lax selection criteria. We tested (1) whether uncertainty over identification of shape increases as the criteria of inclusion of studies relaxes and (2) whether studies that conform to stringent selection criteria show robustness in SRA patterns to variation in method used to identify shape. We identified the shape of each SRA relationship using statistical and visual methods; data sets that suggested several shapes as equally likely were sorted out by consensus. Arthropods suggested multiple forms in the SRA relationship, with predominance of hump-shaped patterns in the stringent subset. Uncertainty over identification of shape increased after application of intermediate and lax selection criteria. The method of analysis interacted with the quality of basic data to influence the relative distribution of patterns. We concluded that the gathering of large quantities of data is insufficient and that critical evaluation of literature is crucial to infer with confidence the general shape of ecological patterns in meta-analysis.     

Developing a common currency for stable isotope analyses of nesting marine turtles

Understanding geospatial linkages is critical to the development of appropriate management and conservation strategies for migratory species. Stable isotope analysis is a powerful tool that is performed routinely across taxa to unravel migratory connectivity. Marine turtles are a highly migratory and widely distributed taxon, but are largely studied at breeding areas. Isotopic values of several slow turnover rate tissues have been used to identify often distant foraging areas. However, as more isotopic data from various tissues become available, the relationships between tissues need to be calculated to permit meta-analyses to elucidate isotopic patterns across broader spatiotemporal scales. We used several commonly collected tissues (blood, skin, fresh eggs and unhatched eggs) collected simultaneously from loggerhead turtles (Caretta caretta) to develop a common currency for stable isotope analysis studies conducted on the nesting beach. We found highly significant relationships between the tissue signatures (r ² ranged from 0.83 to 0.96) and developed equations to convert isotopic values from one tissue to another. We examined inter- and intra-clutch isotopic variability and found that a single sampling event over the 4-month nesting season adequately defined the loggerhead female foraging area. Consequently, we propose using unhatched eggs as a common currency in stable isotope studies of nesting loggerheads. Unhatched eggs represent a noninvasive and nondestructive method that enables more extensive (both numerically and spatially) sampling. Given similar physiologies, analogous relationships might be derived in other sea turtle species.     

贺兰山高山草甸生物多样性和地上生物量的关系

在对贺兰山高山草甸进行群落调查的基础上,研究高山草甸生物多样性和地上生物量与环境因子之间的关系,进而分析生物多样性和地上生物量的关系.结果表明:(1)地上生物量主要与土层深度成正相关关系.(2)海拔高度与生物多样性成负相关关系,而其它影响物种丰富度或Shannon指数的环境因子仅在个别群落类型中起作用.(3)生物多样性与地上生物量主要呈单峰曲线关系.     

Null model analysis of species associations using abundance data

The influence of negative species interactions has dominated much of the literature on community assembly rules. Patterns of negative covariation among species are typically documented through null model analyses of binary presence/absence matrices in which rows designate species, columns designate sites, and the matrix entries indicate the presence (1) or absence (0) of a particular species in a particular site. However, the outcome of species interactions ultimately depends on population-level processes. Therefore, patterns of species segregation and aggregation might be more clearly expressed in abundance matrices, in which the matrix entries indicate the abundance or density of a species in a particular site. We conducted a series of benchmark tests to evaluate the performance of 14 candidate null model algorithms and six covariation metrics that can be used with abundance matrices. We first created a series of random test matrices by sampling a metacommunity from a lognormal species abundance distribution. We also created a series of structured matrices by altering the random matrices to incorporate patterns of pairwise species segregation and aggregation. We next screened each algorithm-index combination with the random and structured matrices to determine which tests had low Type I error rates and good power for detecting segregated and aggregated species distributions. In our benchmark tests, the best-performing null model does not constrain species richness, but assigns individuals to matrix cells proportional to the observed row and column marginal distributions until, for each row and column, total abundances are reached. Using this null model algorithm with a set of four covariance metrics, we tested for patterns of species segregation and aggregation in a collection of 149 empirical abundance matrices and 36 interaction matrices collated from published papers and posted data sets. More than 80% of the matrices were significantly segregated, which reinforces a previous meta-analysis of presence/absence matrices. However, using two of the metrics we detected a significant pattern of aggregation for plants and for the interaction matrices (which include plant-pollinator data sets). These results suggest that abundance matrices, analyzed with an appropriate null model, may be a powerful tool for quantifying patterns of species segregation and aggregation.     

Defining and measuring the impact of dynamic traits on interspecific interactions

Trait- and density-mediated indirect effects describe different pathways by which indirect interactions in food webs are propagated from one species to another, through changes in intermediate species. A series of articles in Ecology has progressively altered the original definitions of "trait-mediated" to the point where understanding is being impeded. The most recent of these articles are two meta-analyses that use "trait-mediated" to describe the demographic costs to a prey species of employing anti-predator defenses. These same articles introduce a companion term, "density-mediated interaction", apparently to describe direct and indirect interactions that only involve changes in population density due to consumption by predators. This new terminology has many disadvantages, including (1) using a general term for a relatively narrow group of processes; (2) using "mediated" in a manner inconsistent with existing terminology; (3) confusing the accepted definitions of different types of indirect effects; and (4) providing a highly incomplete measure of the impact of behavior on the predator-prey interaction. Solutions to these problems and the meaning of the meta-analyses are discussed.     

Metacommunity models predict the local-regional species richness relationship in a natural system

Many natural communities exhibit positive relationships between local and regional species richness (LSR-RSR relationships), which can be either linear or curvilinear. Previous models have shown that the form of this relationship depends on the relative rates of colonization and extinction and the sensitivity of these rates to competition. We use simple models to show that the LSR-RSR relationship also depends on the type of metacommunity structure (Levins-like or mainland-island), and our models generate a wider range of realistic forms than do most previous models. We parameterize and test our models with two independent data sets for Daphnia in rock pools on islands in Finland and Sweden. We find that the Levins-like model with competition correctly predicts the observed LSR-RSR relationship and provides the best fit to the average local species richness per island. Simulations show that our models are robust to relaxing our assumption of identical species properties. Our study is one of the first to make and successfully test quantitative predictions for how a widely studied community pattern, the LSR-RSR relationship, arises from metacommunity dynamics.     

The derivation of species response curves with Gaussian logistic regression is sensitive to sampling intensity and curve characteristics

We investigated quantitatively the sensitivity of plant species response curves to sampling characteristics (number of plots, occurrence and frequency of species), along a simulated pH gradient. We defined 54 theoretical unimodal response curves, issued from combinations of six values for optimum (opt = 3, 4, …, 8), three values for tolerance (tol = 0.5, 1.0, and 1.5, sensu ter Braak and Looman [ter Braak, C.J.F., Looman, C.W.N., 1986. Weighted averaging, logistic regression and the Gaussian response model. Vegetatio 65, 3–11]), and three values for maximum probability of presence (p_max = 0.05, 0.20, and 0.50). For each of these 54 theoretical response curves, we built artificial binary data sets (presence/absence) to test the influence of species occurrence, frequency, or number of available plots. With real data extracted from EcoPlant, a phytoecological database for French forests [Gégout, J.-C., Coudun, Ch., Bailly, G., Jabiol, B., 2005. EcoPlant: a forest sites database linking floristic data with soil characteristics and climatic conditions. J. Veg. Sci. 16, 257–260], we compared the ecological response of 50 plant species to soil pH, based first on a small data set (100 randomly sampled plots), and then based on the whole data set available (3810 plots).     

Contrasting plant productivity-diversity relationships across latitude: the role of evolutionary history

The relationship between net primary productivity and biological diversity has been a central topic in ecology for several decades. The unimodal ("hump-back") relationship has been the most widely accepted for plants with the decrease in diversity at high productivity usually attributed to competitive exclusion. However, the relatively small species pool size under high productivity conditions may account for this pattern as well. Small species pool sizes for highly productive habitats are characteristic of temperate regions, where productive habitats for speciation and species migration have historically been rare. In contrast, productive habitats in the tropics have been relatively common during evolutionary history, resulting in large species pools. We hypothesize that evolutionary history contributes to the observed productivity-diversity relationship of plants, and that the productivity-diversity relationship differs between temperate and tropical regions. We investigated the productivity-diversity relationship patterns from 163 case studies throughout the world. Latitude described approximately 80% of the variation in the shape of the relationships. The unimodal relationship was found to dominate in the temperate zone, whereas the positive relationship was significantly more common in the tropics. We detected no influence due to methods of productivity measurement, but unimodal or positive productivity-diversity relationships were more likely within larger ranges of productivity. The length of the productivity gradient did not affect the latitudinal influence. In summary, the shape of the productivity-diversity relationship differs between temperate and tropical regions and the different evolutionary history of the local species pools is a probable cause for the difference.     

A novel statistical method for classifying habitat generalists and specialists

We develop a novel statistical approach for classifying generalists and specialists in two distinct habitats. Using a multinomial model based on estimated species relative abundance in two habitats, our method minimizes bias due to differences in sampling intensities between two habitat types as well as bias due to insufficient sampling within each habitat. The method permits a robust statistical classification of habitat specialists and generalists, without excluding rare species a priori. Based on a user-defined specialization threshold, the model classifies species into one of four groups: (1) generalist; (2) habitat A specialist; (3) habitat B specialist; and (4) too rare to classify with confidence. We illustrate our multinomial classification method using two contrasting data sets: (1) bird abundance in woodland and heath habitats in southeastern Australia and (2) tree abundance in second-growth (SG) and old-growth (OG) rain forests in the Caribbean lowlands of northeastern Costa Rica. We evaluate the multinomial model in detail for the tree data set. Our results for birds were highly concordant with a previous nonstatistical classification, but our method classified a higher fraction (57.7%) of bird species with statistical confidence. Based on a conservative specialization threshold and adjustment for multiple comparisons, 64.4% of tree species in the full sample were too rare to classify with confidence. Among the species classified, OG specialists constituted the largest class (40.6%), followed by generalist tree species (36.7%) and SG specialists (22.7%). The multinomial model was more sensitive than indicator value analysis or abundance-based phi coefficient indices in detecting habitat specialists and also detects generalists statistically. Classification of specialists and generalists based on rarefied subsamples was highly consistent with classification based on the full sample, even for sampling percentages as low as 20%. Major advantages of the new method are (1) its ability to distinguish habitat generalists (species with no significant habitat affinity) from species that are simply too rare to classify and (2) applicability to a single representative sample or a single pooled set of representative samples from each of two habitat types. The method as currently developed can be applied to no more than two habitats at a time.     

Use of Surrogates to Predict the Stressor Response of Imperiled Species

Abstract: Rare or narrowly distributed species may be threatened by stressors to which they have never been exposed or for which data are very limited. In such cases the species response cannot be predicted on the basis of directly measured data, but may be inferred from the response of one or more appropriate surrogate species. Here, I propose a practical way to use the stressor response of one or more surrogate species to develop a working hypothesis or model of the stressor response of the target species. The process has 4 steps: (1) identify one or more candidate surrogate species, (2) model the relationship between the stressor and the response variable of interest for the surrogate species, (3) adapt the stressor–response relationship from the surrogate species to a model for the target species, possibly using Bayesian methods, and (4) incorporate additional data as they become available and adjust the response model of the target species appropriately. I applied the approach to an endangered fish species, the amber darter (Percina antesella), which is potentially threatened by urbanization. I used a Bayesian approach to combine data from a surrogate species (the bronze darter[Percina palmaris]) with available data for the amber darter to produce a model of expected amber darter response. Although this approach requires difficult decisions on the part of the manager, especially in the selection of surrogate species, its value lies in the fact that all assumptions are clearly stated in the form of hypotheses, which may be scrutinized and tested. It therefore provides a rational basis for instituting management policy even in the face of considerable uncertainty.     

全尺度人工湿地中植物多样性对生产力与多样性效应的影响

在中国东南部的全尺度复合垂直流人工湿地中开展2年的植物多样性实验,以研究植物多样性（包括植物物种丰富度和植物组成）对群落生产力与多样性效应（即互补效应、选择效应和净多样性效应）的影响及其产生机制。结果表明,2007年物种丰富度与群落生产力呈线形正相关,而2008年显著的单峰格局,其关系式为：y=-0.213x2＋3.455x＋15.192（R=0.215）。2008年物种丰富度与互补效应呈显著地线形负相关,而2007年呈单峰格局,其关系式为：y=-0.389x2＋6.974x-10.707（R=0.247）,而且2007年与2008年的互补效应与生产力都呈显著的正相关,表明互补效应对生产力的提高有重要作用。然而,2007年与2008年物种丰富度与选择效应之间均没有显著相关性,且选择效应与群落生产力之间也没有显著相关性,表明选择效应对生产力的提高作用不显著。2007年与2008年中物种组成对生产力、互补效应、选择效应与净多样性效应均有显著影响,说明人工湿地的植物配置对其生态系统功能的维持尤为重要。2008年物种丰富度与净多样性效应呈极显著地线形负相关,而2007年呈显著单峰格局,其关系式为：y=-0.329 x2＋5.968 x-12.659（R=0.234）,这种趋势主要是由于植物多样性-生态系统功能关系的影响因素（如物种的竞争力和生态位）在2年中有所变化。同时,2007年与2008年的多样性净效应与生产力都呈显著正相关关系,表明生产力与多样性净效应的变化趋势是同步的。与抽样效应假说不同的是,本实验中单种最高产物种（芦竹）在混种时没有表现出高产,主要是由于生长的分配、资源的竞争力与环境的变化等。     

Publishing science without results and recycling research

Most scientists focus too much on publishing original articles. In doing so, scientists are restricting their writing skills to this form of highly specialised publication, which is poorly readable by scientists from other disciplines. In the context of rising interdisciplinary research and data abundance, there is a need for more publications that recycle existing research and communicate to a wider audience. Therefore, I present here five types of publications that do not require additional experiments, namely reviews, methods, data papers, meta-analyses and videos. Benefits include more citations, larger visibility, wider dissemination, easier job finding, grant success and better recycling of research.     

A hidden species-area curve

The species-area curve, which describes the relationship between the number of observed species in a geographical region and the area of the region, plays a central role in biogeography. Beyond its scientific importance, the species-area curve is commonly used to assess the loss of species due to habitat loss. When the species-area curve is estimated from spatial samples, the existence of species with low or highly spatially variable abundance exaggerates the true rate at which species accumulate with area. Here, a hidden species-area curve is defined that accounts for this sampling effect and its estimation by maximum likelihood is outlined. Both the species-area curve and the hidden species-area curve are conditioned on the observed species list; thus the analysis does not depend on the total number of observed and unobserved species, that is, species richness. The method is tested by sub-sampling some tropical forest data and found to work well. It is also applied to a classic data set from the deep sea. For these data, accounting for this sampling effect has a large impact.     

A global evaluation of metabolic theory as an explanation for terrestrial species richness gradients

We compiled 46 broadscale data sets of species richness for a wide range of terrestrial plant, invertebrate, and ectothermic vertebrate groups in all parts of the world to test the ability of metabolic theory to account for observed diversity gradients. The theory makes two related predictions: (1) In-transformed richness is linearly associated with a linear, inverse transformation of annual temperature, and (2) the slope of the relationship is near -0.65. Of the 46 data sets, 14 had no significant relationship; of the remaining 32, nine were linear, meeting prediction 1. Model I (ordinary least squares, OLS) and model II (reduced major axis, RMA) regressions then tested the linear slopes against prediction 2. In the 23 data sets having nonlinear relationships between richness and temperature, split-line regression divided the data into linear components, and regressions were done on each component to test prediction 2 for subsets of the data. Of the 46 data sets analyzed in their entirety using OLS regression, one was consistent with metabolic theory (meeting both predictions), and one was possibly consistent. Using RMA regression, no data sets were consistent. Of 67 analyses of prediction 2 using OLS regression on all linear data sets and subsets, two were consistent with the prediction, and four were possibly consistent. Using RMA regression, one was consistent (albeit weakly), and four were possibly consistent. We also found that the relationship between richness and temperature is both taxonomically and geographically conditional, and there is no evidence for a universal response of diversity to temperature. Meta-analyses confirmed significant heterogeneity in slopes among data sets, and the combined slopes across studies were significantly lower than the range of slopes predicted by metabolic theory based on both OLS and RMA regressions. We conclude that metabolic theory, as currently formulated, is a poor predictor of observed diversity gradients in most terrestrial systems.     

Estimating landscape-scale species richness: reconciling frequency- and turnover-based approaches

One hypothesis for why estimators of species richness tend to underestimate total richness is that they do not explicitly account for increases in species richness due to spatial or environmental turnover in species composition (beta diversity). I analyze the similarity of a data set of native trees in Great Smoky Mountains National Park, USA, and assess the robustness of these estimators against recently developed ones that incorporate turnover explicitly: the total species accumulation method (T-S) and a method based on the distance decay of similarity. I show that the T-S estimator can give reliable estimates of species richness, given an appropriate grouping of sites. The estimator based on distance decay of similarity performed poorly. There are two main reasons for this: sample size effects and the assumption that distance decay of similarity exhibits a power law relationship. I show that estimators based on distance-decay relationships exhibit systematically lower rates of distance decay for samples with few individuals per site independent of environmental variation. Second, the data presented here and many other survey data sets exhibit exponential rather than power law distance-decay relationships. Richness estimators that explicitly incorporate beta diversity can be improved by beginning from an exponential distance-decay relationship and adjusting for the systematic errors introduced by small sample sizes.     

Evaluating mortality rates with a novel integrated framework for nonmonogamous species

The conservation of wildlife requires management based on quantitative evidence, and especially for large carnivores, unraveling cause‐specific mortalities and understanding their impact on population dynamics is crucial. Acquiring this knowledge is challenging because it is difficult to obtain robust long‐term data sets on endangered populations and, usually, data are collected through diverse sampling strategies. Integrated population models (IPMs) offer a way to integrate data generated through different processes. However, IPMs are female‐based models that cannot account for mate availability, and this feature limits their applicability to monogamous species only. We extended classical IPMs to a two‐sex framework that allows investigation of population dynamics and quantification of cause‐specific mortality rates in nonmonogamous species. We illustrated our approach by simultaneously modeling different types of data from a reintroduced, unhunted brown bear (Ursus arctos) population living in an area with a dense human population. In a population mainly driven by adult survival, we estimated that on average 11% of cubs and 61% of adults died from human‐related causes. Although the population is currently not at risk, adult survival and thus population dynamics are driven by anthropogenic mortality. Given the recent increase of human‐bear conflicts in the area, removal of individuals for management purposes and through poaching may increase, reversing the positive population growth rate. Our approach can be generalized to other species affected by cause‐specific mortality and will be useful to inform conservation decisions for other nonmonogamous species, such as most large carnivores, for which data are scarce and diverse and thus data integration is highly desirable.     

The role of local and landscape level measures of greenness in modelling boreal plant species richness

Measurements of primary productivity and its heterogeneity based on satellite images can provide useful estimates of species richness and distribution patterns. However, species richness at a given site may depend not only on local habitat quality and productivity but also on the characteristics of the surrounding landscape. In this study we investigated whether the predictions of species richness of plant families in northern boreal landscape in Finland can be improved by incorporating greenness information from the surrounding landscape, as derived from remotely sensed data (mean, maximum, standard deviation and range values of NDVI derived from Landsat ETM), into local greenness models. Using plant species richness data of 28 plant families from 440 grid cells of 25 ha in size, generalized additive models (GAMs) were fitted into three different sets of explanatory variables: (1) local greenness only, (2) landscape greenness only, and (3) combined local and landscape greenness. The derived richness–greenness relationships were mainly unimodal or positively increasing but varied between different plant families, and depended also on whether greenness was measured as mean or maximum greenness. Incorporation of landscape level greenness variables improved significantly both the explanatory power and cross-validation statistics of the models including only local greenness variables. Landscape greenness information derived from remote sensing data integrated with local information has thus the potentiality to improve predictive assessments of species richness over extensive and inaccessible areas, especially in high-latitude landscapes. Overall, the significant relationship between plants and surrounding landscape quality detected here suggests that landscape factors should be considered in preserving species richness of boreal environments, as well as in conservation planning for biodiversity in other environments.     

Factors affecting the detection distances of reef fish: implications for visual counts

Detection patterns of coral reef fish were assessed from the meta-analysis of distance sampling surveys performed by visual census in New Caledonia and French Polynesia, from 1986 to 1999. From approximately 100,000 observations relating to 593 species, the frequency distributions of fish detection distances perpendicular to the transect line were compared according to species characteristics and sampling conditions. The shape and extension of these detection profiles varied markedly with fish size, shyness, and crypticity, indicating strong differences of detectability across species. Detection of very small and cryptic fish decreased strongly 1 m away from the line. Conversely, sightings of shy and large species were excessively low in the first meters due to diver avoidance prior to detection. The larger the fish, the greater the fleeing distance. Distance data underscore how inconsistent detectability biases across species and sites can affect the accuracy of visual censuses when assessing coral reef fish populations.