Testing the Generality of Bird-Habitat Models

Bird-habitat models are frequently used as predictive modeling tools—for example, to predict how a species will respond to habitat modifications. We investigated the generality of the predictions from this type of model. Multivariate models were developed for Golden Eagle (Aquila chrysaetos), Raven (Corvus corax), and Buzzard (Buteo buteo) living in northwest Scotland. Data were obtained for all habitat and nest locations within an area of 2349 km². This assemblage of species is relatively static with respect to both occupancy and spatial positioning. The area was split into five geographic subregions: two on the mainland and three on the adjacent Island of Mull, which has one of United Kingdom's richest raptor fauna assemblages. Because data were collected for all nest locations and habitats, it was possible to build models that did not incorporate sampling error. A range of predictive models was developed using discriminant analysis and logistic regression. The models differed with respect to the geographical origin of the data used for model development. The predictive success of these models was then assessed by applying them to validation data. The models showed a wide range of predictive success, ranging from only 6% of nest sites correctly predicted to 100% correctly predicted. Model validation techniques were used to ensure that the models' predictions were not statistical artefacts. The variability in prediction success seemed to result from methodological and ecological processes, including the data recording scheme and interregional differences in nesting habitat. The results from this study suggest that conservation biologists must be very careful about making predictions from such studies because we may be working with systems that are inherently unpredictable.     

Predicting the Range of Chinese Mitten Crabs in Europe

Abstract:  Ecological niche modeling provides a means for predicting the potential future distribution of a nonindigenous species based on environmental characteristics of the species' native range. We applied this method to the Chinese mitten crab (Eriocheir sinensis) , a catadromous crustacean with a long history of invasion in Europe. We used genetic algorithm for rule-set prediction to predict the potential European distribution of mitten crab based on its distribution in 42 locations in its native Asia. The climatic variables, air temperature, number of days, amount of precipitation, and wetness index, contributed significantly to predictions of native distribution limits. Although the genetic algorithm for rule-set prediction model was developed for the native range, the species' extensive distribution in Europe ( n = 434) allowed independent validation of the predictions. Application of the model to Europe was successful, with 84% of occurrences in regions predicted to be suitable by >80% of the models and <4% of occurrences in areas predicted suitable by <50% of the models (mainly along the northern range). At the watershed scale, areas with established mitten crab populations had significantly higher habitat matching than sites that were not invaded. The independent validation of the Asian-based model by the European distribution revealed that predictions were highly accurate. The model also identified large areas of Europe, particularly along the Mediterranean coast, as vulnerable to future invasion. These predictions can be used to develop strategies to control the spread of mitten crab by preventing introductions into vulnerable areas.     

Can entropy maximization use functional traits to explain species abundances? A comprehensive evaluation

Entropy maximization (EM) is a method that can link functional traits and community composition by predicting relative abundances of each species in a community using limited trait information. We developed a complementary suite of tests to examine the strengths and limitations of EM and the community-aggregated traits (CATs; i.e., weighted averages) on which it depends that can be applied to virtually any plant community data set. We show that suites of CATs can be used to differentiate communities and that EM can address the classic problem of characterizing ecological niches by quantifying constraints (CATs) on complex trait relationships in local communities. EM outperformed null models and comparable regression models in communities with different levels of dominance, diversity, and trait similarity. EM predicted well the abundance of the dominant species that drive community-level traits; it typically identified rarer species as such, although it struggled to predict the abundances of the rarest species in some cases. Predictions were sensitive to choice of traits, were substantially improved by using informative priors based on null models, and were robust to variation in trait measurement due to intraspecific variability or measurement error. We demonstrate how similarity in species' traits confounds predictions and provide guidelines for applying EM.     

The Application of Neutral Landscape Models in Conservation Biology

Neutral landscape models, derived from percolation theory in the field of landscape ecology, are grid-based maps in which complex habitat distributions are generated by random or fractal algorithms. This grid-based representation of landscape structure is compatible with the raster-based format of geographical information systems (GIS), which facilitates comparisons between theoretical and real landscapes. Neutral landscape models permit the identification of critical thresholds in connectivity, which can be used to predict when landscapes will become fragmented. The coupling of neutral landscape models with generalized population models, such as metapopulation theory, provides a null model for generating predictions about population dynamics in fragmented landscapes. Neutral landscape models can contribute to the following applications in conservation: (1) incorporation of complex spatial patterns in (meta)population models; (2) identification of species' perceptions of landscape structure; (3) determination of landscape connectivity; (4) evaluation of the consequences of habitat fragmentation for population subdivision; (5) identification of the domain of metapopulation dynamics; (6) prediction of the occurrence of extinction thresholds; ( 7) determination of the genetic consequences of habitat fragmentation; and (8) reserve design and ecosystem management. This generalized, spatially explicit framework bridges the gap between spatially implicit, patch-based models and spatially realistic GIS applications which are usually parameterized for a single species in a specific landscape. Development of a generalized, spatially explicit framework is essential in conservation biology because we will not be able to develop individual models for every species of management concern.     

Integration of distance, direction and habitat into a predictive migratory movement model for blue-winged teal (Anas discors)

Historically, the migration of birds has been poorly understood in comparison to other life stages during the annual cycle. The goal of our research is to present a novel approach to predict the migratory movement of birds. Using a blue-winged teal case study, our process incorporates not only constraints on habitat (temperature, precipitation, elevation, and depth to water table), but also approximates the likely bearing and distance traveled from a starting location. The method allows for movement predictions to be made from unsampled areas across large spatial scales. We used USGS’ Bird Banding Laboratory database as the source of banding and recovery locations. We used recovery locations from banding sites with multiple within-30-day recoveries were used to build core maximum entropy models. Because the core models encompass information regarding likely habitat, distance, and bearing, we used core models to project (or forecast) probability of movement from starting locations that lacked sufficient data for independent predictions. The final model for an unsampled area was based on an inverse-distance weighted averaged prediction from the three nearest core models. To illustrate this approach, three unsampled locations were selected to probabilistically predict where migratory blue-wing teals would stopover. These locations, despite having little or none data, are assumed to have populations. For the blue-winged teal case study, 104 suitable locations were identified to generate core models. These locations ranged from 20 to 228 within-30-day recoveries, and all core models had AUC scores greater than 0.80. We can infer based on model performance assessment, that our novel approach to predicting migratory movement is well-grounded and provides a reasonable approximation of migratory movement.     

Predicting the distributions of marine organisms at the global scale

We present and evaluate AquaMaps, a presence-only species distribution modelling system that allows the incorporation of expert knowledge about habitat usage and was designed for maximum output of standardized species range maps at the global scale. In the marine environment there is a significant challenge to the production of range maps due to large biases in the amount and location of occurrence data for most species. AquaMaps is compared with traditional presence-only species distribution modelling methods to determine the quality of outputs under equivalently automated conditions. The effect of the inclusion of expert knowledge to AquaMaps is also investigated. Model outputs were tested internally, through data partitioning, and externally against independent survey data to determine the ability of models to predict presence versus absence. Models were also tested externally by assessing correlation with independent survey estimates of relative species abundance. AquaMaps outputs compare well to the existing methods tested, and inclusion of expert knowledge results in a general improvement in model outputs. The transparency, speed and adaptability of the AquaMaps system, as well as the existing online framework which allows expert review to compensate for sampling biases and thus improve model predictions are proposed as additional benefits for public and research use alike.     

Crustacean habitat potential mapping in a tidal flat using remote sensing and GIS

To make a macrofaunal (crustacean) habitat potential map, the spatial distribution of ecological variables in the Hwangdo tidal flat, Korea, was explored. Spatial variables were mapped using remote sensing and a geographic information system (GIS) combined with field observations. A frequency ratio (FR) and logistic regression (LR) model were employed to map the macrofauna potential area for the Ilyoplax dentimerosa, a crustacean species. Spatial variables affecting the tidal macrofauna distribution were selected based on abundance and biomass and used within a spatial database derived from remotely sensed data of various types of sensors. The spatial variables included the intertidal digital elevation model (DEM), slope, distance from a tidal channel, tidal channel density, surface sediment facies, spectral reflectance of the near infrared (NIR) bands and the tidal exposure duration. The relation between the I. dentimerosa and each spatial variable was calculated using the FR and LR. The species was randomly divided into a training set (70%) to analyse habitat potential using FR and LR and a test set (30%) to validate the predicted habitat potential map. The relations were overlaid to produce a habitat potential map with the species potential index (SPI) value for each pixel. The potential habitat maps were compared with the surveyed habitat locations such as validation data set. The comparison results showed that the LR model (accuracy is 85.28%) is better in prediction than the FR (accuracy is 78.96%) model. The performance of models gave satisfactory accuracies. The LR provides the quantitative influence of variables on a potential habitat of species; otherwise, the FR shows the quantitative influence of a class in each variable. The combination of a GIS-based frequency ratio and logistic regression models and remote sensing with field observations is an effective method to determine locations favorable for macrofaunal species occurrences in a tidal flat.     

Characterizing cumulative impacts using a brook trout population dynamics model

An individuals-based modelling framework is used to characterize the nature of exploitation and toxaphene stressors acting simultaneously on a population of brook trout (Salvelinus fontinalis) in terms of age 0 + and adult abundance, survivorship and population size-structure. A no-stressor control case was estimated against which exploitation-only, toxaphene-only and cumulative exploitation and toxaphene stressor cases were compared to determine the extent and significance of impacts. Single stressor case results were used to predict cumulative impacts by assuming additivity and predictions compared to modelled cumulative stressor results. Comparisons indicated the inadequacy of assuming additivity in predicting cumulative impacts. A factorial experimental design was used to estimate the size and significance of interactive effects. Effects are substantial and underscore the necessity of interpreting probable impacts of increases in a single stressor in conjunction with knowledge of other stressors acting on a population. A positive functional relationship between variability in population abundance and stress was estimated and is suggested as a potentially useful means of characterizing risks posed to populations by increases in, or additions to, population stressors. Multiple stressors were also demonstrated to effectively eliminate the significance of density-dependent mortality processes in determining age 0 + and adult abundance. Taken together, results indicate the inappropriateness of attempting to predict additional perturbation impacts without considering the sum of population stressors and their associated interactions.     

A Mechanistic Approach to Evaluation of Umbrella Species as Conservation Surrogates

Abstract:  Although species with large area requirements are sometimes used as umbrella species, their general utility as conservation tools is uncertain. We surveyed the species diversity of birds, butterflies, carabids, and forest-floor plants in forest sites across an area (1600 km²) in which we delineated large breeding home ranges of Northern Goshawk ( Accipiter gentilis ). We tested whether protection of the home ranges could serve as an effective umbrella to protect sympatric species of the four taxa. We also used an empirical habitat model of occupancy of home range to examine mechanisms by which the Northern Goshawk acts as an umbrella species. Among species richness, abundance, and species composition of the four taxa, only abundance and species composition of birds differed between sites located inside and outside home ranges, which was due to greater abundance of bird species that were prey of Northern Goshawks inside the home ranges. Thus, although home range indicated areas with high abundance of certain bird prey species, it was not effective as an indicator of the species diversity of all four taxa. We also did not find any difference in species richness, abundance, and species composition between sites predicted as occupied and unoccupied using the habitat model. In contrast, when we selected sites on the basis of each habitat variable in the model, habitat variables that selected sites either in agricultural or forested landscapes encompassed sites with high species richness or particular species composition. This result suggests that the low performance of the Northern Goshawk as an umbrella species is due to this species' preference for habitat in both agricultural and forested landscapes. Species that can adjust to changes in habitat conditions may not act as effective umbrella species despite having large home ranges.     

Drivers of euphausiid species abundance and numerical abundance in the Atlantic Ocean

Mid-ocean ridges are common features of the world’s oceans but there is a lack of understanding as to how their presence affects overlying pelagic biota. The Mid-Atlantic Ridge (MAR) is a dominant feature of the Atlantic Ocean. Here, we examined data on euphausiid distribution and abundance arising from several international research programmes and from the continuous plankton recorder. We used a generalized additive model (GAM) framework to explore spatial patterns of variability in euphausiid distribution on, and at either side of, the MAR from 60°N to 55°S in conjunction with variability in a suite of biological, physical and environmental parameters. Euphausiid species abundance peaked in mid-latitudes and was significantly higher on the ridge than in adjacent waters, but the ridge did not influence numerical abundance significantly. Sea surface temperature (SST) was the most important single factor influencing both euphausiid numerical abundance and species abundance. Increases in sea surface height variance, a proxy for mixing, increased the numerical abundance of euphausiids. GAM predictions of variability in species abundance as a function of SST and depth of the mixed layer were consistent with present theories, which suggest that pelagic niche availability is related to the thermal structure of the near surface water: more deeply-mixed water contained higher euphausiid biodiversity. In addition to exposing present distributional patterns, the GAM framework enables responses to potential future and past environmental variability including temperature change to be explored.     

The importance of spatial autocorrelation,extent and resolution in predicting forest bird occurrence

Concerns about declines in forest biodiversity underscore the need for accurate estimates of the distribution and abundance of organisms at large scales and at resolutions that are fine enough to be appropriate for management. This paper addresses three major objectives: (i) to determine whether the resolution of typical air photo-derived forest inventory is sufficient for the accurate prediction of site occupancy by forest birds. We compared prediction success of habitat models using air photo variables to models with variables derived from finer resolution, ground-sampled vegetation plots. (ii) To test whether incorporating spatial autocorrelation into habitat models via autologistic regression increases prediction success. (iii) To determine whether landscape structure is an important factor in predicting bird distribution in forest-dominated landscapes. Models were tested locally (Greater Fundy Ecosystem [GFE]) using cross-validation, and regionally using an independent data set from an area located ca. 250 km to the northwest (Riley Brook [RB]). We found significant positive spatial autocorrelation in the residuals of at least one habitat model for 76% (16/21) of species examined. In these cases, the logistic regression assumption of spatially independent errors was violated. Logistic models that ignored spatial autocorrelation tended to overestimate habitat effects. Though overall prediction success was higher for autologistic models than logistic models in the GFE, the difference was only significantly improved for one species. Further, the inclusion of spatial covariates did little to improve model performance in the geographically discrete study area. For 62% (13/21) of species examined, landscape variables were significant predictors of forest bird occurrence even after statistically controlling for stand-level variability. However, broad spatial extents explained less variation than local factors. In the GFE, 76% (16/21) of air photo and 81% (17/21) of ground plot models were accurate enough to be of practical utility (AUC > 0.7). When applied to RB, both model types performed effectively for 55% (11/20) of the species examined. We did not detect an overall difference in prediction success between air photo and ground plot models in either study area. We conclude that air photo data are as effective as fine resolution vegetation data for predicting site occupancy for the majority of species in this study. These models will be of use to forest managers who are interested in mapping species distributions under various timber harvest scenarios, and to protected areas planners attempting to optimize reserve function.     

Predicting tree distributions in an East African biodiversity hotspot: model selection, data bias and envelope uncertainty

The Eastern Arc Mountains (EAMs) of Tanzania and Kenya support some of the most ancient tropical rainforest on Earth. The forests are a global priority for biodiversity conservation and provide vital resources to the Tanzanian population. Here, we make a first attempt to predict the spatial distribution of 40 EAM tree species, using generalised additive models, plot data and environmental predictor maps at sub 1 km resolution. The results of three modelling experiments are presented, investigating predictions obtained by (1) two different procedures for the stepwise selection of predictors, (2) down-weighting absence data, and (3) incorporating an autocovariate term to describe fine-scale spatial aggregation. In response to recent concerns regarding the extrapolation of model predictions beyond the restricted environmental range of training data, we also demonstrate a novel graphical tool for quantifying envelope uncertainty in restricted range niche-based models (envelope uncertainty maps). We find that even for species with very few documented occurrences useful estimates of distribution can be achieved. Initiating selection with a null model is found to be useful for explanatory purposes, while beginning with a full predictor set can over-fit the data. We show that a simple multimodel average of these two best-model predictions yields a superior compromise between generality and precision (parsimony). Down-weighting absences shifts the balance of errors in favour of higher sensitivity, reducing the number of serious mistakes (i.e., falsely predicted absences); however, response functions are more complex, exacerbating uncertainty in larger models. Spatial autocovariates help describe fine-scale patterns of occurrence and significantly improve explained deviance, though if important environmental constraints are omitted then model stability and explanatory power can be compromised. We conclude that the best modelling practice is contingent both on the intentions of the analyst (explanation or prediction) and on the quality of distribution data; generalised additive models have potential to provide valuable information for conservation in the EAMs, but methods must be carefully considered, particularly if occurrence data are scarce. Full results and details of all species models are supplied in an online Appendix.     

Spatiotemporal coupling/decoupling of planktonic larvae and benthic settlement in decapods in the Scottish east coast

Settlement patterns and the relationship between meroplanktonic larvae and settlement in decapods were studied on the Scottish east coast. Artificial settlement substrates (ASS), deployed at two locations (sandy vs. rocky sea substrates), were employed to collect megalopae and newly settled juveniles. Abundance of meroplanktonic larvae was used as an indicator of larval supply. The results showed a clear seasonality in settlement rates, and in some cases, significant differences between sites were detected. Nevertheless, the interference of the ASS with the surrounding habitat limits the study of spatial variability in settlement rates. Significant cross-correlation was found between the abundance of megalopae and juveniles in the collectors and planktonic larval abundance a month earlier. For individual species, this relationship was observed only in Pisidia longicornis. Complexities caused by the great variety of pre- and post-settlement processes, alongside effects of secondary dispersals of early juveniles may have obscured the relationship between meroplanktonic larvae and juveniles in other species.     

In search of quorum effects in metacommunity structure: species co-occurrence analyses

Alternative models of community assembly emphasize regional, stochastic, dispersal-based processes or local, deterministic, niche-based processes. Community ecology's historical focus on local processes implicitly assumes that local processes surpass regional processes over time or across space to derive nonrandom metacommunity structure (i.e., a quorum effect). Quorum effects are expected late in succession among nearby sites, whereas quorum effects are not expected early in succession among distant sites. I conducted a meta-analysis of zooplankton data sets encompassing time scales of one to thousands of years and spatial scales of <1 m to thousands of kilometers. Species co-occurrence analyses statistically evaluated presence/absence patterns relative to random patterns obtained with Monte Carlo null models. A series of weighted analyses was conducted and alternative randomization algorithms and null models were evaluated. Most zooplankton metacommunities were randomly structured in unweighted analyses, and the distribution of significant structure did not follow quorum effect predictions. Weighted analyses (e.g., by habitat area) revealed significant, nonrandom structure in most zooplankton metacommunities, but the distribution of significant structure still did not adhere well to quorum effect predictions. Finally, additional weighting for study scale (number of sites) nullified most significant area-weighted structure, and again, the distribution of significant structure did not follow quorum effect predictions. Overall, a quorum effect was not supported, perhaps related to zooplankton life histories and energetics and/or the quorum effect itself. Results at the presence/absence level of resolution indicated that local processes did not generally override regional processes over time or across space to drive community structure. A full integration of dispersal- and niche-based concepts in metacommunity dynamics will be most fruitful for unraveling community assembly. Species co-occurrence analyses were scale dependent (habitat area and study size). Future analyses should use weights for important factors (e.g., habitat area), and meta-analyses should include study scale as an additional factor contributing to apparent patterns.     

Neighborhood and habitat effects on vital rates: expansion of the Barred Owl in the Oregon coast ranges

In this paper, we modify dynamic occupancy models developed for detection-nondetection data to allow for the dependence of local vital rates on neighborhood occupancy, where neighborhood is defined very flexibly. Such dependence of occupancy dynamics on the status of a relevant neighborhood is pervasive, yet frequently ignored. Our framework permits joint inference about the importance of neighborhood effects and habitat covariates in determining colonization and extinction rates. Our specific motivation is the recent expansion of the Barred Owl (Strix varia) in western Oregon, USA, over the period 1990-2010. Because the focal period was one of dramatic range expansion and local population increase, the use of models that incorporate regional occupancy (sources of colonists) as determinants of dynamic rate parameters is especially appropriate. We began our analysis of 21 years of Barred Owl presence/nondetection data in the Tyee Density Study Area (TDSA) by testing a suite of six models that varied only in the covariates included in the modeling of detection probability. We then tested whether models that used regional occupancy as a covariate for colonization and extinction outperformed models with constant or year-specific colonization or extinction rates. Finally we tested whether habitat covariates improved the AIC of our models, focusing on which habitat covariates performed best, and whether the signs of habitat effects are consistent with a priori hypotheses. We conclude that all covariates used to model detection probability lead to improved AIC, that regional occupancy influences colonization and extinction rates, and that habitat plays an important role in determining extinction and colonization rates. As occupancy increases from low levels toward equilibrium, colonization increases and extinction decreases, presumably because there are more and more dispersing juveniles. While both rates are affected, colonization increases more than extinction decreases. Colonization is higher and extinction is lower in survey polygons with more riparian forest. The effects of riparian forest on extinction rates are greater than on colonization rates. Model results have implications for management of the invading Barred Owl, both through habitat alteration and removal.     

Habitat Loss and Changes in the Species-Area Relationship

Abstract: The species-area relationship (SAR) has been used successfully to predict extinction from extent of habitat reduction. These extinction estimates assume that species have uniformly distributed range requirements and a minimum abundance level required for persistence; how many species are lost depends solely on how much habitat is removed, not on where it is removed. We consider another limiting case in which range requirements, rather than abundances, determine extinctions. We used a new method for constructing SARs based on assumptions about geographic ranges of species. Our results show that habitat destruction can change the SAR and consequently the number of species predicted to be lost due to habitat destruction. Our method generates SARs that vary in shape according to the specific distributions of geographic range and occupancy but that have the common feature of being described by a power law with an exponent of <1. When the geographic range of species was included in the SAR, the way habitat was lost became important. Although the SAR before habitat destruction is often used to predict species loss after habitat destruction, assumptions must be clearly stated. To predict the damage caused by habitat loss with our model, it is necessary to know the fraction of aggregated species, the distribution of geographic ranges, the form of habitat destruction, and the sampling protocol. The remaining theoretical challenge is to develop a full theory that links abundance and range.     

Effects of Natura 2000 on nontarget bird and butterfly species based on citizen science data

The European Union's Natura 2000 (N2000) is among the largest international networks of protected areas. One of its aims is to secure the status of a predetermined set of (targeted) bird and butterfly species. However, nontarget species may also benefit from N2000. We evaluated how the terrestrial component of this network affects the abundance of nontargeted, more common bird and butterfly species based on data from long-term volunteer-based monitoring programs in 9602 sites for birds and 2001 sites for butterflies. In almost half of the 155 bird species assessed, and particularly among woodland specialists, abundance increased (slope estimates ranged from 0.101 [SD 0.042] to 3.51 [SD 1.30]) as the proportion of landscape covered by N2000 sites increased. This positive relationship existed for 27 of the 104 butterfly species (estimates ranged from 0.382 [SD 0.163] to 4.28 [SD 0.768]), although most butterflies were generalists. For most species, when land-cover covariates were accounted for these positive relationships were not evident, meaning land cover may be a determinant of positive effects of the N2000 network. The increase in abundance as N2000 coverage increased correlated with the specialization index for birds, but not for butterflies. Although the N2000 network supports high abundance of a large spectrum of species, the low number of specialist butterflies with a positive association with the N2000 network shows the need to improve the habitat quality of N2000 sites that could harbor open-land butterfly specialists. For a better understanding of the processes involved, we advocate for standardized collection of data at N2000 sites.     

A Matrix‐Calibrated Species‐Area Model for Predicting Biodiversity Losses Due to Land‐Use Change

Abstract: Application of island biogeography theory to prediction of species extinctions resulting from habitat loss is based on the assumption that the transformed landscape matrix is completely inhospitable to the taxa considered, despite evidence demonstrating the nontrivial influence of matrix on populations within habitat remnants. The island biogeography paradigm therefore needs refining to account for specific responses of taxa to the area of habitat “islands” and to the quality of the surrounding matrix. We incorporated matrix effects into island theory by partitioning the slope (z value) of species–area relationships into two components: γ, a constant, and σ, a measure of taxon‐specific responses to each component of a heterogeneous matrix. We used our matrix‐calibrated model to predict extinction and endangerment of bird species resulting from land‐use change in 20 biodiversity hotspots and compared these predictions with observed numbers of extinct and threatened bird species. We repeated this analysis with the conventional species–area model and the countryside species–area model, considering alternative z values of 0.35 (island) or 0.22 (continental). We evaluated the relative strength of support for each of the five candidate models with Akaike's information criterion (AIC). The matrix‐calibrated model had the highest AIC weight (w_i = 89.21%), which means the weight of evidence in support of this model was the optimal model given the set of candidate models and the data. In addition to being a valuable heuristic tool for assessing extinction risk, our matrix‐calibrated model also allows quantitative assessment of biodiversity benefits (and trade‐offs) of land‐management options in human‐dominated landscapes. Given that processes of secondary regeneration have become more widespread across tropical regions and are predicted to increase, our matrix‐calibrated model will be increasingly appropriate for practical conservation in tropical landscapes.     

Quantifying effects of pollution on biodiversity: a case study of highly diverse molluscan assemblages in the Mediterranean

Structured sampling designs are important in the assessment of environmental impacts of variable ecological systems. Recent developments have provided a useful framework extending existing univariate techniques into a multivariate context. Measures of taxonomic relatedness have also been introduced, which complement existing measures of diversity of assemblages. In this study, the potential effects of sewage discharge on spatial patterns of highly diverse molluscan assemblages in a Mediterranean rocky subtidal habitat were investigated. Nine 20 cm×20 cm quadrats were taken from each of three sites (80 m–100 m apart) within a putatively impacted location near a sewage outfall (I) and at each of two control locations (Cs) by destructive sampling by SCUBA divers at a depth of 3 m–4 m. A total of 5507 specimens of 151 species were collected. The average and the variance in total abundance of molluscs were greater, on average, at I than at Cs. Higher abundances at the sewage outfall were largely driven by greater numbers of juvenile molluscs. The Shannon diversity of molluscs (H′) was significantly lower at I, but no difference among locations was detected for the total number of species (S). In addition, the taxonomic distinctness (Δ*) of molluscs was greater at Cs, although it was more variable at I. Multivariate analyses showed that there was a significant difference in the structure of assemblages at I compared with Cs. The location near the outfall was characterized by greater abundances of several species, including especially the gastropods Pusillina philippi, Bittium latreilli, and Bittium reticulatum. There was also greater variability in the structure of assemblages among sites and among quadrats at control locations compared to those near the outfall. Using a suite of univariate and multivariate measures, including diversity indices, detailed information on taxonomic structure and analyses of variability at different spatial scales, provided useful insights into the effects of sewage impacts on these diverse assemblages. These results also highlighted the importance of analysing measures of variance, as well as mean in considering effects of stress in natural communities.