Prey diversity as a driver of resource partitioning between river‐dwelling fish species

Although food resource partitioning among sympatric species has often been explored in riverine systems, the potential influence of prey diversity on resource partitioning is little known. Using empirical data, we modeled food resource partitioning (assessed as dietary overlap) of coexisting juvenile Atlantic salmon (Salmo salar) and alpine bullhead (Cottus poecilopus). Explanatory variables incorporated into the model were fish abundance, benthic prey diversity and abundance, and several dietary metrics to give a total of seventeen potential explanatory variables. First, a forward stepwise procedure based on the Akaike information criterion was used to select explanatory variables with significant effects on food resource partitioning. Then, linear mixed‐effect models were constructed using the selected explanatory variables and with sampling site as a random factor. Food resource partitioning between salmon and bullhead increased significantly with increasing prey diversity, and the variation in food resource partitioning was best described by the model that included prey diversity as the only explanatory variable. This study provides empirical support for the notion that prey diversity is a key driver of resource partitioning among competing species.     

Fine spatial grain,large spatial extent and biogeography of macrophyte‐associated cladoceran communities across Neotropical floodplains

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Spatial and environmental components of variation in the distribution patterns of subarctic plant species at Kevo, N Finland — a case study at the meso-scale level

This study presents a quantitative partitioning of the total variance in the patterns of occurrence of 231 vascular plant taxa in 362 1 × 1 km grids in the Kevo Nature Reserve into four independent components: purely spatial variation, spatially structured environmental variation, non-spatial environmental variation, and unexplained variation. This partitioning is done with (partial) constrained ordinations (canonical correspondence analysis) and associated Monte Carlo permutation tests. The numerical results suggest that most of the biological variance captured by the external explanatory variables is related to 'local' meso-scale environmental factors, as 12.6% of the variation in the species data is explained solely by the environmental variables. Part of the variance (6%) represents a spatially covarying environmental component, but only a very small part, ca 2%, is related to purely spatial variation. The amount of unexplained variation is very high (>75%). The results are compared and discussed in relation to the relative amounts of these four variance components at broader- and finer-scales and to the concepts of domains and transition zones of scales in biological patterning.     

Climate‐driven uncertainties in modeling terrestrial energy and water fluxes: a site‐level to global‐scale analysis

We used a land surface model constrained using data from flux tower sites, to analyze the biases in ecosystem energy and water fluxes arising due to the use of meteorological reanalysis datasets. Following site‐level model calibration encompassing major vegetation types from the tropics to the northern high‐latitudes, we repeated the site and global simulations using two reanalysis datasets: the NCEP/NCAR and the CRUNCEP. In comparison with the model simulations using observed meteorology from sites, the reanalysis‐driven simulations produced several systematic biases in net radiation (R_n), latent heat (LE), and sensible heat (H) fluxes. These include: (i) persistently positive tropical/subtropical biases in R_n using the NCEP/NCAR, and gradually transitioning to negative R_n biases in the higher latitudes; (ii) large positive H biases in the tropics/subtropics using the NCEP/NCAR; (iii) negative LE biases using the NCEP/NCAR above 40°N; (iv) high tropical LE using the CRUNCEP in comparison with observationally derived global estimates; and (v) flux‐partitioning biases from canopy and ground components. Across vegetation types, we investigated the role of the meteorological drivers (shortwave and longwave radiation, atmospheric humidity, temperature, precipitation) and their seasonal biases in controlling these reanalysis‐driven uncertainties. At the global scale, our site‐level analysis explains several model‐data differences in the LE and H fluxes when compared with observationally derived global estimates of these fluxes. Using our results, we discuss the implications of site‐level model calibration on subsequent regional/global applications to study energy and hydrological processes. The flux‐partitioning biases presented in this study have potential implications on the couplings among terrestrial carbon, energy, and water fluxes, and for the calibration of land–atmosphere parameterizations that are dependent on LE/H partitioning.     

Water–energy,land‐cover and heterogeneity drivers of the distribution of plant species richness in a mountain region of the European Alps

Aim To evaluate the relative importance of water–energy, land‐cover, environmental heterogeneity and spatial variables on the regional distribution of Red‐Listed and common vascular plant species richness. Location Trento Province (c. 6200 km²) on the southern border of the European Alps (Italy), subdivided regularly into 228 3′ × 5′ quadrants. Methods Data from a floristic inventory were separated into two subsets, representing Red‐Listed and common (i.e. all except Red‐Listed) plant species richness. Both subsets were separately related to water–energy, land‐cover and environmental heterogeneity variables. We simultaneously applied ordinary least squares regression with variation partitioning and hierarchical partitioning, attempting to identify the most important factors controlling species richness. We combined the analysis of environmental variables with a trend surface analysis and a spatial autocorrelation analysis. Results At the regional scale, plant species richness of both Red‐Listed and common species was primarily related to energy availability and land cover, whereas environmental heterogeneity had a lesser effect. The greatest number of species of both subsets was found in quadrants with the largest energy availability and the greatest degree of urbanization. These findings suggest that the elevation range within our study region imposes an energy‐driven control on the distribution of species richness, which resembles that of the broader latitude gradient. Overall, the two species subsets had similar trends concerning the relative importance of water–energy, land cover and environmental heterogeneity, showing a few differences regarding the selection of some predictors of secondary importance. The incorporation of spatial variables did not improve the explanatory power of the environmental models and the high original spatial autocorrelation in the response variables was reduced drastically by including the selected environmental variables. Main conclusions Water–energy and land cover showed significant pure effects in explaining plant species richness, indicating that climate and land cover should both be included as explanatory variables in modelling species richness in human‐affected landscapes. However, the high degree of shared variation between the two groups made the relative effects difficult to separate. The relatively low range of variation in the environmental heterogeneity variables within our sampling domain might have caused the low importance of this complex factor.     

Canonical Correspondence Analysis with variation partitioning: some comments and an application

Abstract. This study presents an alternative treatment of data from a comprehensive vegetation study in which the main gradient structure of boreal coniferous forest vegetation in southern Norway was investigated by ordination techniques. The data sets include vegetation samples of different plot sizes, supplied with measurements of 33 environmental explanatory variables (classified in four groups) and nine spatial explanatory variables derived from geographical coordinates. Partitioning the variation of the species-sample plot matrices on different sets of explanatory variables is performed by use of (partial) Canonical Correspondence Analysis. Several aspects of vegetation-environment relationships in the investigation area are discussed on the basis of results obtained by the new method. Generally, ca. 35% of the variation in species abundances are explained by environmental and spatial variables. The results indicate support for the hypothesis of macro-scale topographic control over the differentiation of the vegetation, more strongly so in pine than in spruce forest where soil nutrients play a major role. Towards finer scales, the primary topographical and topographically dependent factors lose importance, and vegetational differentiation is more strongly affected by the accumulated effects of the vegetation (including the tree stand) on soils, shading, litter fall, etc. The fraction of variation in species abundance explained by significant environmental variables was found to be ca. twice as large as the fraction explained by spatial variables. The fraction of variation explained by the supplied variables differed between data sets; it was lower for cryptogams than for vascular plants, and lower for smaller than for larger sample plots. Possible reasons for these patterns are discussed. Some methodological aspects of CCA with variation partitioning are discussed: improvements, necessary precautions, and the advantages over alternative methods.     

Color patterns in white‐beaked dolphins (Lagenorhynchus albirostris) from Iceland

Few studies have examined the natural complex pigmentation patterns of white‐beaked dolphins. From 2002 to 2014, whale‐watching trips in Iceland provided a platform of opportunity to collect a large body of photographs of free‐ranging individuals from a single area of distribution for this species. Based on 823 images, 571 individuals showing one or more color components were identified, and assigned to the following four age classes: adults (n = 437), juveniles (n = 109), calves (n = 14), and neonates (n = 11). A total of 26 color components were observed and described: seven terms previously applied to white‐beaked dolphins, 12 previously applied to other dolphin species, and seven newly defined terms. Results showed that each age class could be positively identified by differences in specific color components, some of which were exclusive. Therefore, color patterns may prove useful in estimating maturity in free‐ranging white‐beaked dolphins. This tool could be further refined through assessment of a wide sample of freshly stranded specimens of known sex and age, which could reveal new age class‐specific components, as well as sexually dimorphic characteristics not seen here. Geographic variation should be investigated by comparing image data sets and stranded animals from different parts of the North Atlantic.     

Discovery of predictive rule sets for chlorophyll-a dynamics in the Nakdong River (Korea) by means of the hybrid evolutionary algorithm HEA

This paper presents a hybrid evolutionary algorithm (HEA) to discover complex rule sets predicting the concentration of chlorophyll-a (Chl.a) based on the measured meteorological, hydrological and limnological variables in the hypertrophic Nakdong River. The HEA is designed: (1) to evolve the structure of rule sets by using genetic programming and (2) to optimise the random parameters in the rule sets by means of a genetic algorithm. Time-series of input–output data from 1995 to 1998 without and with time lags up to 7 days were used for training HEA. Independent input–output data for 1994 were used for testing HEA. HEA successfully discovered rule sets for multiple nonlinear relationships between physical, chemical variables and Chl.a, which proved to be predictive for unseen data as well as explanatory. The comparison of results by HEA and previously applied recurrent artificial neural networks to the same data with input–output time lags of 3 days revealed similar good performances of both methods. The sensitivity analysis for the best performing predictive rule set unraveled relationships between seasons, specific input variables and Chl.a which to some degree correspond with known properties of the Nakdong River. The statistics of numerous random runs of the HEA also allowed determining most relevant input variables without a priori knowledge.     

Ecological gradients driving the distribution of four Ericaceae in boreal Quebec,Canada

Understory species play a significant role in forest ecosystem dynamics. As such, species of the Ericaceae family have a major effect on the regeneration of tree species in boreal ecosystems. It is thus imperative to understand the ecological gradients controlling their distribution and abundance, so that their impacts can be taken into account in sustainable forest management. Using innovative analytical techniques from landscape ecology, we aimed to position, along ecological gradients, four Ericaceae found in the boreal forest of Quebec (Canada) (Rhododendron groenlandicum, Kalmia angustifolia, Chamaedaphne calyculata, and Vaccinium spp), to regionalize these species into landscape units relevant to forest management, and to estimate the relative importance of several ecological drivers (climate, disturbances, stand attributes, and physical environment) that control the species distribution and abundance. We conducted our study in boreal Quebec, over a study area covering 535,355 km². We used data from 15,339 ecological survey plots and forest maps to characterize 1422 ecological districts covering the study region. We evaluated the relative proportion of each ericaceous species and explanatory variables at the district level. Vegetation and explanatory variables matrices were used to conduct redundancy, cluster, and variation partitioning analyses. We observed that ericaceous species are mainly distributed in the western part of the study area and each species has a distinct latitudinal and longitudinal gradient distribution. On the basis of these gradients, we delimited 10 homogeneous landscape units distinct in terms of ericaceous species abundance and environmental drivers. The distribution of the ericaceous species along ecological gradients is closely related to the overlaps between the four sets of explanatory variables considered. We conclude that the studied Ericaceae occupy specific positions along ecological gradients and possess a specific abundance and distribution controlled by the integration of multiple explanatory variables.     

Exploration of data partitioning in an eight‐gene data set: phylogeny of metalmark moths (Lepidoptera,Choreutidae)

Rota, J. & Wahlberg, N. (2012). Exploration of data partitioning in an eight‐gene data set: phylogeny of metalmark moths (Lepidoptera, Choreutidae). —Zoologica Scripta, 41, 536–546. Molecular data sets for phylogenetic inference continue to increase in size, especially with respect to the number of genes sampled. As more and more genes are included in analyses, the importance of partitioning the data to avoid problems that can arise from underparameterization becomes more apparent. With an eight‐gene data set from 38 metalmark moth species (12 genera represented) and three outgroups, we explored different data partitioning strategies and their influence on convergence and mixing of Markov Chains Monte Carlo in a Bayesian setting. We found that in larger data sets, with an increase in the number of partitions that are made a priori (e.g. by gene and codon position), convergence and mixing become poor. This problem can be overcome by using a recently published algorithm in which homologous sites are grouped into blocks with similar evolutionary rates that can then be modelled as separate data subsets. Using this novel approach to data partitioning, our analyses resolve with strong support relationships among the genera of metalmark moths. Support for the monophyly of the family, the two subfamilies and all genera except Hemerophila is strong. Hemerophila is broken into two separate clades, Hemerophila sensu stricto and another well‐supported clade. To render Hemerophila monophyletic, we describe a new genus, Ornarantia Rota, gen. nov., and transfer 18 species from Hemerophila to it. The type species of Ornarantia is Hemerophila laciniosella Busck, 1914.     

Can niche‐based distribution models outperform spatial interpolation?

Aim Distribution modelling relates sparse data on species occurrence or abundance to environmental information to predict the population of a species at any point in space. Recently, the importance of spatial autocorrelation in distributions has been recognized. Spatial autocorrelation can be categorized as exogenous (stemming from autocorrelation in the underlying variables) or endogenous (stemming from activities of the organism itself, such as dispersal). Typically, one asks whether spatial models explain additional variability (endogenous) in comparison to a fully specified habitat model. We turned this question around and asked: can habitat models explain additional variation when spatial structure is accounted for in a fully specified spatially explicit model? The aim was to find out to what degree habitat models may be inadvertently capturing spatial structure rather than true explanatory mechanisms. Location We used data from 190 species of the North American Breeding Bird Survey covering the conterminous United States and southern Canada. Methods We built 13 different models on 190 bird species using regression trees. Our habitat‐based models used climate and landcover variables as independent variables. We also used random variables and simulated ranges to validate our results. The two spatially explicit models included only geographical coordinates or a contagion term as independent variables. As another angle on the question of mechanism vs. spatial structure we pitted a model using related bird species as predictors against a model using randomly selected bird species. Results The spatially explicit models outperformed the traditional habitat models and the random predictor species outperformed the related predictor species. In addition, environmental variables produced a substantial R² in predicting artificial ranges. Main conclusions We conclude that many explanatory variables with suitable spatial structure can work well in species distribution models. The predictive power of environmental variables is not necessarily mechanistic, and spatial interpolation can outperform environmental explanatory variables.     

Species–area relationships of red-listed species in old boreal forests: a large-scale data analysis

Aim Species–area relationships are often applied, but not generally approved, to guide practical conservation planning. The specific species group analysed may affect their applicability. We asked if species–area curves constructed from extensive databases of various sectors of natural resource administration can provide insights into large‐scale conservation of boreal forest biodiversity if the analyses are restricted only to red‐listed species. Location Finland, northern Europe. Methods Our data included 12,645 records of 219 red‐listed Coleoptera and Fungi from the whole of Finland. The forest data also covered the entire country, 202,761 km². The units of species–area analyses were 224 municipalities where the red‐listed forest species have been observed. We performed a hierarchical partitioning analysis to reveal the relative importance of different potential explanatory variables. Based on the results, for all red‐listed species, species associated with coniferous trees and for Fungi, the area of economically over‐aged forests explained the best the variation in data. For species associated with deciduous trees and Coleoptera, the forest area explained better variation in data than the area of old forests. In the subsequent log–log species–area regression analyses, we used the best variables as the explanatory variable for each species group. Results There was a strong relationship between the number of all red‐listed species and the area of old forests remaining, with a z‐value of 0.45. The area explained better the number of species associated with conifer trees and Fungi than the number of species associated with deciduous trees and Coleoptera. Main conclusions The high z‐values of species–area curves indicate that the remaining old‐growth patches constitute a real archipelago for the conifer‐associated red‐listed species, since lower values had been expected if the surrounding habitat matrix were a suitable habitat for the species analysed.     

Family‐level variation in early life‐cycle traits of kelp

Temperate kelp forests (Laminarians) are threatened by temperature stress due to ocean warming and photoinhibition due to increased light associated with canopy loss. However, the potential for evolutionary adaptation in kelp to rapid climate change is not well known. This study examined family‐level variation in physiological and photosynthetic traits in the early life‐cycle stages of the ecologically important Australasian kelp Ecklonia radiata and the response of E. radiata families to different temperature and light environments using a family × environment design. There was strong family‐level variation in traits relating to morphology (surface area measures, branch length, branch count) and photosynthetic performance (F_v/F_m) in both haploid (gametophyte) and diploid (sporophyte) stages of the life‐cycle. Additionally, the presence of family × environment interactions showed that offspring from different families respond differently to temperature and light in the branch length of male gametophytes and oogonia surface area of female gametophytes. Negative responses to high temperatures were stronger for females vs. males. Our findings suggest E. radiata may be able to respond adaptively to climate change but studies partitioning the narrow vs. broad sense components of heritable variation are needed to establish the evolutionary potential of E. radiata to adapt under climate change.     

Can we explain regional abundance and road‐kill patterns with variables derived from local‐scale road‐kill models? Evaluating transferability with the European polecat

Aim We evaluated the transferability of variables previously found to have a significant effect on European polecat Mustela putorius road‐kills at a local scale (i.e. 50 m around location points) when we extrapolate them to a large scale [Universal Transverse Mercator (UTM) 100 km²] in a neighbouring area. Location Andalusia, south Spain. We carried out our study in 821 of the 985 UTM 100 km² cells included in this region. Methods The units of the different variables were adapted to the new scale. We used data from the Spanish Atlas survey to obtain the abundance of the different species and GIS data for the rest of the variables. We controlled the spatial autocorrelation by incorporating spatial filters obtained with Spatial Eigenvector Mapping into multiple regression analyses. We used AIC criteria and the best subset procedure to investigate the relationship between the selected variables and species abundance, and road‐kill occurrence. Results The best subset procedure provided two models that explained 40% of variation in polecat abundance and eleven models that explained around 25% of variation in road‐kills. The main explanatory factor for polecat abundance was the abundance of other carnivores, whereas polecat abundance was the main factor for road‐kills. In both cases, rabbit abundance was the second most important explanatory variable. Main Conclusions Our findings highlight the possibility of partially explaining the abundance and road‐kill patterns at a large scale based on significant variables from local‐scale models. Mitigation measures to reduce polecat fatalities should combine actions at different scales. Routes that cross carnivore hotspots, including those of polecats, and areas with important populations of rabbits, should be avoided during road planning. When these routes are unavoidable, local‐scale mitigation measures must be implemented.     

Modelling spatial and environmental effects at the edge of the distribution: the red-backed shrike Lanius collurio in Northern Portugal

This study examined the interplay of spatial and environmental effects shaping the range margin of the red‐backed shrike (Lanius collurio) in northern Portugal. The occurrence of shrikes in 10 × 10 km UTM squares was related to three sets of explanatory variables, reflecting environmental effects (climate and habitat), large‐scale spatial trends, and neighbourhood influences (considering an autologistic term); spatial variables were used as surrogates for historical and demographic factors. Multiple logistic regression models were built for each set, and then variation partitioning based on partial regressions isolated the unique and shared components of explained variation. The environmental model revealed a dominant influence of climate effects, with the occurrence of shrikes increasing with frost and thermal amplitude, declining with insolation, and responding unimodally to rainfall. There was a weaker influence of habitat conditions, though shrikes were more likely with increasing cover by annual crops and pastures, and decreasing forest cover. Only a relatively small proportion of explained variation was due to a ‘pure’ environmental component (10.4%), as most variation explained by environmental factors appeared spatially structured (51.9%). The unique contributions of spatial variables to the overall model were also small, though the neighbourhood effects appeared relatively stronger than large‐scale trends. Taken together, results suggested that the south‐western range margin of the red‐backed shrike was largely determined by spatially structured environmental factors. Nevertheless, there were also ‘pure’ environmental factors determining some isolate occurrences irrespective of any spatial structure, and ‘pure’ spatial factors that appeared to favour the occupation of squares surrounding the core distribution areas irrespective of environmental conditions. These results add to the growing evidence that both environmental and spatial factors need to be considered in predictive modelling of species range margins.     

Habitat distribution models for intertidal seaweeds: responses to climatic and non‐climatic drivers

Aim Because intertidal organisms often live close to their physiological tolerance limits, they are potentially sensitive indicators of climate‐driven changes in the environment. The goals of this study were to assess the effect of climatic and non‐climatic factors on the geographical distribution of intertidal macroalgae, and to predict future distributions under different climate‐warming scenarios. Location North‐western Iberian Peninsula, southern Europe. Methods We developed distribution models for six ecologically important intertidal seaweed species. Occurrence and microhabitat data were sampled at 1‐km² resolution and analysed with climate variables measured at larger spatial scales. We used generalized linear models and applied the deviance and Bayesian information criterion to model the relationship between environmental variables and the distribution of each target species. We also used hierarchical partitioning (HP) to identify predictor variables with higher independent explanatory power. Results The distributions of Himanthalia elongata and Bifurcaria bifurcata were correlated with measures of terrestrial and marine climate, although in opposite directions. Model projections under two warming scenarios indicated the extinction of the former at a faster rate in the Cantabrian Sea (northern Spain) than in the Atlantic (west). In contrast, these models predicted an increase in the occurrence of B. bifurcata in both areas. The occurrences of Ascophyllum nodosum and Pelvetia canaliculata, species showing rather static historical distributions, were related to specific non‐climatic environmental conditions and locations, such as the location of sheltered sites. At the southernmost distributional limit, these habitats may present favourable microclimatic conditions or provide refuges from competitors or natural enemies. Model performances for Fucus vesiculosus and F. serratus were similar and poor, but several climatic variables influenced the occurrence of the latter in the HP analyses. Main conclusions The correlation between species distributions and climate was evident for two species, whereas the distributions of the others were associated with non‐climatic predictors. We hypothesize that the distribution of F. serratus responds to diverse combinations of factors in different sections of the north‐west Iberian Peninsula. Our study shows how the response of species distributions to climatic and non‐climatic variables may be complex and vary geographically. Our analyses also highlight the difficulty of making predictions based solely on variation in climatic factors measured at coarse spatial scales.     

Spatiotemporal dynamics of Lutzomyia longipalpis and macro‐habitat characterization using satellite images in a leishmaniasis‐endemic city in Argentina

The spatiotemporal population dynamics of Lutzomyia longipalpis (Lutz & Neiva, 1912) (Diptera: Psychodidae) were evaluated in a city in Argentina in which visceral leishmaniasis is endemic. Over 14 sampling sessions, 5244 specimens of five species of Phlebotominae (Diptera: Psychodidae) were captured, of which 2458 (46.87%) specimens were L. longipalpis. Generalized linear models were constructed to evaluate the associations between L. longipalpis abundance and explanatory variables derived from satellite images. The spatial variable ‘stratum’ and the temporal variable ‘season’ were also included in the models. Three variables were found to have significant associations: the normalized difference vegetation index; land surface temperature, and low urban coverage. The last two of these were associated with L. longipalpis abundance only during summer and winter, respectively. This variation between seasons supports the development of models that include temporal variables because models of distributions of the abundance of a species may show different critical variables according to the climatic period of the year. Abundance decreased gradually towards the downtown area, which suggests that L. longipalpis responds to a meta‐population structure, in which rural–periurban source populations that persist over time may colonize adjacent areas. This information allows for a spatiotemporal stratification of risk, which provides public health authorities with a valuable tool to help optimize prevention measures against visceral leishmaniasis.     

A novel framework for disentangling the scale‐dependent influences of abiotic factors on alpine treeline position

Low‐temperature growth limitation largely determines alpine treeline position globally, but treeline elevation also varies locally at a range of scales in response to multiple biotic and abiotic factors. In this study, we conceptualise how variability in treeline elevation is related to abiotic factors that act as thermal modifiers, physiological stressors, or disturbance agents. We then present a novel analytical framework for quantifying how abiotic factors influence treeline elevation at different spatial scales using New Zealand Nothofagus treelines as a case study. We delineated Nothofagus treelines in a GIS, along which we extracted data for treeline elevation and eight abiotic explanatory variables at 54 000 points. Each location was classified at each of five spatial scales based on nested river catchments, ranging from large regional to small hillslope catchments. We used hierarchical linear models to partition the variation in both treeline elevation and the eight abiotic variables by spatial scale, and then quantified the relationships between these at each spatial scale in turn. Nothofagus treeline elevation varied from 800–1740 m a.s.l. across New Zealand. Abiotic factors explained 82% of the variation in treeline elevation at the largest (regional) scale and 44–52% of variation at the four finer scales. Broad‐scale variation in Nothofagus treeline elevation was strongly associated with thermal modifiers, consistent with the idea that treelines coincide with a temperature‐driven, physiological limit. However, much of the finer‐scale variation in treeline elevation was explained by a combination of thermal, physiological stress‐related, and disturbance variables operating at different spatial scales. The conceptual model and analytical methods developed here provide a general framework for understanding treeline variation at different spatial scales.     

Does local habitat fragmentation affect large‐scale distributions? The case of a specialist grassland bird

Aim

Although the negative effects of habitat fragmentation have been widely documented at the landscape scale, much less is known about its impacts on species distributions at the biogeographical scale. We hypothesize that fragmentation influences the large‐scale distribution of area‐ and edge‐sensitive species by limiting their occurrence in regions with fragmented habitats , despite otherwise favourable environmental conditions. We test this hypothesis by assessing the interplay of climate and landscape factors influencing the distribution of the calandra lark, a grassland specialist that is highly sensitive to habitat fragmentation.

Location

Iberia Peninsula, Europe.

Methods

Ecological niche modelling was used to investigate the relative influence of climate/topography, landscape fragmentation and spatial structure on calandra lark distribution. Modelling assumed explicitly a hierarchically structured effect among explanatory variables, with climate/topography operating at broader spatial scales than landscape variables. An eigenvector‐based spatial filtering approach was used to cancel bias introduced by spatial autocorrelation. The information theoretic approach was used in model selection, and variation partitioning was used to isolate the unique and shared effects of sets of explanatory variables.

Results

Climate and topography were the most influential variables shaping the distribution of calandra lark, but incorporating landscape metrics contributed significantly to model improvement. The probability of calandra lark occurrence increased with total habitat area and declined with the number of patches and edge density. Variation partitioning showed a strong overlap between variation explained by climate/topography and landscape variables. After accounting for spatial structure in species distribution, the explanatory power of environmental variables remained largely unchanged.

Main conclusions

We have shown here that landscape fragmentation can influence species distributions at the biogeographical scale. Incorporating fragmentation metrics into large‐scale ecological niche models may contribute for a better understanding of mechanism driving species distributions and for improving predictive modelling of range shifts associated with land use and climate changes.

     

Scale-dependence of fish habitat associations in a North American river system

1. Determining the appropriate measurement scale to assess habitat variables is critical for ecologists assessing biological or ecological conditions. Depth, velocity, substrate, woody debris and other fish cover variables occur on both reach and microhabitat scales, and fish habitat associations with these variables may be scale-dependent. The aim of this work was to better understand the importance of scale for fish–habitat associations with these variables in a framework consistent with environmental filtering and to test the hypothesis that habitat variable importance is scale-dependent.
2. I used prepositioned areal electrofishing in wadeable streams of the Delaware River basin to evaluate the associations of fish with the same variables summarised on different reach and microhabitat scales. The importance of scale for fish–habitat associations was assessed using two approaches that approximate an environmental filtering framework: variance partitioning with (1) ordination and (2) generalised linear mixed models.
3. Variables on both the reach and microhabitat scales explained a significant fraction of the total variation in fish community composition (p < 0.05). Variation decomposition of reach- and microhabitat-scale effects revealed 20.2% and 2.0% of all variation were due uniquely to reach and microhabitat scales, respectively. Measures of coarseness, embeddedness, amount of riffle and areal coverage of five fish cover variables were significant explanatory variables of community composition at the reach scale only (p < 0.05). Velocity and mesohabitat (amount or presence of riffle) were the only two habitat features that were significant explanatory variables of fish community composition at both the reach and microhabitat scales (p < 0.05). Individual models of species occurrence revealed similar patterns as seen with analyses of community composition.
4. For many fishes, habitat features quantified at the reach scale were more explanatory than at the microhabitat scale. Longnose dace (Rhinichthys cataractae) were more dependent upon microhabitat variables than reach-scale variables, relative to other fishes. Mean velocity at the reach scale was the most important explanatory variable for explaining fish community composition and indicated support for the concept of environmental filtering at the reach and microhabitat scales.
5. Few studies of fish occurrence have incorporated a study design and analytical framework that approximates the hierarchical nature of habitat. This study identifies important scales and predictors, demonstrates the importance of a multiscale approach, and provides support for the environmental filtering concept at the reach and microhabitat scales. These findings will allow ecologists to better account for scale-dependent habitat associations and justify the use of fish habitat associations on reach and microhabitat scales for assessing biotic integrity, restoration and conservation of fishes.