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.     

Multivariate analysis of spatial patterns: a unified approach to local and global structures

We propose a new approach to the multivariate analysis of data sets with known sampling site spatial positions. A between-sites neighbouring relationship must be derived from site positions and this relationship is introduced into the multivariate analyses through neighbouring weights (number of neighbours at each site) and through the matrix of the neighbouring graph. Eigenvector analysis methods (e.g. principal component analysis, correspondence analysis) can then be used to detect total, local and global structures. The introduction of the D-centring (centring with respect to the neighbouring weights) allows us to write a total variance decomposition into local and global components, and to propose a unified view of several methods. After a brief review of the matrix approach to this problem, we present the results obtained on both simulated and real data sets, showing how spatial structure can be detected and analysed. Freely available computer programs to perform computations and graphical displays are proposed.     

Planning for robust reserve networks using uncertainty analysis

Planning land-use for biodiversity conservation frequently involves computer-assisted reserve selection algorithms. Typically such algorithms operate on matrices of species presence–absence in sites, or on species-specific distributions of model predicted probabilities of occurrence in grid cells. There are practically always errors in input data—erroneous species presence–absence data, structural and parametric uncertainty in predictive habitat models, and lack of correspondence between temporal presence and long-run persistence. Despite these uncertainties, typical reserve selection methods proceed as if there is no uncertainty in the data or models. Having two conservation options of apparently equal biological value, one would prefer the option whose value is relatively insensitive to errors in planning inputs. In this work we show how uncertainty analysis for reserve planning can be implemented within a framework of information-gap decision theory, generating reserve designs that are robust to uncertainty. Consideration of uncertainty involves modifications to the typical objective functions used in reserve selection. Search for robust-optimal reserve structures can still be implemented via typical reserve selection optimization techniques, including stepwise heuristics, integer-programming and stochastic global search.     

Management and exploitation dynamics of small-scale fisheries in the Bay of Biafra: An integrative analysis of purse seine fishing activity

For many decades, fisheries research has focused on stock assessment and the impact of the fishery effort on resources. Although this knowledge remains necessary, a more integrated analysis of the joint dynamics of resource and operational activities is needed to provide more useful advice for the management of fishery systems. Since 1994 a new approach to fishery science has been carried out for Cameroonian small-scale fisheries, the aim being the incorporation into fishery science of research on fishery management, fishing processes and fishermen's behaviour. This paper presents a more systemic data approach which combines biological parameters and operational factor analyses with the goal of sustainable development. From two years of data collected on the purse seine fishing units operating in the Bay of Biafra (2002 and 2003), a set of three correspondence analyses is applied: (1) to the length frequency distribution of Ethmalosa fimbriata, (2) to the number of visits per fishing ground, and (3) to the species appearance frequency in the landings. These three analyses were plotted per month-year period. The results are presented in the form of an annual exploitation cycle, linking fishing grounds, the main species caught, and corresponding fishing period.     

Arthropod assemblages are best predicted by plant species composition

Insects and spiders comprise more than two-thirds of the Earth's total species diversity. There is wide concern, however, that the global diversity of arthropods may be declining even more rapidly than the diversity of vertebrates and plants. For adequate conservation planning, ecologists need to understand the driving factors for arthropod communities and devise methods, that provide reliable predictions when resources do not permit exhaustive ground surveys. Which factor most successfully predicts arthropod community structure is still a matter of debate, however. The purpose of this study was to identify the factor best predicting arthropod assemblage composition. We investigated the species composition of seven functionally different arthropod groups (epigeic spiders, grasshoppers, ground beetles, weevils, hoppers, hoverflies, and bees) at 47 sites in The Netherlands comprising a range of seminatural grassland types and one heathland type. We then compared the actual arthropod composition with predictions based on plant species composition, vegetation structure, environmental data, flower richness, and landscape composition. For this we used the recently published method of predictive co-correspondence analysis, and a predictive variant of canonical correspondence analysis, depending on the type of predictor data. Our results demonstrate that local plant species composition is the most effective predictor of arthropod assemblage composition, for all investigated groups. In predicting arthropod assemblages, plant community composition consistently outperforms both vegetation structure and environmental conditions (even when the two are combined), and also performs better than the surrounding landscape. These results run against a common expectation of vegetation structure as the decisive factor. Such expectations, however, have always been biased by the fact that until recently no methods existed that could use an entire (plant) species composition in the explanatory role. Although more recent experimental diversity work has reawakened interest in the role of plant species, these studies still have not used (or have not been able to use) entire species compositions. They only consider diversity measures, both for plant and insect assemblages, which may obscure relationships. The present study demonstrates that the species compositions of insect and plant communities are clearly linked.     

Copépodes planctoniques du lagon de Nouvelle-Calédonie: facteurs écologiques et associations d'espèces

In the southwestern part of the lagoon of New Caledonia (South Pacific Ocean), a plankton sampling programme was conducted from February 1978 to April 1979. During 11 cruises, 5 stations in the open sea and various bays were sampled at approximately monthly intervals. A transect of 3 stations (mid lagoon, near-reef and barrier-reef channel) visited each fortnight, completed the sampling programme. The copepods collected were identified to the specific level, counted and the 52 most abundant species analysed for seasonal and regional variations. Cruise and transect data, dealt with separately, were analysed as follows: (a) Correspondence analyses (reciprocal averaging) of qualitative and quantitative copepod counts were made; these revealed the relationships between species and between samples by means of factorial design. After computation of factorial axes, the barycentres of different sets of samples were projected as reference points (zero weight) in the factorial space; these reference points, characterizing different stations and sampling times, facilitate ecological interpretation. (b) Species partition was achieved by two successive methods: non-hierarchical, followed by hierarchical classification. Between-species distance was computed from their co-ordinates on the factorial axis. (c) The clusters of species obtained were plotted in the factorial planes to assess ecological preference. Then, clusters from cruise and transect data sets were compared to improve the copepod classification. The main ecological factors appear to be (i) spatial patterns, (ii) seasonal temperature cycle, (iii) changes in wind force and direction. Different populations inhabit the open sea, near-reef, mid-lagoon, shallow and deep-bay waters. Acartia australis outnumbers all other species in the reef vicinity. A. amboinensis is the most abundant in the deep, fjord-like bay. Canthocalanus pauper, Paracalanus parvus, Bestiola sp., Centropages orsinii, A. bispinosa are characteristic of shallow bays. In the factorial structure produced by correspondence analysis, certain seasonal barycentres appear close to some station barycentres: summer close to mid-lagoon, winter close to open sea, and spring close to near-reef barycentres, respectively. This may be explained by the seasonal dynamics of the lagoon water. After the heavy summer rainfalls, freshwater runoff carries midlagoon plankton towards the open sea; conversely, during the winter westerly gales, oceanic species enter the lagoon through the barrier-reef channel or above the reef with the swell breakers. In October, the surface-layer current induced by the strong trade wind carries large swarms of A. australis out to the open sea. Finally, the variations of plankton populations and biomass in the lagoon seem to be governed by the direction of water flow across the reef channels. Enrichment factors are terrestrial sediment wash-out after rainfall and, probably, trade wind-induced upwelling. Therefore, the more or less steady state of the lagoon's plankton biomass may result from the fact that enrichment factors are also exportation factors. Reciprocally, the entry of plankton-poor oceanic water cannot increase the plankton biomass in the lagoon.     

SOCPROG programs: analysing animal social structures

SOCPROG is a set of programs which analyses data on animal associations. Data usually come from observations of the social behaviour of individually identifiable animals. Associations among animals, sampling periods, restrictions on the data and association indices can be defined very flexibly. SOCPROG can analyse data sets including 1,000 or more individuals. Association matrices are displayed using sociograms, principal coordinates analysis, multidimensional scaling and cluster analyses. Permutation tests, Mantel and related tests and matrix correlation methods examine hypotheses about preferred associations among individuals and classes of individual. Weighted network statistics are calculated and can be tested against null hypotheses. Temporal analyses include displays of lagged association rates (rates of reassociation following an association). Models can be fitted to lagged association rates. Multiple association measures, including measures produced by other programs such as genetic or range use data, may be analysed using Mantel tests and principal components analysis. SOCPROG also performs mark-recapture population analyses and movement analyses. SOCPROG is written in the programming language MATLAB and may be downloaded free from the World Wide Web.     

Variation partitioning of species data matrices: estimation and comparison of fractions

Establishing relationships between species distributions and environmental characteristics is a major goal in the search for forces driving species distributions. Canonical ordinations such as redundancy analysis and canonical correspondence analysis are invaluable tools for modeling communities through environmental predictors. They provide the means for conducting direct explanatory analysis in which the association among species can be studied according to their common and unique relationships with the environmental variables and other sets of predictors of interest, such as spatial variables. Variation partitioning can then be used to test and determine the likelihood of these sets of predictors in explaining patterns in community structure. Although variation partitioning in canonical analysis is routinely used in ecological analysis, no effort has been reported in the literature to consider appropriate estimators so that comparisons between fractions or, eventually, between different canonical models are meaningful. In this paper, we show that variation partitioning as currently applied in canonical analysis is biased. We present appropriate unbiased estimators. In addition, we outline a statistical test to compare fractions in canonical analysis. The question addressed by the test is whether two fractions of variation are significantly different from each other. Such assessment provides an important step toward attaining an understanding of the factors patterning community structure. The test is shown to have correct Type I. error rates and good power for both redundancy analysis and canonical correspondence analysis.     

Identifying the possibilities and pitfalls of conducting IUCN Red List assessments from remotely sensed habitat information based on insights from poorly known Cuban mammals

The International Union for Conservation of Nature's Red List of Threatened Species (RLS) is the key global tool for objective, repeatable assessment of species’ extinction risk status, and plays an essential role in tracking biodiversity loss and guiding conservation action. Satellite remote sensing (SRS) data sets on global ecosystem distributions and functioning show exciting potential for informing range-based RLS assessment, but their incorporation has been restricted by low temporal resolution and coverage of data sets, lack of incorporation of degradation-driven habitat loss, and noninclusion of assumptions related to identification of changing habitat distributions for taxa with varying habitat dependency and ecologies. For poorly known mangrove-associated Cuban hutias (Mesocapromys spp.), we tested the impact of possible assumptions regarding these issues on range-based RLS assessment outcomes. Specifically, we used annual (1985–2018) Landsat data and land-cover classification and habitat degradation analyses across different internal time series slices to simulate range-based RLS assessments for our case study taxa to explore potential assessment uncertainty arising from temporal SRS data set coverage, incorporating proxies of (change in) habitat quality, and assumptions on spatial scaling of habitat extent for RLS parameter generation. We found extensive variation in simulated species-specific range-based RLS assessments, and this variation was mostly associated with the time series over which parameters were estimated. However, results of some species-specific assessments differed by up to 3 categories (near threatened to critically endangered) within the same time series, due to the effects of incorporating habitat quality and the spatial scaling used in RLS parameter estimation. Our results showed that a one-size-fits-all approach to incorporating SRS information in RLS assessment is inappropriate, and we urge caution in conducting range-based assessments with SRS for species for which habitat dependence on specific ecosystem types is incompletely understood. We propose novel revisions to parameter spatial scaling guidelines to improve integration of existing time series data on ecosystem change into the RLS assessment process.     

Modelling skewed data with many zeros: A simple approach combining ordinary and logistic regression

We discuss a method for analyzing data that are positively skewed and contain a substantial proportion of zeros. Such data commonly arise in ecological applications, when the focus is on the abundance of a species. The form of the distribution is then due to the patchy nature of the environment and/or the inherent heterogeneity of the species. The method can be used whenever we wish to model the data as a response variable in terms of one or more explanatory variables. The analysis consists of three stages. The first involves creating two sets of data from the original: one shows whether or not the species is present; the other indicates the logarithm of the abundance when it is present. These are referred to as the presence data and the log-abundance data, respectively. The second stage involves modelling the presence data using logistic regression, and separately modelling the log-abundance data using ordinary regression. Finally, the third stage involves combining the two models in order to estimate the expected abundance for a specific set of values of the explanatory variables. A common approach to analyzing this sort of data is to use a ln (y+c) transformation, where c is some constant (usually one). The method we use here avoids the need for an arbitrary choice of the value of c, and allows the modelling to be carried out in a natural and straightforward manner, using well-known regression techniques. The approach we put forward is not original, having been used in both conservation biology and fisheries. Our objectives in this paper are to (a) promote the application of this approach in a wide range of settings and (b) suggest that parametric bootstrapping be used to provide confidence limits for the estimate of expected abundance.     

Rapid assessment of postfire plant invasions in coniferous forests of the western United States.

Fire is a natural part of most forest ecosystems in the western United States, but its effects on nonnative plant invasion have only recently been studied. Also, forest managers are engaging in fuel reduction projects to lessen fire severity, often without considering potential negative ecological consequences such as nonnative plant species introductions. Increased availability of light, nutrients, and bare ground have all been associated with high-severity fires and fuel treatments and are known to aid in the establishment of nonnative plant species. We use vegetation and environmental data collected after wildfires at seven sites in coniferous forests in the western United States to study responses of nonnative plants to wildfire. We compared burned vs. unburned plots and plots treated with mechanical thinning and/or prescribed burning vs. untreated plots for nonnative plant species richness and cover and used correlation analyses to infer the effect of abiotic site conditions on invasibility. Wildfire was responsible for significant increases in nonnative species richness and cover, and a significant decrease in native cover. Mechanical thinning and prescribed fire fuel treatments were associated with significant changes in plant species composition at some sites. Treatment effects across sites were minimal and inconclusive due to significant site and site x treatment interaction effects caused by variation between sites including differences in treatment and fire severities and initial conditions (e.g., nonnative species sources). We used canonical correspondence analysis (CCA) to determine what combinations of environmental variables best explained patterns of nonnative plant species richness and cover. Variables related to fire severity, soil nutrients, and elevation explained most of the variation in species composition. Nonnative species were generally associated with sites with higher fire severity, elevation, percentage of bare ground, and lower soil nutrient levels and lower canopy cover. Early assessments of postfire stand conditions can guide rapid responses to nonnative plant invasions.     

Distribution and ecology of deep-water mollusks from the continental slope,southeastern Gulf of California,Mexico

Specimens of deep-water mollusks were collected with a bottom dredge in the SE Gulf of California during four cruises (TALUD project) in 2000–2001. A total of 24 species (16 Pelecypoda, 5 Gastropoda, and 3 Scaphopoda) were collected. Analyses of environmental data (depth, epibenthic temperature and oxygen content, sediment texture and organic matter content) associated with each record indicated that the community diversity of these deep-water mollusks was related to oxygen concentration and density was correlated with depth. A canonical correspondence analysis (CCA) indicated that the five environmental variables explained 53.8% of observed variance in the model. In a multiple regression analysis, density (ind l⁻¹) was best correlated with dissolved oxygen concentration (R = −0.25), followed by temperature (R = −0.20), organic matter content (R = −0.15), and depth (R = −0.12). As oxygen best explained variance in the CCA it was selected to perform a single correspondence analysis (CA) using the most abundant and frequent species (two Pelecypoda and three Scapophoda). The analysis shows that pelecypods occur in near anoxic values while scaphopods occur in intermediate oxygen concentrations.     

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.     

Evaluating Causes of Population Change in North American Insectivorous Songbirds

Although the North American Breeding Bird Survey (BBS) is a principal source of information regarding populations of most North American bird species, many features of the survey complicate analysis of population change. Correlation studies based on BBS data cannot be used to define cause and effect relationships unambiguously. Recently, Böhning-Gaese et al. (1993) presented an analysis of population trends in insectivorous songbirds using data from the BBS. They concluded that predation has played an important role in influencing population trends. We review aspects of the analysis methods for estimating population trends (e.g., observer effects, data subset) and for associating mean trends with species attributes (e.g., confounding of attributes). Using alternative analyses of the same BBS data, we demonstrate that the evidence that predation is associated with population declines is weaker than they suggested. Based on our analyses the only factor among those tested that was consistently associated with population trends was migration status (short-distance migrant/resident versus long-distance migrant) during the period 1978–1987. Also, we present evidence that the harsh winters of the mid-1970s severely depressed populations of short-distance migrant species and may have been responsible for the observed associations between migration status and population trends.     

2种典型家用消毒剂对水生生物的急性毒性影响及生态效应阈值研究

家用消毒剂大量用于日常生活中,进入水体环境后对水生生物产生潜在危害效应,目前尚缺乏保护水生生物安全的生态效应阈值。本研究以2种典型家用消毒剂(有效成分分别为对氯间二甲苯酚和次氯酸钠,前者命名为消毒剂A、后者为消毒剂B)为研究对象,开展其对8种不同营养级淡水水生生物的急性毒性效应研究。结果表明,除底栖动物外,消毒剂B对藻类、溞类和鱼类的急性毒性均高于消毒剂A;我国本土种稀有鮈鲫对2种消毒剂的敏感性高于其他2种鱼类;2种消毒剂对藻类的毒性高低均为近头状伪蹄形藻斜生栅藻蛋白核小球藻;近头状伪蹄形藻对2种消毒剂最敏感。基于上述毒性数据构建了物种敏感分布(SSD)曲线,计算对保护95%的物种不受影响时所对应的污染物浓度(HC5),并结合评估因子法推导出2种消毒剂预测无效应浓度(PNEC)值作为急性生态效应阈值,消毒剂A和消毒剂B的PNEC值分别为13.16 mg·L~(-1)(有效成分对氯间二甲苯酚PNEC值为0.33 mg·L~(-1))和0.71 mg·L~(-1)(有效成分次氯酸钠PNEC值为0.01 mg·L~(-1)),消毒剂A对淡水生物的PNEC比消毒剂B大了一个数量级,表明相较于消毒剂B,消毒剂A对水生态环境更为友好。本研究结果可为制订典型家用消毒剂的水质基准提供科学依据。     

Robust, comparable population metrics through collaborative photo-monitoring of whale sharks Rhincodon typus.

The formulation of conservation policy for species that are rare and migratory requires broad cooperation to ensure that adequate levels of standardized data collection are achieved and that the results of local analyses are comparable. Estimates of apparent survival rate, relative change in abundance, and proportions of newly marked and returning individuals can inform local management decisions while highlighting corresponding changes at other linked research stations. We have applied computer-assisted photo-identification and mark-recapture population modeling to whale sharks Rhincodon typus at Ningaloo Marine Park (NMP), Western Australia, to create a baseline trend for comparison with other regional aggregations of the species. We estimate several ecological parameters of interest, including an average apparent survival rate of 0.55 yr(-1) for sharks newly marked (new) and 0.83 yr(-1) for sharks captured in multiple seasons (philopatric). The average proportion of philopatric sharks is found to be 0.65 of the total population, and we derive an average population growth rate of 1.12 yr(-1) for them. Our analysis uncovered significant heterogeneity in capture and survival probabilities in this study population; our chosen model structures and data analysis account for these influences and demonstrate a good overall fit to the time-series data. The results show good correspondence between capture probability and an available measure of recapture effort, suggesting that unmodeled systematic effects contribute insignificantly to the model fits. We find no evidence of a decline in the whale shark population at NMP, and our results provide metrics of value to their future management. Overall, our study suggests an effective approach to analyzing and modeling mark-recapture data for a rare species using computer-assisted photo-identification and opportunistic data collection from ecotourism to ensure the quality and volume of data required for population analysis.     

Les peuplements benthiques (zoobenthos) de la région de Marseille (France): aspects méthodologiques de la délimitation des peuplements par les méthodes mathématiques

A data matrix comprising 480 species at 25 stations and using three parameters (density, frequency, biomass) is presented. The number of species has been reduced as a function of the following criteria: relative percentage of total abundance, total information (Shannon measure), quantity of information per station. From these reduced matrices, three correlation coefficients have been calculated: the linear correlation coefficient r, the Kendall rank coefficient τ and the Pearson's ? coefficient. For comparison purposes the 20 matrices may be analysed by hierarchical classification (dendrogram) and principal component analysis before and after rotation. The stations groups delimited are ecologically significant. When a too small matrix or the coefficient τ and ? are used, grouping is indistinct. Groups can be defined very clearly when the coefficient r is calculated on a matrix including more than 65 species. The observed group pattern can be explained by 4 factors: Huveaune and Rhône river-outflow and two types of currents (coastal and whirling) produced by different winds.     

Comparative larval development of the shipworms Bankia gouldi and Teredo navalis

Larvae of Bankia gouldi (Bartsch) and Teredo navalis L. were reared in the laboratory at various temperatures and salinities. T. navalis spawned at lower temperatures than B. gouldi. T. navalis larvae were released at temperatures from 13° to 30°C; in B. gouldi, spawning occurred from 17.5° to 30°C. Both species released offspring at salinities of 20 and 30‰. Larvae of the two species can be distinguished morphometrically at the earliest pelagic veliger stage and in the pediveliger stage. Average dimensions of newly released T. navalis larvae are 88 x 75 μ (length x height), while the youngest B. gouldi veligers measure 61 x 50 μ. Pediveligers of T. navalis (205 x 239 μ) are smaller than those of B. gouldi (221 x 260 μ). At other stages of larval development the two species appear so similar that they cannot yet readily be distinguished. Under laboratory conditions of 25°C and 30 ‰, the free-swimming life of B. gouldi to the pediveliger stage was about 10 days longer than that of T. navalis (25 and 15 days, respectively). Incubated larval development of T. navalis was estimated to be 5 days at 25°C. Potential competition between larval stages of the two species, and modification of settling behavior by dissolved humic material (Gelbstoff), is discussed.     

Using community‐level metrics to monitor the effects of marine protected areas on biodiversity

Marine protected areas (MPAs) are used to protect species, communities, and their associated habitats, among other goals. Measuring MPA efficacy can be challenging, however, particularly when considering responses at the community level. We gathered 36 abundance and 14 biomass data sets on fish assemblages and used meta‐analysis to evaluate the ability of 22 distinct community diversity metrics to detect differences in community structure between MPAs and nearby control sites. We also considered the effects of 6 covariates—MPA size and age, MPA size and age interaction, latitude, total species richness, and level of protection—on each metric. Some common metrics, such as species richness and Shannon diversity, did not differ consistently between MPA and control sites, whereas other metrics, such as total abundance and biomass, were consistently different across studies. Metric responses derived from the biomass data sets were more consistent than those based on the abundance data sets, suggesting that community‐level biomass differs more predictably than abundance between MPA and control sites. Covariate analyses indicated that level of protection, latitude, MPA size, and the interaction between MPA size and age affect metric performance. These results highlight a handful of metrics, several of which are little known, that could be used to meet the increasing demand for community‐level indicators of MPA effectiveness.