Instream and offstream environmental conditions and stream biotic integrity: Importance of scale and site similarities for learning and prediction

Two different methods to predict biotic integrity were tested and compared in the present paper. The first one tries to predict the fish indices of biotic integrity (IBI) at the state or regional scale based on the most similar observations to a specific target site of interest using the simple to implement k-nearest neighbors (or kNN) method. Two different distance functions were considered to find the k-nearest neighbors: the Euclidean and the Mahalanobis. The second method was applied on the same datasets and consisted of a step-wise multiple regression. The two modeling approaches yielded similar results but kNN proved to be more time-efficient and very fast computationally for the given dataset sizes, which allowed applying a leave-one-out cross validation.In an attempt to reveal the importance of scale in the prediction of IBI, regression models were constructed at the state (or regional) scale and at the more refined cluster of sampling sites scale. Clusters of sites were extracted using Kohonen's self-organizing maps (SOM) followed by k-means clustering of the SOM neurons. Cluster-level regression models, constructed after site patterning, performed better in IBI prediction than global regression models constructed without any previous site patterning. The importance of identifying groups of sites with similar environmental characteristics affecting the IBI was revealed. The combined use of site patterning and regression modeling for IBI prediction also helped identifying important variables acting at the local scale which remain latent at the global scale.     

A simulation model for Japanese sardine (Sardinops melanostictus) migrations in the western North Pacific

A two-dimensional individual-based model coupled with fish bioenergetics was developed to simulate migration and growth of Japanese sardine (Sardinops melanostictus) in the western North Pacific. In the model, fish movement is controlled by feeding and spawning migrations with passive transport by simulated ocean current. Feeding migration was assumed to be governed by search for local optimal habitats, which is estimated by the spatial distribution of net growth rate of a sardine bioenergetics model. The forage density is one of the most important factors which determines the geographical distributions of Japanese sardine during their feeding migrations. Spawning migration was modeled by an artificial neural network (ANN) with an input layer composed of five neurons that receive environmental information (surface temperature, temperature change experienced, current speed, day length and distance from land). Once the weight of the ANN was determined, the fish movement was solved by combining with the feeding migration model. To obtain the weights of the ANN, three experiments were conducted in which (1) the ANN was trained with back propagation (BP) method with optimum training data, (2) genetic algorithm (GA) was used to adjust the weights and (3) the weights of the ANN were decided by the GA with BP, respectively. BP is a supervised learning technique for training ANNs. GA is a search technique used in computing to find approximate solutions, such as optimization of parameters. Condition factor of sardine in the model is used as a factor of optimization in the GA works. The methods using only BP or GA did not work to search the appropriate weights in the ANN for spawning migration. In the third method, which is a combined approach of GA with BP, the model reproduced the most realistic spawning migration of Japanese sardine. The changes in temperature and day length are important factors for the orientation cues of Japanese sardine according to the sensitivity analysis of the weights of the ANN.     

Effects of site-selection bias on estimates of biodiversity change

Estimates of biodiversity change are essential for the management and conservation of ecosystems. Accurate estimates rely on selecting representative sites, but monitoring often focuses on sites of special interest. How such site-selection biases influence estimates of biodiversity change is largely unknown. Site-selection bias potentially occurs across four major sources of biodiversity data, decreasing in likelihood from citizen science, museums, national park monitoring, and academic research. We defined site-selection bias as a preference for sites that are either densely populated (i.e., abundance bias) or species rich (i.e., richness bias). We simulated biodiversity change in a virtual landscape and tracked the observed biodiversity at a sampled site. The site was selected either randomly or with a site-selection bias. We used a simple spatially resolved, individual-based model to predict the movement or dispersal of individuals in and out of the chosen sampling site. Site-selection bias exaggerated estimates of biodiversity loss in sites selected with a bias by on average 300–400% compared with randomly selected sites. Based on our simulations, site-selection bias resulted in positive trends being estimated as negative trends: richness increase was estimated as 0.1 in randomly selected sites, whereas sites selected with a bias showed a richness change of −0.1 to −0.2 on average. Thus, site-selection bias may falsely indicate decreases in biodiversity. We varied sampling design and characteristics of the species and found that site-selection biases were strongest in short time series, for small grains, organisms with low dispersal ability, large regional species pools, and strong spatial aggregation. Based on these findings, to minimize site-selection bias, we recommend use of systematic site-selection schemes; maximizing sampling area; calculating biodiversity measures cumulatively across plots; and use of biodiversity measures that are less sensitive to rare species, such as the effective number of species. Awareness of the potential impact of site-selection bias is needed for biodiversity monitoring, the design of new studies on biodiversity change, and the interpretation of existing data.     

Comparing discriminant analysis, neural networks and logistic regression for predicting species distributions: a case study with a Himalayan river bird

We assessed the occurrence of a common river bird, the Plumbeous Redstart Rhyacornis fuliginosus, along 180 independent streams in the Indian and Nepali Himalaya. We then compared the performance of multiple discrimant analysis (MDA), logistic regression (LR) and artificial neural networks (ANN) in predicting this species’ presence or absence from 32 variables describing stream altitude, slope, habitat structure, chemistry and invertebrate abundance. Using the entire data (=training set) and a threshold for accepting presence in ANN and LR set to P≥0.5, ANN correctly classified marginally more cases (88%) than either LR (83%) or MDA (84%). Model performance was assessed from two methods of data partitioning. In a ‘leave-one-out’ approach, LR correctly predicted more cases (82%) than MDA (73%) or ANN (69%). However, in a holdout procedure, all the methods performed similarly (73–75%). All methods predicted true absence (i.e. specificity in holdout: 81–85%) better than true presence (i.e. sensitivity: 57–60%). These effects reflect species’ prevalence (=frequency of occurrence), but are seldom considered in distribution modelling. Despite occurring at only 36% of the sites, Plumbeous Redstarts are one of the most common Himalayan river birds, and problems will be greater with less common species. Both LR and ANN require an arbitrary threshold probability (often P=0.5) at which to accept species presence from model prediction. Simulations involving varied prevalence revealed that LR was particularly sensitive to threshold effects. ROC plots (received operating characteristic) were therefore used to compare model performance on test data at a range of thresholds; LR always outperformed ANN. This case study supports the need to test species’ distribution models with independent data, and to use a range of criteria in assessing model performance. ANN do not yet have major advantages over conventional multivariate methods for assessing bird distributions. LR and MDA were both more efficient in the use of computer time than ANN, and also more straightforward in providing testable hypotheses about environmental effects on occurrence. However, LR was apparently subject to chance significant effects from explanatory variables, emphasising the well-known risks of models based purely on correlative data.     

Neural network modelling of coastal algal blooms

An artificial neural network (ANN), a data driven modelling approach, is proposed to predict the algal bloom dynamics of the coastal waters of Hong Kong. The commonly used back-propagation learning algorithm is employed for training the ANN. The modeling is based on (a) comprehensive biweekly water quality data at Tolo Harbour (1982–2000); and (b) 4-year set of weekly phytoplankton abundance data at Lamma Island (1996–2000). Algal biomass is represented as chlorophyll-a and cell concentration of Skeletonema at the two locations, respectively. Analysis of a large number of scenarios shows that the best agreement with observations is obtained by using merely the time-lagged algal dynamics as the network input. In contrast to previous findings with more complicated neural networks of algal blooms in freshwater systems, the present work suggests the algal concentration in the eutrophic sub-tropical coastal water is mainly dependent on the antecedent algal concentrations in the previous 1–2 weeks. This finding is also supported by an interpretation of the neural networks’ weights. Through a systematic analysis of network performance, it is shown that previous reports of predictability of algal dynamics by ANN are erroneous in that ‘future data’ have been used to drive the network prediction. In addition, a novel real time forecast of coastal algal blooms based on weekly data at Lamma is presented. Our study shows that an ANN model with a small number of input variables is able to capture trends of algal dynamics, but data with a minimum sampling interval of 1 week is necessary. However, the sufficiency of the weekly sampling for real time predictions using ANN models needs to be further evaluated against longer weekly data sets as they become available.     

Limitations of Biodiversity Databases: Case Study on Seed-Plant Diversity in Tenerife, Canary Islands

Abstract:  Databases on the distribution of species can be used to describe the geographic patterns of biodiversity. Nevertheless, they have limitations. We studied three of these limitations: (1) inadequacy of raw data to describe richness patterns due to sampling bias, (2) lack of survey effort assessment (and lack of exhaustiveness in compiling data about survey effort), and (3) lack of coverage of the geographic and environmental variations that affect the distribution of organisms. We used a biodiversity database (BIOTA-Canarias) to analyze richness data from a well-known group (seed plants) in an intensively surveyed area (Tenerife Island). Observed richness and survey effort were highly correlated. Species accumulation curves could not be used to determine survey effort because data digitalization was not exhaustive, so we identified well-sampled sites based on observed richness to sampling effort ratios. We also developed a predictive model based on the data from well-sampled sites and analyzed the origin of the geographic errors in the obtained extrapolation by means of a geographically constrained cross-validation. The spatial patterns of seed-plant species richness obtained from BIOTA-Canarias data were incomplete and biased. Therefore, some improvements are needed to use this database (and many others) in biodiversity studies. We propose a protocol that includes controls on data quality, improvements on data digitalization and survey design to improve data quality, and some alternative data analysis strategies that will provide a reliable picture of biodiversity patterns.     

Use of Coarse‐Resolution Models of Species’ Distributions to Guide Local Conservation Inferences

Abstract: Distribution models are used increasingly for species conservation assessments over extensive areas, but the spatial resolution of the modeled data and, consequently, of the predictions generated directly from these models are usually too coarse for local conservation applications. Comprehensive distribution data at finer spatial resolution, however, require a level of sampling that is impractical for most species and regions. Models can be downscaled to predict distribution at finer resolutions, but this increases uncertainty because the predictive ability of models is not necessarily consistent beyond their original scale. We analyzed the performance of downscaled, previously published models of environmental favorability (a generalized linear modeling technique) for a restricted endemic insectivore, the Iberian desman (Galemys pyrenaicus), and a more widespread carnivore, the Eurasian otter (Lutra lutra), in the Iberian Peninsula. The models, built from presence–absence data at 10 × 10 km resolution, were extrapolated to a resolution 100 times finer (1 × 1 km). We compared downscaled predictions of environmental quality for the two species with published data on local observations and on important conservation sites proposed by experts. Predictions were significantly related to observed presence or absence of species and to expert selection of sampling sites and important conservation sites. Our results suggest the potential usefulness of downscaled projections of environmental quality as a proxy for expensive and time‐consuming field studies when the field studies are not feasible. This method may be valid for other similar species if coarse‐resolution distribution data are available to define high‐quality areas at a scale that is practical for the application of concrete conservation measures.     

Study on the utility of IRS 1D LISS-III data and the classification techniques for mapping of Sunderban mangroves

Mangrove conservation and management is a stupendous task chiefly due to the inaccessibility and the hostile substrate conditions. Remote sensing technology serves as an important tool in providing fast, accurate and up-to-date baseline information on the status of mangroves. It is almost impossible to carry out conventional field surveys in these swampy areas. The present study aims at the classification and mapping of the mangroves in Sunderban Biosphere Reserve (SBR) in the West Bengal province of India using IRS 1D LISS-III satellite data. Different classification approaches, viz., on-screen visual interpretation, supervised and unsupervised classifications were tried. The study showed that four mangroves classes, viz., Avicennia, Phoenix, mixed mangroves, and mangrove scrub and eight non-mangrove classes could be delineated using all the three approaches. All the mangrove and non-mangrove classes were field verified and the overall accuracy as well as user’s and producer’s accuracies for each category were determined. It was observed that among the three approaches, on-screen visual interpretation yielded higher classification accuracy (91.67%) compared to supervised (79.90%) and unsupervised classifications (71.08%). The results obtained through on-screen visual interpretation showed that all mangrove categories together cover 23.21% of the total geographical area of SBR, of which the mixed mangrove category covers maximum area (18.31%). Among the non-mangrove classes, the waterbody occupies largest area (35.36%) followed by agriculture (34.51%).     

Relationships between Plant and Animal Species Richness at a Regional Scale in China

Abstract:  Important questions in conservation biology and ecology include whether species diversities of different groups of organisms are correlated and, in particular, whether plant diversity influences animal diversity. I used correlation and partial regression analyses to examine the relationships between species richness of vascular plants and four major groups of terrestrial vertebrates (mammals, amphibians, reptiles, and birds) in 28 provinces in China. Species richness data were obtained from the literature. Environmental variables included normalized difference vegetation index, mean January temperature, mean annual temperature, annual precipitation, May through August precipitation, actual evapotranspiration, potential evapotranspiration, and elevation range. Species richness was strongly and positively correlated among the five groups of organisms. Plant richness was correlated with animal richness more strongly than the richness of different animal groups correlated with each other except for reptile richness, which had a slightly higher correlation with amphibian richness than with plant richness. Plant richness uniquely explained 41 times more variance in the species richness of the four vertebrate groups combined than environmental variables uniquely did, suggesting that plant richness influences terrestrial vertebrate richness at the regional scale examined. Because of strong correlations between the diversity of vascular plants and vertebrates, the diversity of vascular plants may be used as a surrogate for the diversity of terrestrial animals in China. My results have implications for selection of areas to be protected at both regional and local scales.     

Characterization of biological responses under different environmental conditions: A hierarchical modeling approach

A natural river system is organized as a nested hierarchy of interconnected habitats with specific environmental conditions to which the biological community has adapted. Due to this hierarchical structure, identifying the role of different stressors on the biological community is a formidable task. Efforts trying to link stressors to biological integrity have always been bound to the geographic scale of the selected study area, leading to scale-specific results. In this research, an attempt is made to lift this limitation and develop a hierarchical, scale-sensitive methodology that can identify the significant environmental stressors to the biological community at different scales. Sites with similar background environmental conditions are clustered using self-organizing maps (SOM). This is used to identify stressors which affect the biological community throughout the area of study - called environmental gradients or large-scale stressors. Subsequently, these clusters of similar observations (sampling sites) are progressively sub-divided using environmental variables with a significant but localized effect on the biological community - called small-scale stressors. A parent group of sites is split only when the resulting sub-groups have significantly different biological responses. At the end of this recursive sites decomposition procedure, the original set of observations is organized as a tree of environmentally homogeneous groups of observations characterized by unique biological responses to multiple stressors with different geographic extents. The developed hierarchical analysis methodology has been validated using a large-size dataset of environmental observations from the State of Ohio. Our results show that habitat degradation and increased nutrient loading are the large-scale stressors with a widespread impact in Ohio. Other stressors, such as heavy metals, pH or nitrate concentrations have significant albeit localized effects on biological integrity.     

Estimation of stream temperature in firtina creek (Rize-Turkiye) using artificial neural network model

Water temperature is one of the most important environmental variables in aquatic ecosystem. Temperature changes may have positive or negative effects on organisms. High water temperatures have caused mortalities in salmonid fishes. Therefore, monitoring and prediction of potential adverse changes in water temperature is very important. Here, we have developed and tested an artificial neural network (ANN) model to predict stream temperature of Firtina Creekin Black Sea region; using local water temperature, dissolved oxygen, pH and other available meteorological data (air temperature, rainfall). Thus, enabling define suitable habitat for native Sea Trout (Salmo trutta labrax, Pallas 1811) under past drought or other adverse envIronmental conditions.     

Predicting Richness of Native, Rare, and Exotic Plants in Response to Habitat and Disturbance Variables across a Variegated Landscape

Species richness of native, rare native, and exotic understorey plants was recorded at 120 sites in temperate grassy vegetation in New South Wales. Linear models were used to predict the effects of environment and disturbance on the richness of each of these groups. Total native species and rare native species showed similar responses, with richness declining on sites of increasing natural fertility of parent material as well as declining under conditions of water enrichment (resulting from human-induced changes in drainage characteristics, leading to increased run-off), severe livestock grazing, and soil disturbance. The response of rare native species to water enrichment, however, was significantly greater than that of all native species. Exotic species richness varied in reverse to that of native species with positive responses to water enrichment and soil disturbance. The contrasting behaviors are attributed to differences in the evolutionary history of native and exotic assemblages and their resulting preadaptations to a landscape recently subjected to agricultural settlement. It would appear that for exogenous disturbances, the intermediate disturbance hypothesis is not supported by our data. In the sampled region, pastures represent the major land-use in terms of area, but have relatively low densities of native and rare species compared with more lightly grazed areas. However, their management is considered to be essential to the maintenance of diversity on a regional scale.     

RIVPACS models for predicting the expected macroinvertebrate fauna and assessing the ecological quality of rivers

The European Union Water Framework Directive recognises the need for and value of biological monitoring. This paper reviews the modelling approach known as River Invertebrate Prediction and Classification System (RIVPACS for assessing the ecological quality of river sites using macroinvertebrate sampling. The RIVPACS philosophy is to develop statistical relationships between the fauna and the environmental characteristics of a large set of high quality reference sites which can be used to predict the macroinvertebrate fauna to be expected at any site in the absence of pollution or other environmental stress. The observed fauna at new test sites can then be compared with their site-specific expected fauna to derive indices of ecological quality. All methodological decisions in any such model development have implications for the reliability, precision and robustness of any resulting indices for assessing the ecological quality and ecological grade (‘status’) of individual river stretches. The choice of reference sites and environmental predictor variables, the site classification and discrimination methods, the estimation of the expected fauna, and indices for comparing the agreement, or lack of it, between the observed and expected fauna, are all discussed. The indices are assessed on the reference sites and on a separate test set of 340 sites, which are subject to a wide range of types and degrees of impairment.     

Surrogates for Macrofungi and Mosses in Reservation Planning

Abstract: Our knowledge of cryptogam taxonomy and species distributions is currently too poor to directly plan for their conservation. We used inventory data from four distinct vegetation types, near Hobart Tasmania, to address the proposition that vegetation type, vascular plant taxon composition, and environmental variables can act as surrogates for mosses and macrofungi in reservation planning. The four vegetation types proved distinct in their taxon composition for all macrofungi, mosses, and vascular plants. We tested the strength of the relationships between the composition of cryptogam taxonomic groups and vascular plant composition and between the environmental variables and canopy cover. Taxon composition of woody vascular plants and vascular plants was the best predictor of the taxon composition of mosses and macrofungi. Combinations of environmental variables and canopy cover were also strong predictors of the taxon composition of mosses and macrofungi. We used an optimization routine for vascular plant taxa and woody plant species and determined the representation of cryptogam taxa in these selections. We identified sites with approximately 10% and 30% of the greatest proportions of vascular plants and woody vascular plants and calculated representation of mosses and macrofungi at these sites. We compared the results of these site selections with random site selections and random selections stratified by vegetation type. Random selection of sites by vegetation type generally captured more cryptogams than site selection by vascular plants at the 10% level. Vascular plant and woody plant taxon composition, vegetation type, and environmental and structural characteristics, all showed promise as surrogates for capturing common cryptogams in reserve systems.     

Artificial neural network modeling of the river water quality—A case study

The paper describes the training, validation and application of artificial neural network (ANN) models for computing the dissolved oxygen (DO) and biochemical oxygen demand (BOD) levels in the Gomti river (India). Two ANN models were identified, validated and tested for the computation of DO and BOD concentrations in the Gomti river water. Both the models employed eleven input water quality variables measured in river water over a period of 10 years each month at eight different sites. The performance of the ANN models was assessed through the coefficient of determination (R²) (square of the correlation coefficient), root mean square error (RMSE) and bias computed from the measured and model computed values of the dependent variables. Goodness of the model fit to the data was also evaluated through the relationship between the residuals and model computed values of DO and BOD. The model computed values of DO and BOD by both the ANN models were in close agreement with their respective measured values in the river water. Relative importance and contribution of the input variables to the model output was evaluated through the partitioning approach. The identified ANN models can be used as tools for the computation of water quality parameters.     

Supralittoral amphipod abundances across habitats on Mediterranean temperate beaches

Community complexity and abiotic conditions are key components of environmental heterogeneity that affect the abundance and distribution of species. In this study we evaluated how environmental conditions affect abundances of supralittoral amphipods (Talitridae) in four habitats (sandy beach, rivermouth, wrack and lakeshore), along the Italian peninsula in the Mediterranean Sea. All samplings covered a 12 month period, and used the same sampling methodology thereby enabling comparison of abundances and species composition and richness. Four species (Talitrus saltator (Montagu, 1808), Orchestia gammarellus (Pallas, 1766), O. montagui Audouin 1826, O. cf. cavimana Heller 1865) were collected in the different habitats, but most species were found or were abundant in only one of the four habitats. Abundances of talitrids (numbers per sampling hour) differed significantly among the habitats with highest abundances found in the wrack and on the riverbank in proximity to an estuary, and lowest abundances observed on four sandy beach sites. Environmental conditions (temperature, moisture, substrate penetrability) differed among the habitats and were associated with some of the among-site variability in abundances. Our findings demonstrate that talitrids thrive better in some supralittoral habitats than others, and that some habitats could be considered to be “hotspots” of talitrid ecology and biodiversity.     

The Effectiveness of Surrogate Taxa for the Representation of Biodiversity

Abstract: Biodiversity is too complex to measure directly, so conservation planning must rely on surrogates to estimate the biodiversity of sites. The species richness of selected taxa is often used as a surrogate for the richness of other taxa. Surrogacy values of taxa have been evaluated in diverse contexts, yet broad trends in their effectiveness remain unclear. We reviewed published studies testing the ability of species richness of surrogate taxa to capture the richness of other (target) taxa. We stratified studies into two groups based on whether a complementarity approach (surrogates used to select a combination of sites that together maximize total species richness for the taxon) or a richness‐hotspot approach (surrogates used to select sites containing the highest species richness for the taxon) was used. For each comparison of one surrogate taxon with one target, we used the following predictor variables: biome, spatial extent of study area, surrogate taxon, and target taxon. We developed a binary response variable based on whether the surrogate taxon provided better than random representation of the target taxon. For studies that used an evaluation approach that was not based on better than random representation of target taxa, we based the response variable on the interpretation of results in the original study. We performed a categorical regression to elucidate trends in the effectiveness of surrogate taxa with regard to each of the predictor variables. A surrogate was 25% more likely to be effective with a complementarity approach than with a hotspot approach. For hotspot‐based approaches, biome, extent of study, surrogate taxon, and target taxon significantly influenced effectiveness of the surrogate. For complementarity‐based approaches, biome, extent, and surrogate taxon significantly influenced effectiveness of the surrogate. For all surrogate evaluations, biome explained the greatest amount of variation in surrogate effectiveness. From most to least, extent, surrogate taxon, and target taxon explained the most variation after biome. Surrogate taxa were most effective in grasslands and in some cases boreal zones, deserts, and tropical forests; surrogate taxa also were more effective in studies examining larger areas. Herpetofauna were the most effective taxon as both surrogate and target when a richness‐hotspot approach was used; however, herpetofauna were analyzed in fewer studies, so this result is tentative. For complementarity approaches, taxa that are easy to measure and tend to have a large number of habitat specialists distributed collectively across broad environmental gradients (e.g., plants, birds, and mammals) were the most effective surrogates.     

Limited salvage logging effects on forest regeneration after moderate-severity windthrow.

Recent conceptual advances address forest response to multiple disturbances within a brief time period, providing an ideal framework for examining the consequences of natural disturbances followed by anthropogenic management activities. The combination of two or more disturbances in a short period may produce "ecological surprises," and models predict a threshold of cumulative disturbance severity above which forest composition will be drastically altered and regeneration may be impaired. Salvage logging (the harvesting of timber after natural disturbances; also called "salvaging" or "sanitary logging") is common, but there have been no tests of the manner in which salvaging after natural wind disturbance affects woody plant regeneration. Here we present findings from three years after a moderate-severity wind disturbance in west-central Tennessee, USA. We compare two unsalvaged sites and two sites that had intermediate-intensity salvaging. Our approach demonstrates the calculation of cumulative severity measures, which combine natural windthrow severity and anthropogenic tree cutting and removal, on a plot-by-plot basis. Seedling/sapling density and species richness were not influenced by cumulative disturbance severity, but species diversity showed a marginal increase with increasing cumulative severity. The amount of compositional change (from predisturbance trees to post-disturbance seedlings/saplings) increased significantly with cumulative severity of disturbance but showed no evidence of thresholds within the severity range examined. Overall, few deleterious changes were evident in these sites. Moderate-severity natural disturbances followed by moderate-intensity salvaging may have little detrimental effect on forest regeneration and diversity in these systems; the ecological surprises and threshold compositional change are more likely after combinations of natural and anthropogenic disturbances that have a much greater cumulative severity.     

Spider, bee, and bird communities in cities are shaped by environmental control and high stochasticity

Spatially organized distribution patterns of species and communities are shaped by both autogenic processes (neutral mechanism theory) and exogenous processes (niche theory). In the latter, environmental variables that are themselves spatially organized induce spatial structure in the response variables. The relative importance of these processes has not yet been investigated in urban habitats. We compared the variance explained by purely spatial, spatially structured environmental, and purely environmental components for the community composition of spiders (Araneae), bees (Apidae), and birds (Aves) at 96 locations in three Swiss cities. Environmental variables (topography, climate, land cover, urban green management) were measured on four different radii around sampling points (< 10 m, 50 m, 250 m, 1000 m), while Moran's eigenvector maps (MEMs) acted as spatial variables. All three taxonomic groups showed weak spatial structure. Spider communities reacted to very fine-scaled environmental changes of lawn and meadow management and climate. Bird community composition was determined by woody plants as well as solar radiation at all radii, the scale of the influence varying among species. Bee communities were weakly explained by isolated variables only. Our results suggest that the anthropogenic structuring of urban areas has disrupted the spatial organization of environmental variables and inhibited the development of biotic spatial processes. The near absence of spatial structure may therefore be a feature typical of urban species assemblages, resulting in urban community composition mainly influenced by local environmental variables. Urban environments represent a close-knit mosaic of habitats that are regularly disturbed. Species communities in urban areas are far from equilibrium. Our analysis also suggests that urban communities need to be considered as being in constant change to adapt to disturbances and changes imposed by human activities.     

Evaluating the Quality of Citizen‐Scientist Data on Pollinator Communities

Abstract: Concerns about pollinator declines have grown in recent years, yet the ability to detect changes in abundance, taxonomic richness, and composition of pollinator communities is hampered severely by the lack of data over space and time. Citizen scientists may be able to extend the spatial and temporal extent of pollinator monitoring programs. We developed a citizen‐science monitoring protocol in which we trained 13 citizen scientists to observe and classify floral visitors at the resolution of orders or super families (e.g., bee, wasp, fly) and at finer resolution within bees (superfamily Apoidea) only. We evaluated the protocol by comparing data collected simultaneously at 17 sites by citizen scientists (observational data set) and by professionals (specimen‐based data set). The sites differed with respect to the presence and age of hedgerows planted to improve habitat quality for pollinators. We found significant, positive correlations among the two data sets for higher level taxonomic composition, honey bee (Apis mellifera) abundance, non‐Apis bee abundance, bee richness, and bee community similarity. Results for both data sets also showed similar trends (or lack thereof) in these metrics among sites differing in the presence and age of hedgerows. Nevertheless, citizen scientists did not observe approximately half of the bee groups collected by professional scientists at the same sites. Thus, the utility of citizen‐science observational data may be restricted to detection of community‐level changes in abundance, richness, or similarity over space and time, and citizen‐science observations may not reliably reflect the abundance or frequency of occurrence of specific pollinator species or groups.