Modelling roach (Rutilus rutilus) microhabitat using linear and nonlinear techniques

• 1 Multiple linear regression (MLR), generalised additive models (GAM) and artificial neural networks (ANN), were used to define young of the year (0+) roach (Rutilus rutilus) microhabitat and to predict its abundance.
• 2 0+ Roach and nine environmental variables were sampled using point abundance sampling by electrofishing in the littoral area of Lake Pareloup (France) during summer 1997. Eight of these variables were used to set up the models after log₁₀ (x+ 1) transformation of the dependent variable (0+ roach density). Model training and testing were performed on independent subsets of the whole data matrix containing 306 records.
• 3 The predictive quality of the models was estimated using the determination coefficient between observed and estimated values of roach densities. The best models were provided by ANN, with a correlation coefficient (r) of 0.83 in the training procedure and 0.62 in the testing procedure. GAM and MLR gave lower prediction in the training set (r = 0.53 for GAM and r = 0.32 for MLR) and in the testing set (r = 0.48 for GAM and r = 0.43 for MLR). In the same way, samples without fish were reliably predicted by ANN whereas GAM and MLR predicted absence unreliably.
• 4 ANN sensitivity analysis of the eight environmental variables in the models revealed that 0+ roach distribution was mainly influenced by five variables: depth, distance from the bank, local slope of the bottom and percentage of mud and flooded vegetation cover. The nonlinear influence of these variables on 0+ roach distribution was clearly shown using nonparametric lowess smoothing procedures.
• 5 Non‐linear modelling methods, such as GAM and ANN, were able to define 0+ fish microhabitat precisely and to provide insight into 0+ roach distribution and abundance in the littoral zone of a large reservoir. The results showed that in lakes, 0+ roach microhabitat is influenced by a complex combination of several environmental variables acting mainly in a nonlinear way.

     

The prediction of macrophyte species occurrence in Swiss ponds

The study attempted to model the abundance of aquatic plant species recorded in a range of ponds in Switzerland. A stratified sample of 80 ponds, distributed all over the country, provided input data for model development. Of the 154 species recorded, 45 were selected for modelling. A total of 14 environmental parameters were preselected as candidate explanatory variables. Two types of statistical tools were used to explore the data and to develop the predictive models: linear regression (LR) and generalized additive models (GAMs). Six LR species models had a reasonable predictive ability (30–50% of variance explained by the selected predictors). There was a gradient in the quality of the 45 GAM models. Ten species models exhibited both a good fit and statistical robustness: Lemna minor, Phragmites australis, Lysimachia vulgaris, Galium palustre, Lysimachia nummularia, Iris pseudacorus, Lythrum salicaria, Lycopus europaeus, Phalaris arundinacea, Alisma plantago-aquatica, Schoenoplectus lacustris, Carex nigra. Altitude appeared to be a key explanatory variable in most of the species models. In some cases, the degree to which the shore was shaded, connectivity between water bodies, pond area, mineral nitrogen levels, pond age, pond depth, and the extent of agriculture or pasture in the catchment were selected as additional explanatory variables. The species models demonstrated that it is possible to predict species abundance of aquatic macrophytes and that each species responded individually to distinct environmental variables.     

Environmental factors prevail over dispersal constraints in determining the distribution and assembly of Trichoptera species in mountain lakes

Aiming to elucidate whether large‐scale dispersal factors or environmental species sorting prevail in determining patterns of Trichoptera species composition in mountain lakes, we analyzed the distribution and assembly of the most common Trichoptera (Plectrocnemia laetabilis, Polycentropus flavomaculatus, Drusus rectus, Annitella pyrenaea, and Mystacides azurea) in the mountain lakes of the Pyrenees (Spain, France, Andorra) based on a survey of 82 lakes covering the geographical and environmental extremes of the lake district. Spatial autocorrelation in species composition was determined using Moran's eigenvector maps (MEM). Redundancy analysis (RDA) was applied to explore the influence of MEM variables and in‐lake, and catchment environmental variables on Trichoptera assemblages. Variance partitioning analysis (partial RDA) revealed the fraction of species composition variation that could be attributed uniquely to either environmental variability or MEM variables. Finally, the distribution of individual species was analyzed in relation to specific environmental factors using binomial generalized linear models (GLM). Trichoptera assemblages showed spatial structure. However, the most relevant environmental variables in the RDA (i.e., temperature and woody vegetation in‐lake catchments) were also related with spatial variables (i.e., altitude and longitude). Partial RDA revealed that the fraction of variation in species composition that was uniquely explained by environmental variability was larger than that uniquely explained by MEM variables. GLM results showed that the distribution of species with longitudinal bias is related to specific environmental factors with geographical trend. The environmental dependence found agrees with the particular traits of each species. We conclude that Trichoptera species distribution and composition in the lakes of the Pyrenees are governed predominantly by local environmental factors, rather than by dispersal constraints. For boreal lakes, with similar environmental conditions, a strong role of dispersal capacity has been suggested. Further investigation should address the role of spatial scaling, namely absolute geographical distances constraining dispersal and steepness of environmental gradients at short distances.     

Predictions of establishment risk highlight biosurveillance priorities for invasive fish in New Zealand lakes

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Predicting species distributions: a critical comparison of the most common statistical models using artificial species

Aim To test statistical models used to predict species distributions under different shapes of occurrence–environment relationship. We addressed three questions: (1) Is there a statistical technique that has a consistently higher predictive ability than others for all kinds of relationships? (2) How does species prevalence influence the relative performance of models? (3) When an automated stepwise selection procedure is used, does it improve predictive modelling, and are the relevant variables being selected? Location We used environmental data from a real landscape, the state of California, and simulated species distributions within this landscape. Methods Eighteen artificial species were generated, which varied in their occurrence response to the environmental gradients considered (random, linear, Gaussian, threshold or mixed), in the interaction of those factors (no interaction vs. multiplicative), and on their prevalence (50% vs. 5%). The landscape was then randomly sampled with a large (n = 2000) or small (n = 150) sample size, and the predictive ability of each statistical approach was assessed by comparing the true and predicted distributions using five different indexes of performance (area under the receiver‐operator characteristic curve, Kappa, correlation between true and predictive probability of occurrence, sensitivity and specificity). We compared generalized additive models (GAM) with and without flexible degrees of freedom, logistic regressions (general linear models, GLM) with and without variable selection, classification trees, and the genetic algorithm for rule‐set production (GARP). Results Species with threshold and mixed responses, additive environmental effects, and high prevalence generated better predictions than did other species for all statistical models. In general, GAM outperforms all other strategies, although differences with GLM are usually not significant. The two variable‐selection strategies presented here did not discriminate successfully between truly causal factors and correlated environmental variables. Main conclusions Based on our analyses, we recommend the use of GAM or GLM over classification trees or GARP, and the specification of any suspected interaction terms between predictors. An expert‐based variable selection procedure was preferable to the automated procedures used here. Finally, for low‐prevalence species, variability in model performance is both very high and sample‐dependent. This suggests that distribution models for species with low prevalence can be improved through targeted sampling.     

Impacts of space,local environment and habitat connectivity on macrophyte communities in conservation lakes

Aim  To assess the relative impacts of spatial, local environmental and habitat connectivity on the structure of aquatic macrophyte communities in lakes designated for their conservation value. Location  Selected lakes of conservation importance all over Scotland, representing a wide variety of lake habitat types and associated macrophyte communities. Methods  Local environmental variables and species occurrence were measured in the field. Spatial variables were generated using principal coordinates of neighbour matrices (PCNM) analysis. Connectivity between each lake and its neighbours was defined as either (i) all lakes within a radius of 5, 10, 25, 50, 75 or 100 km; (ii) all lakes in same river system; or (iii) all lakes in the same catchment and upstream of the lake. Using variance partitioning within canonical correspondence analysis, the relative impact of E = local environment, S = space and C = lake connectivity was compared on submerged (n = 119 lakes) and emergent (n = 96 lakes) macrophyte assemblages. Results  Local environmental conditions, such as total phosphorus, alkalinity/conductivity and the presence of invasive species, as well as spatial gradients were key drivers of observed variation in macrophyte communities; e.g., for submerged macrophytes, a combination of local to moderate factors relating to water chemistry and broad‐scale gradients reflecting elevation and climate are important. Spatially structured environmental variables explained a large portion of observed variation. Main conclusions  Our findings confirmed the need to manage local environmental pressures such as eutrophication, but suggested that the traditional catchment approach was insufficient. The spatial aggregation of environmental and connectivity factors indicated that a landscape scale approach should be used in lake management to augment the risk assessment to conservation species from the deterioration of suitable lake sites over broad biogeographic areas.     

Stream diatom assemblages as predictors of climate

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Unexpected stasis in a changing world: Lake nutrient and chlorophyll trends since 1990

The United States (U.S.) has faced major environmental changes in recent decades, including agricultural intensification and urban expansion, as well as changes in atmospheric deposition and climate—all of which may influence eutrophication of freshwaters. However, it is unclear whether or how water quality in lakes across diverse ecological settings has responded to environmental change. We quantified water quality trends in 2913 lakes using nutrient and chlorophyll (Chl) observations from the Lake Multi‐Scaled Geospatial and Temporal Database of the Northeast U.S. (LAGOS‐NE), a collection of preexisting lake data mostly from state agencies. LAGOS‐NE was used to quantify whether lake water quality has changed from 1990 to 2013, and whether lake‐specific or regional geophysical factors were related to the observed changes. We modeled change through time using hierarchical linear models for total nitrogen (TN), total phosphorus (TP), stoichiometry (TN:TP), and Chl. Both the slopes (percent change per year) and intercepts (value in 1990) were allowed to vary by lake and region. Across all lakes, TN declined at a rate of 1.1% year^?1, while TP, TN:TP, and Chl did not change. A minority (7%–16%) of individual lakes had changing nutrients, stoichiometry, or Chl. Of those lakes that changed, we found differences in the geospatial variables that were most related to the observed change in the response variables. For example, TN and TN:TP trends were related to region‐level drivers associated with atmospheric deposition of N; TP trends were related to both lake and region‐level drivers associated with climate and land use; and Chl trends were found in regions with high air temperature at the beginning of the study period. We conclude that despite large environmental change and management efforts over recent decades, water quality of lakes in the Midwest and Northeast U.S. has not overwhelmingly degraded or improved.     

FRESHWATER DIATOMS FROM THE CANADIAN ARCTIC TREELINE AND DEVELOPMENT OF PALEOLIMNOLOGICAL INFERENCE MODELS1

Relationships between surface sediment diatom assemblages and measured environmental variables from 77 lakes in the central Canadian arctic treeline region were examined using multivariate statistical methods. Lakes were distributed across the arctic treeline from boreal forest to arctic tundra ecozones, along steep climatic and environmental gradients. Forward selection in canonical correspondence analysis determined that dissolved inorganic carbon (DIC), dissolved organic carbon (DOC), total nitrogen (TN), lake surface area, silica, lake‐water depth, and iron explained significant portions of diatom species variation. Weighted‐averaging (WA) regression and calibration techniques were used to develop inference models for DIC, DOC, and TN from the estimated optima of the diatom taxa to these environmental variables. Simple WA models with classical deshrinking produced models with the strongest predictive abilities for all three variables based on the bootstrapped root mean squared errors of prediction (RMSEP). WA partial least squares showed little improvement over the simpler WA models as judged by the jackknifed RMSEP. These models suggest that it is possible to infer trends in DIC, DOC, and TN from fossil diatom assemblages from suitably chosen lakes in the central Canadian arctic treeline region.     

RELATIONSHIPS BETWEEN LITTORAL DIATOMS AND THEIR CHEMICAL ENVIRONMENT IN NORTHEASTERN GERMAN LAKES AND RIVERS1

We explored statistical relationships between the composition of littoral diatom assemblages and 21 chemical and physical environmental variables in 69 lakes and 15 river sites in the lowland of northeastern Germany. Canonical correspondence analysis with single treatment and with forward selection of environmental variables was used to detect 11 important ecological variables (dissolved inorganic carbon [DIC], Na ⁺ , total phosphorus [TP], dissolved organic carbon [DOC], total nitrogen [TN], pH, oxygen saturation, dissolved iron, SO₄^{2 ?} , NH₄ ⁺ , soluble reactive silicium) and maximum water depth or Ca^{2 +} or soluble reactive phosphorus that most independently explain major proportions of the total diatom variance among the habitats. Monte Carlo permutation tests showed that each contributed a significant additional proportion (P < 0.05) of the variance in species composition. Together, these 11 most important environmental variables explained 34% of the total variance in species composition among the sites and captured 73% of the explained variance from the full 21 parameters model. Weighted‐averaging regression and calibration of 304 indicator taxa with tolerance down‐weighting and classic deshrinking was used to develop transfer functions between littoral diatoms and DIC, pH, TP, TN, and Cl ^? . The DOC:TP ratio was introduced and a weighted‐averaging model was developed to infer allochthonous DOC effects in freshwater ecosystems. This diatom‐DOC/TP model was significant (P < 0.001) and explained 7.6% of the total diatom variance among the sites, surpassing the inferential power of the diatom‐TP‐transfer function (7.3% explained variance). The root‐mean‐square errors of prediction of the models were estimated by jack‐knifing and were comparable with published data sets from surface sediment diatom samples. The data set of littoral diatoms and environmental variables allows use of the diatom‐environmental transfer functions in biomonitoring and paleolimnological approaches across a broad array of natural water resources (such as floodplains, flushed lakes, estuaries, shallow lakes) in the central European lowland ecoregion.     

USING EPILITHIC ALGAL COMMUNITIES TO ASSESS TROPHIC STATUS IN IRISH LAKES1

Relationships between taxonomic composition of shallow epilithic algal communities and nine environmental variables in 32 lakes of different trophic states in Ireland were explored using gradient analysis. A canonical correspondence analysis using four representative environmental variables, alkalinity (correlated with pH and conductivity), maximum phytoplankton chl a (CHL_max) (correlated with total P, total N, and chl), turbidity, and water color explained 21% of the variance in taxa distributions. The first two axes were significant and accounted for 77% of the variance in the periphyton–environmental relationship. The first axis was strongly related to alkalinity and color, which reflected geology and land use in the watersheds. The second axis was most correlated with CHL_max, and separation of lakes corresponded to their Organization for Economic Cooperation and Development (OECD) trophic classification based on water chemistry. Eutrophic lakes were characterized by cyanobacteria taxa and Stigeoclonium sp. Diatoms and desmids were generally more abundant in oligotrophic and mesotrophic lakes. Values for diatom trophic indices were poor indicators of trophic state. Weighted averaging regression and calibration techniques were used to develop transfer functions between 84 taxa and total P, total N, and CHL_max. The total P inference model predicted OECD trophic classification correctly for 84% of the lakes. Values for taxa preferences resulting from such models can provide the foundation for biomonitoring schemes using extant periphyton communities. The turnover time of periphyton taxa should integrate changes in environmental conditions at a temporal scale intermediate to surface‐sediment fossil diatom assemblages and water column variables, which may be more appropriate for detecting annual changes.     

Predicting the distribution of four species of raptors (Aves: Accipitridae) in southern Spain: statistical models work better than existing maps

Aim To test the effectiveness of statistical models based on explanatory environmental variables vs. existing distribution information (maps and breeding atlas), for predicting the distribution of four species of raptors (family Accipitridae): common buzzard Buteo buteo (Linnaeus, 1758), short‐toed eagle Circaetus gallicus (Gmelin, 1788), booted eagle Hieraaetus pennatus (Gmelin, 1788) and black kite Milvus migrans (Boddaert, 1783). Location Andalusia, southern Spain. Methods Generalized linear models of 10 × 10 km squares surveyed for the presence/absence of the species by road census. Statistical models use as predictors variables derived from topography, vegetation and land‐use, and the geographical coordinates (to take account of possible spatial trends). Predictions from the models are compared with current distribution maps from the national breeding atlas and leading reference works. Results The maps derived from statistical models for all four species were more predictive than the previously published range maps and the recent national breeding atlas. The best models incorporated both topographic and vegetation and land‐use variables. Further, in three of the four species the inclusion of spatial coordinates to account for neighbourhood effects improved these models. Models for the common buzzard and black kite were highly predictive and easy to interpret from an ecological point of view, while models for short‐toed eagle and, particularly, booted eagle were not so easy to interpret, but still predicted better than previous distribution information. Main conclusions It is possible to build accurate predictive models for raptor distribution with a limited field survey using as predictors environmental variables derived from digital maps. These models integrated in a geographical information system produced distribution maps that were more accurate than previously published ones for the study species in the study area. Our study is an example of a methodology that could be used for many taxa and areas to improve unreliable distribution information.     

Environmental variables associated with the distribution and occupancy of habitat specialist tadpoles in naturally acidic,oligotrophic waterbodies

Environmental factors play an integral role, either directly or indirectly, in structuring faunal assemblages. Water chemistry, predation, hydroperiod and competition influence tadpole assemblages within waterbodies. We surveyed aquatic predators, habitat refugia, water height and water chemistry variables (pH, salinity and turbidity) at 37 waterbodies over an intensive 22‐day field survey to determine which environmental factors influence the relative abundance and occupancy of two habitat specialist anuran tadpole species in naturally acidic, oligotrophic waterbodies within eastern Australian wallum communities. The majority of tadpoles found were of Litoria olongburensis (wallum sedge frog) and Crinia tinnula (wallum froglet) species, both habitat specialists that are associated with wallum waterbodies and listed as Vulnerable under the IUCN Red List. Tadpoles of two other species (Litoria fallax (eastern sedge frog), and Litoria cooloolensis (cooloola sedge frog)) were recorded from two waterbodies. Tadpoles of Litoria gracilenta (graceful treefrog) were recorded from one waterbody. Relative abundance and occupancy of L. olongburensis tadpoles were associated with pH and water depth. Additionally, L. olongburensis tadpole relative abundance was negatively associated with turbidity. Waterbody occupancy by C. tinnula tadpoles was negatively associated with predatory fish and water depth and positively associated with turbidity. Variables associated with relative abundance of C. tinnula tadpoles were inconclusive and further survey work is required to identify these environmental factors. Our results show that the ecology of specialist and non‐specialist tadpole species associated with ‘unique’ (e.g. wallum) waterbodies is complex and species specific, with specialist species likely dominating unique habitats.     

Models to predict lake annual mean total phosphorus

A lake is a product of processes in its watershed, and these relationships should be empirically quantifiable. Yet few studies have made that attempt. This study quantifies and ranks variables of significance to predict annual mean values of total phosphorus (TP) in small glacial lakes. Several new empirical models based on water chemistry variables, on map parameters of the lake and its catchment, and combinations of such variables are presented. Each variable provides only a limited (statistical) explanation of the variation in annual mean values of TP among lakes. The models are markedly improved by accounting for the distribution of the characteristics (e.g., the mires) in the watershed. The most important map parameters were the proportion of the watershed lying close to the lake covered by rocks and open land (as determined with the drainage area zonation method), relief of the drainage area, lake area and mean depth. These empirical models can be used to predict annual mean TP but only for lakes of the same type. The model based on map parameters (r ²=0.56) appears stable. The effects of other factors/variables not accounted for in the model (like redox-induced internal loading and anthropogenic sources) on the variation in annual mean TP may then be estimated quantitatively by residual analysis. A new mixed model (which combines a dynamic mass-balance approach with empirical knowledge) was also developed. The basic objective was to put the empirical results into a dynamic framework, thereby increasing predictive accuracy. Sensitivity tests of the mixed model indicate that it works as intended. However, comparisons against independent data for annual mean TP show that the predictive power of the mixed model is low, likely because crucial model variables, like sedimentation rate, runoff rate, diffusion rate and precipitation factor, cannot be accurately predicted. These model variables vary among lakes, but this mixed model, like most dynamic models, assumed that they are constants.     

Seventy years of changes in the abundance of Danish charophytes

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Stream macroinvertebrate response to catchment urbanisation (Georgia, U.S.A.)

SUMMARY 1. The effects of catchment urbanisation on water quality were examined for 30 streams (stratified into 15, 50 and 100 km² ± 25% catchments) in the Etowah River basin, Georgia, U.S.A. We examined relationships between land cover (implying cover and use) in these catchments (e.g. urban, forest and agriculture) and macroinvertebrate assemblage attributes using several previously published indices to summarise macroinvertebrate response. Based on a priori predictions as to mechanisms of biotic impairment under changing land cover, additional measurements were made to assess geomorphology, hydrology and chemistry in each stream. 2. We found strong relationships between catchment land cover and stream biota. Taxon richness and other biotic indices that reflected good water quality were negatively related to urban land cover and positively related to forest land cover. Urban land cover alone explained 29–38% of the variation in some macroinvertebrate indices. Reduced water quality was detectable at c. >15% urban land cover. 3. Urban land cover correlated with a number of geomorphic variables such as stream bed sediment size (–) and total suspended solids (+) as well as a number of water chemistry variables including nitrogen and phosphorus concentrations (+), specific conductance (+) and turbidity (+). Biotic indices were better predicted by these reach scale variables than single, catchment scale land cover variables. Multiple regression models explained 69% of variation in total taxon richness and 78% of the variation in the Invertebrate Community Index (ICI) using phi variability, specific conductance and depth, and riffle phi, specific conductance and phi variability, respectively. 4. Indirect ordination analysis was used to describe assemblage and functional group changes among sites and corroborate which environmental variables were most important in driving differences in macroinvertebrate assemblages. The first axis in a non‐metric multidimensional scaling ordination was highly related to environmental variables (slope, specific conductance, phi variability; adj. R²=0.83) that were also important in our multiple regression models. 5. Catchment urbanisation resulted in less diverse and more tolerant stream macroinvertebrate assemblages via increased sediment transport, reduced stream bed sediment size and increased solutes. The biotic indices that were most sensitive to environmental variation were taxon richness, EPT richness and the ICI. Our results were largely consistent over the range in basin size we tested.     

Determinations of clusters,functional groups and variants among lake and catchment area variables

A cluster is a family of variables showing high internal correlation. For example, conductivity, hardness and Ca-concentration which generally appear with correlation coefficients (r) > 0.8 for mean annual values among lakes. They indicate the amount of salts and ions in the water. A functional group is a family of variables which describe a particular process or mechanism in an ecosystem, like mean depth relative depth and volume development. All of these could be related to resuspension. Such variables would also constitute a cluster if they are well correlated across many lakes belonging to a given lake type. These three parameters express different form elements of lakes, they belong to the same functional group in contexts of lake resuspension, but they do not constitute a cluster since they are only rather poorly correlated (the r-values between these parameters is generally <0.5). A variant is a value for a given variable from a defined period of time, like a mean annual or monthly value. One can then ask: Which variant from which time period should be used in relation to a given y-variable one wants to predict to obtain a model with a high predictive power? Variables belonging to the same cluster can often replace one another in models without significantly altering predictive accuracy. The aim of this work is to determine clusters among standard groups of water variables, lake morphometric parameters and catchment parameters. The analysis uses a comprehensive data-set from 95 Swedish lakes. There are about 83,000 lakes in Sweden, about 81,000 belong to this lake type of glacial lakes, which is the most common lake type on Earth. Selected results: Among the catchment parameters, one may note that the proportion of lakes does not co-vary closely with any other parameter, but that the percentage of morainic soils is negatively associated with the area covered by bedrock and flat rocks. Two clusters of morphometric parameters can be identified: Size parameters (e.g., volume and area) and form parameters (e.g., relative depth and dynamic ratio). Among the water variables, colour, iron concentration and Secchi depth are strongly correlated. The concentration of total phosphorus, which is functionally associated with the production of algae, is also related to Secchi depth.     

Combining multivariate statistical techniques and random forests model to assess and diagnose the trophic status of Poyang Lake in China

Floodplain lakes are valuable to humans because of their various functions. An emerging public concern on lake eutrophication has heightened the need to assess and predict the trophic status in floodplain lakes, particularly for those with high spatial heterogeneity. In this study, combined multivariate statistical techniques and random forests model were used to characterize the water quality and trophic status of Poyang Lake. By classifying and characterizing seasonal water samples comprising 11 water quality parameters collected from 13 sampling sites in Poyang Lake between 2008 and 2014, the dataset was divided into the central and northern lake groups, which corresponded to lentic and lotic regions in Poyang Lake, respectively. The spatial water quality variations and underlying patterns were investigated by performing discriminant analysis and principal component analysis (PCA). Lastly, random forests (RF) were used to predict the chlorophyll a (Chl-a) variations of the central and northern lakes. The PCA results indicated that the water quality of the central and northern areas of the lake was controlled by different environmental variables and underlying pollutant sources. The RF model outperformed the artificial neural network and linear regression and was robust with strong predictive capabilities. It was determined that the most important predictors of the Chl-a variations in the northern lake were water temperature (T) and water level, whereas transparency, T, and water level were the most efficient predictors in the central lake. The RF model can also be applied to trophic prediction in other large lakes with considerable spatial variations. This study will have implications on water quality management and eutrophication prevention in floodplain lakes with high spatial heterogeneity.     

Quantifying the impact of environmental variables upon catch per unit effort of the blue shark Prionace glauca in the western English Channel

The effect of environmental variables on blue shark Prionace glauca catch per unit effort (CPUE) in a recreational fishery in the western English Channel, between June and September 1998–2011, was quantified using generalized additive models (GAMs). Sea surface temperature (SST) explained 1·4% of GAM deviance, and highest CPUE occurred at 16·7° C, reflecting the optimal thermal preferences of this species. Surface chlorophyll a concentration (CHL) significantly affected CPUE and caused 27·5% of GAM deviance. Additionally, increasing CHL led to rising CPUE, probably due to higher productivity supporting greater prey biomass. The density of shelf‐sea tidal mixing fronts explained 5% of GAM deviance, but was non‐significant, with increasing front density negatively affecting CPUE. Time‐lagged frontal density significantly affected CPUE, however, causing 12·6% of the deviance in a second GAM and displayed a positive correlation. This outcome suggested a delay between the evolution of frontal features and the subsequent accumulation of productivity and attraction of higher trophic level predators, such as P. glauca.