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Environmental Predictors (environmental + predictor)
Selected AbstractsEnvironmental Predictors of Geographic Variation in Human Mating PreferencesETHOLOGY, Issue 4 2002Kevin J. McGraw Sexual selection theory classically posits consistent and directional mate-preferences for male traits that provide benefits to females. However, flexible mate-choice tactics may persist within a species when males display multiple desirable features that confer different benefits to females under variable environmental conditions. Ecological factors such as population density, resource demand, and sex ratio can influence the value that female animals place on certain male characteristics across mating environments. In this study, I used human mate-preference data from `lonely hearts' advertisements in the newspapers of 23 cities in the USA to assess geographic differences in female preferences for male traits (e.g. physical attributes, resource-holding potential, emotional characteristics, personal interests) in relation to these ecological parameters. I found that females placed more emphasis on the resource-accruing ability of prospective mates in densely populated cities and cities having greater resource demands (higher cost of living). In contrast, women from densely populated or resource-demanding cities placed less emphasis on the emotional aspects or personal interests of males. Preferences for physical features were not environmentally linked, but instead were a function of the degree to which females advertised their own physical attractiveness. Collectively, these results suggest that certain mate-choice criteria employed by women are sensitive to variation in local environmental conditions and that variable levels of resource or mate availability may favor different mating tactics across human populations. [source] Home Again: Environmental Predictors of Place Attachment and Self-esteem for New Retirement Community ResidentsJOURNAL OF INTERIOR DESIGN, Issue 1 2002Paul E. Eshelman M.F.A. ABSTRACT This study examined the relative contribution of two dimensions of interior features functional and personal meaning,as predictors of place attachment and self-esteem for ninety-two new retirement community residents housed in independent living apartments or cottages of a recently opened continuing care retirement community (CCRC). Residents were interviewed and facilities observed as part of a multi-disciplinary, collaborative study. Stepwise regression determined which subsets of function and meaning variables respectively operated as the most important, independent predictors for place attachment and self-esteem. Hierarchical regression equations then examined the relationship between function and meaning variables in predicting place attachment and self-esteem, asking: exceeding the effects of function, does meaning add to a feeling of place attachment and self-esteem? For both place attachment and self-esteem, significantly more variance is accounted for when meaning variables are added to function variables. Once functional needs are met, both place attachment and self-esteem are elevated by interior features that have personal meaning. These findings expand the concept of hominess widely used in the design of residential caregiving settings. [source] Local- to continental-scale variation in the richness and composition of an aquatic food webGLOBAL ECOLOGY, Issue 5 2010Hannah L. Buckley ABSTRACT Aim, We investigated patterns of species richness and composition of the aquatic food web found in the liquid-filled leaves of the North American purple pitcher plant, Sarracenia purpurea (Sarraceniaceae), from local to continental scales. Location, We sampled 20 pitcher-plant communities at each of 39 sites spanning the geographic range of S. purpurea, from northern Florida to Newfoundland and westward to eastern British Columbia. Methods, Environmental predictors of variation in species composition and species richness were measured at two different spatial scales: among pitchers within sites and among sites. Hierarchical Bayesian models were used to examine correlates and similarities of species richness and abundance within and among sites. Results, Ninety-two taxa of arthropods, protozoa and bacteria were identified in the 780 pitcher samples. The variation in the species composition of this multi-trophic level community across the broad geographic range of the host plant was lower than the variation among pitchers within host-plant populations. Variation among food webs in richness and composition was related to climate, pore-water chemistry, pitcher-plant morphology and leaf age. Variation in the abundance of the five most common invertebrates was also strongly related to pitcher morphology and site-specific climatic and other environmental variables. Main conclusions, The surprising result that these communities are more variable within their host-plant populations than across North America suggests that the food web in S. purpurea leaves consists of two groups of species: (1) a core group of mostly obligate pitcher-plant residents that have evolved strong requirements for the host plant and that co-occur consistently across North America, and (2) a larger set of relatively uncommon, generalist taxa that co-occur patchily. [source] Temperature-dependent stock-recruitment model for walleye pollock (Theragra chalcogramma) around northern JapanFISHERIES OCEANOGRAPHY, Issue 6 2007TETSUICHIRO FUNAMOTO Abstract Changes in fish year-class strength have been attributed to year-to-year variability in environmental conditions and spawning stock biomass (SSB). In particular, sea temperature has been shown to be linked to fish recruitment. In the present study, I examined the relationship between sea surface temperature (SST), SSB and recruitment for two stocks of walleye pollock (Theragra chalcogramma) around northern Japan [Japanese Pacific stock (JPS) and northern Japan Sea stock (JSS)] using a temperature-dependent stock-recruitment model (TDSRM). The recruitment fluctuation of JPS was successfully reproduced by the TDSRM with February and April SSTs, and February SST was a better environmental predictor than April SST. In addition, the JPS recruitment was positively related to February SST and negatively to April SST. The JSS recruitment modeled by the TDSRM incorporating February SST was also consistent with the observation, whereas the relationship between recruitment and February SST was negative, that is the opposite trend to JPS. These findings suggest that SST in February is important as a predictor of recruitment for both stocks, and that higher and lower SSTs in February act favorably on the recruitment of JPS and JSS respectively. Furthermore, Ricker-type TDSRM was not selected for either of the stocks, suggesting that the strong density-dependent effect as in the Ricker model does not exist for JPS and JSS. I formulate hypotheses to explain the links between SST and recruitment, and note that these relationships should be considered in any future attempts to understand the recruitment dynamics of JPS and JSS. [source] Richness patterns, species distributions and the principle of extreme deconstructionGLOBAL ECOLOGY, Issue 2 2009Levi Carina Terribile ABSTRACT Aim, To analyse the global patterns in species richness of Viperidae snakes through the deconstruction of richness into sets of species according to their distribution models, range size, body size and phylogenetic structure, and to test if environmental drivers explaining the geographical ranges of species are similar to those explaining richness patterns, something we called the extreme deconstruction principle. Location, Global. Methods, We generated a global dataset of 228 terrestrial viperid snakes, which included geographical ranges (mapped at 1° resolution, for a grid with 7331 cells world-wide), body sizes and phylogenetic relationships among species. We used logistic regression (generalized linear model; GLM) to model species geographical ranges with five environmental predictors. Sets of species richness were also generated for large and small-bodied species, for basal and derived species and for four classes of geographical range sizes. Richness patterns were also modelled against the five environmental variables through standard ordinary least squares (OLS) multiple regressions. These subsets are replications to test if environmental factors driving species geographical ranges can be directly associated with those explaining richness patterns. Results, Around 48% of the total variance in viperid richness was explained by the environmental model, but richness sets revealed different patterns across the world. The similarity between OLS coefficients and the primacy of variables across species geographical range GLMs was equal to 0.645 when analysing all viperid snakes. Thus, in general, when an environmental predictor it is important to model species geographical ranges, this predictor is also important when modelling richness, so that the extreme deconstruction principle holds. However, replicating this correlation using subsets of species within different categories in body size, range size and phylogenetic structure gave more variable results, with correlations between GLM and OLS coefficients varying from ,0.46 up to 0.83. Despite this, there is a relatively high correspondence (r = 0.73) between the similarity of GLM-OLS coefficients and R2 values of richness models, indicating that when richness is well explained by the environment, the relative importance of environmental drivers is similar in the richness OLS and its corresponding set of GLMs. Main conclusions, The deconstruction of species richness based on macroecological traits revealed that, at least for range size and phylogenetic level, the causes underlying patterns in viperid richness differ for the various sets of species. On the other hand, our analyses of extreme deconstruction using GLM for species geographical range support the idea that, if environmental drivers determine the geographical distribution of species by establishing niche boundaries, it is expected, at least in theory, that the overlap among ranges (i.e. richness) will reveal similar effects of these environmental drivers. Richness patterns may be indeed viewed as macroecological consequences of population-level processes acting on species geographical ranges. [source] Specialized morphology for a generalist diet: evidence for Liem's Paradox in a cichlid fishJOURNAL OF FISH BIOLOGY, Issue 7 2009S. A. Binning The stable isotope ratio and seasonal changes in diet of Alluaud's haplo Astatoreochromis alluaudi, a cichlid fish with massive pharyngeal jaws well known for its ability to process hard-bodied prey, are described. The diet of A. alluaudi was quantified in Lake Saka, Uganda, over a period of 30 months. Variation in physico-chemical variables (mean monthly rainfall, water temperature, turbidity and dissolved oxygen), as well as potential competitor density and food abundance, was measured throughout the second half of the study (14 months). Stomach contents and isotope analysis revealed a diet comprised mainly of fishes and insects, with a low contribution of molluscs (0,33%) in any given month. No correlation was detected between diet and either macroinvertebrate abundance or competitor abundance. The running average rainfall was positively related to the percentage of fish consumed per month. Although A. alluaudi exhibits an apparent molluscivorous trophic morphology in Lake Saka, molluscs did not appear to compose a major portion of its diet. Gradients of rainfall seemed to be the most important environmental predictor of diet choice in Lake Saka. These results are discussed with reference to Liem's Paradox that apparently morphologically specialized fishes often function as generalist feeders in the wild. [source] Differences in spatial predictions among species distribution modeling methods vary with species traits and environmental predictorsECOGRAPHY, Issue 6 2009Alexandra D. Syphard Prediction maps produced by species distribution models (SDMs) influence decision-making in resource management or designation of land in conservation planning. Many studies have compared the prediction accuracy of different SDM modeling methods, but few have quantified the similarity among prediction maps. There has also been little systematic exploration of how the relative importance of different predictor variables varies among model types and affects map similarity. Our objective was to expand the evaluation of SDM performance for 45 plant species in southern California to better understand how map predictions vary among model types, and to explain what factors may affect spatial correspondence, including the selection and relative importance of different environmental variables. Four types of models were tested. Correlation among maps was highest between generalized linear models (GLMs) and generalized additive models (GAMs) and lowest between classification trees and GAMs or GLMs. Correlation between Random Forests (RFs) and GAMs was the same as between RFs and classification trees. Spatial correspondence among maps was influenced the most by model prediction accuracy (AUC) and species prevalence; map correspondence was highest when accuracy was high and prevalence was intermediate (average prevalence for all species was 0.124). Species functional type and the selection of climate variables also influenced map correspondence. For most (but not all) species, climate variables were more important than terrain or soil in predicting their distributions. Environmental variable selection varied according to modeling method, but the largest differences were between RFs and GLMs or GAMs. Although prediction accuracy was equal for GLMs, GAMs, and RFs, the differences in spatial predictions suggest that it may be important to evaluate the results of more than one model to estimate the range of spatial uncertainty before making planning decisions based on map outputs. This may be particularly important if models have low accuracy or if species prevalence is not intermediate. [source] Geographic body size gradients in tropical regions: water deficit and anuran body size in the Brazilian CerradoECOGRAPHY, Issue 4 2009Miguel Á. Olalla-Tárraga A recent interspecific study found Bergmann's size clines for Holarctic anurans and proposed an explanation based on heat balance to account for the pattern. However, this analysis was limited to cold temperate regions, and exploring the patterns in warmer tropical climates may reveal other factors that also influence anuran body size variation. We address this using a Cerrado anuran database. We examine the relationship between mean body size in a grid of 1° cells and environmental predictors and test the relative support for four hypotheses using an AIC-based model selection approach. Also, we considered three different amphibian phylogenies to partition the phylogenetic and specific components of the interspecific variation in body size using a method analogous to phylogenetic eigen vector regression (PVR). To consider the potential effects of spatial autocorrelation we use eigenvector-based spatial filters. We found the largest species inhabiting high water deficit areas in the northeast and the smallest in the wet southwest. Our results are consistent with the water availability hypothesis which, coupled with previous findings, suggests that the major determinant of interspecific body size variation in anurans switches from energy to water towards the equator. We propose that anuran body size gradients reflect effects of reduced surface to volume ratios in larger species to control both heat and water balance. [source] Coefficient shifts in geographical ecology: an empirical evaluation of spatial and non-spatial regressionECOGRAPHY, Issue 2 2009L. Mauricio Bini A major focus of geographical ecology and macroecology is to understand the causes of spatially structured ecological patterns. However, achieving this understanding can be complicated when using multiple regression, because the relative importance of explanatory variables, as measured by regression coefficients, can shift depending on whether spatially explicit or non-spatial modeling is used. However, the extent to which coefficients may shift and why shifts occur are unclear. Here, we analyze the relationship between environmental predictors and the geographical distribution of species richness, body size, range size and abundance in 97 multi-factorial data sets. Our goal was to compare standardized partial regression coefficients of non-spatial ordinary least squares regressions (i.e. models fitted using ordinary least squares without taking autocorrelation into account; "OLS models" hereafter) and eight spatial methods to evaluate the frequency of coefficient shifts and identify characteristics of data that might predict when shifts are likely. We generated three metrics of coefficient shifts and eight characteristics of the data sets as predictors of shifts. Typical of ecological data, spatial autocorrelation in the residuals of OLS models was found in most data sets. The spatial models varied in the extent to which they minimized residual spatial autocorrelation. Patterns of coefficient shifts also varied among methods and datasets, although the magnitudes of shifts tended to be small in all cases. We were unable to identify strong predictors of shifts, including the levels of autocorrelation in either explanatory variables or model residuals. Thus, changes in coefficients between spatial and non-spatial methods depend on the method used and are largely idiosyncratic, making it difficult to predict when or why shifts occur. We conclude that the ecological importance of regression coefficients cannot be evaluated with confidence irrespective of whether spatially explicit modelling is used or not. Researchers may have little choice but to be more explicit about the uncertainty of models and more cautious in their interpretation. [source] The significance of geographic range size for spatial diversity patterns in Neotropical palmsECOGRAPHY, Issue 1 2006Holger Kreft We examined the effect of range size in commonly applied macroecological analyses using continental distribution data for all 550 Neotropical palm species (Arecaceae) at varying grain sizes from 0.5° to 5°. First, we evaluated the relative contribution of range-restricted and widespread species on the patterns of species richness and endemism. Second, we analysed the impact of range size on the predictive value of commonly used predictor variables. Species sequences were produced arranging species according to their range size in ascending, descending, and random order. Correlations between the cumulative species richness patterns of these sequences and environmental predictors were performed in order to analyse the effect of range size. Despite the high proportion of rare species, patterns of species richness were found to be dominated by a minority of widespread species (,20%) which contained 80% of the spatial information. Climatic factors related to energy and water availability and productivity accounted for much of the spatial variation of species richness of widespread species. In contrast, species richness of range-restricted species was to a larger extent determined by topographical complexity. However, this effect was much more difficult to detect due to a dominant influence of widespread species. Although the strength of different environmental predictors changed with spatial scale, the general patterns and trends proved to be relatively stabile at the examined grain sizes. Our results highlight the difficulties to approximate causal explanations for the occurrence of a majority of species and to distinguish between contemporary climatic factors and history. [source] ANNA: A new prediction method for bioassessment programsFRESHWATER BIOLOGY, Issue 1 2005Simon Linke Summary 1. Cluster analysis of reference sites with similar biota is the initial step in creating River Invertebrate Prediction and Classification System (RIVPACS) and similar river bioassessment models such as Australian River Assessment System (AUSRIVAS). This paper describes and tests an alternative prediction method, Assessment by Nearest Neighbour Analysis (ANNA), based on the same philosophy as RIVPACS and AUSRIVAS but without the grouping step that some people view as artificial. 2. The steps in creating ANNA models are: (i) weighting the predictor variables using a multivariate approach analogous to principal axis correlations, (ii) calculating the weighted Euclidian distance from a test site to the reference sites based on the environmental predictors, (iii) predicting the faunal composition based on the nearest reference sites and (iv) calculating an observed/expected (O/E) analogous to RIVPACS/AUSRIVAS. 3. The paper compares AUSRIVAS and ANNA models on 17 datasets representing a variety of habitats and seasons. First, it examines each model's regressions for Observed versus Expected number of taxa, including the r2, intercept and slope. Second, the two models' assessments of 79 test sites in New Zealand are compared. Third, the models are compared on test and presumed reference sites along a known trace metal gradient. Fourth, ANNA models are evaluated for western Australia, a geographically distinct region of Australia. The comparisons demonstrate that ANNA and AUSRIVAS are generally equivalent in performance, although ANNA turns out to be potentially more robust for the O versus E regressions and is potentially more accurate on the trace metal gradient sites. 4. The ANNA method is recommended for use in bioassessment of rivers, at least for corroborating the results of the well established AUSRIVAS- and RIVPACS-type models, if not to replace them. [source] Combining spatial and phylogenetic eigenvector filtering in trait analysisGLOBAL ECOLOGY, Issue 6 2009Ingolf Kühn ABSTRACT Aim, To analyse the effects of simultaneously using spatial and phylogenetic information in removing spatial autocorrelation of residuals within a multiple regression framework of trait analysis. Location, Switzerland, Europe. Methods, We used an eigenvector filtering approach to analyse the relationship between spatial distribution of a trait (flowering phenology) and environmental covariates in a multiple regression framework. Eigenvector filters were calculated from ordinations of distance matrices. Distance matrices were either based on pure spatial information, pure phylogenetic information or spatially structured phylogenetic information. In the multiple regression, those filters were selected which best reduced Moran's I coefficient of residual autocorrelation. These were added as covariates to a regression model of environmental variables explaining trait distribution. Results, The simultaneous provision of spatial and phylogenetic information was effectively able to remove residual autocorrelation in the analysis. Adding phylogenetic information was superior to adding purely spatial information. Applying filters showed altered results, i.e. different environmental predictors were seen to be significant. Nevertheless, mean annual temperature and calcareous substrate remained the most important predictors to explain the onset of flowering in Switzerland; namely, the warmer the temperature and the more calcareous the substrate, the earlier the onset of flowering. A sequential approach, i.e. first removing the phylogenetic signal from traits and then applying a spatial analysis, did not provide more information or yield less autocorrelation than simple or purely spatial models. Main conclusions, The combination of spatial and spatio-phylogenetic information is recommended in the analysis of trait distribution data in a multiple regression framework. This approach is an efficient means for reducing residual autocorrelation and for testing the robustness of results, including the indication of incomplete parameterizations, and can facilitate ecological interpretation. [source] Richness patterns, species distributions and the principle of extreme deconstructionGLOBAL ECOLOGY, Issue 2 2009Levi Carina Terribile ABSTRACT Aim, To analyse the global patterns in species richness of Viperidae snakes through the deconstruction of richness into sets of species according to their distribution models, range size, body size and phylogenetic structure, and to test if environmental drivers explaining the geographical ranges of species are similar to those explaining richness patterns, something we called the extreme deconstruction principle. Location, Global. Methods, We generated a global dataset of 228 terrestrial viperid snakes, which included geographical ranges (mapped at 1° resolution, for a grid with 7331 cells world-wide), body sizes and phylogenetic relationships among species. We used logistic regression (generalized linear model; GLM) to model species geographical ranges with five environmental predictors. Sets of species richness were also generated for large and small-bodied species, for basal and derived species and for four classes of geographical range sizes. Richness patterns were also modelled against the five environmental variables through standard ordinary least squares (OLS) multiple regressions. These subsets are replications to test if environmental factors driving species geographical ranges can be directly associated with those explaining richness patterns. Results, Around 48% of the total variance in viperid richness was explained by the environmental model, but richness sets revealed different patterns across the world. The similarity between OLS coefficients and the primacy of variables across species geographical range GLMs was equal to 0.645 when analysing all viperid snakes. Thus, in general, when an environmental predictor it is important to model species geographical ranges, this predictor is also important when modelling richness, so that the extreme deconstruction principle holds. However, replicating this correlation using subsets of species within different categories in body size, range size and phylogenetic structure gave more variable results, with correlations between GLM and OLS coefficients varying from ,0.46 up to 0.83. Despite this, there is a relatively high correspondence (r = 0.73) between the similarity of GLM-OLS coefficients and R2 values of richness models, indicating that when richness is well explained by the environment, the relative importance of environmental drivers is similar in the richness OLS and its corresponding set of GLMs. Main conclusions, The deconstruction of species richness based on macroecological traits revealed that, at least for range size and phylogenetic level, the causes underlying patterns in viperid richness differ for the various sets of species. On the other hand, our analyses of extreme deconstruction using GLM for species geographical range support the idea that, if environmental drivers determine the geographical distribution of species by establishing niche boundaries, it is expected, at least in theory, that the overlap among ranges (i.e. richness) will reveal similar effects of these environmental drivers. Richness patterns may be indeed viewed as macroecological consequences of population-level processes acting on species geographical ranges. [source] Predictors of representational aggression in preschool children of low-income urban African American adolescent mothers,INFANT MENTAL HEALTH JOURNAL, Issue 1 2010Geoff Goodman Responses to five doll-story stems thematically related to attachment experiences with the mother were videotaped in the home and used to evaluate child, maternal, and environmental predictors of representational aggression in 93 preschool children of African American women receiving public assistance who had become pregnant as teenagers. Significant correlations were found between representational aggression and child's gender (male), birth weight, maternal depressive affect, maternal educational attainment, recent employment, mother's historical residence with her own mother, and felt social support, accounting for 40% of the variance in representational aggression. A significant Felt Social Support × Gender interaction effect suggested that girls of mothers who perceive higher levels of felt social support are more likely to represent less aggression in their stories; felt social support was not associated with boys' representational aggression. A significant Felt Social Support × Employment interaction effect suggested that representational aggression is associated with lower levels of felt social support only among employed mothers. Findings suggest that different pathways exist for representational aggression in children of low-income adolescent mothers, which nevertheless share predictors associated with poverty. [source] Are niche-based species distribution models transferable in space?JOURNAL OF BIOGEOGRAPHY, Issue 10 2006Christophe F. Randin Abstract Aim, To assess the geographical transferability of niche-based species distribution models fitted with two modelling techniques. Location, Two distinct geographical study areas in Switzerland and Austria, in the subalpine and alpine belts. Methods, Generalized linear and generalized additive models (GLM and GAM) with a binomial probability distribution and a logit link were fitted for 54 plant species, based on topoclimatic predictor variables. These models were then evaluated quantitatively and used for spatially explicit predictions within (internal evaluation and prediction) and between (external evaluation and prediction) the two regions. Comparisons of evaluations and spatial predictions between regions and models were conducted in order to test if species and methods meet the criteria of full transferability. By full transferability, we mean that: (1) the internal evaluation of models fitted in region A and B must be similar; (2) a model fitted in region A must at least retain a comparable external evaluation when projected into region B, and vice-versa; and (3) internal and external spatial predictions have to match within both regions. Results, The measures of model fit are, on average, 24% higher for GAMs than for GLMs in both regions. However, the differences between internal and external evaluations (AUC coefficient) are also higher for GAMs than for GLMs (a difference of 30% for models fitted in Switzerland and 54% for models fitted in Austria). Transferability, as measured with the AUC evaluation, fails for 68% of the species in Switzerland and 55% in Austria for GLMs (respectively for 67% and 53% of the species for GAMs). For both GAMs and GLMs, the agreement between internal and external predictions is rather weak on average (Kulczynski's coefficient in the range 0.3,0.4), but varies widely among individual species. The dominant pattern is an asymmetrical transferability between the two study regions (a mean decrease of 20% for the AUC coefficient when the models are transferred from Switzerland and 13% when they are transferred from Austria). Main conclusions, The large inter-specific variability observed among the 54 study species underlines the need to consider more than a few species to test properly the transferability of species distribution models. The pronounced asymmetry in transferability between the two study regions may be due to peculiarities of these regions, such as differences in the ranges of environmental predictors or the varied impact of land-use history, or to species-specific reasons like differential phenotypic plasticity, existence of ecotypes or varied dependence on biotic interactions that are not properly incorporated into niche-based models. The lower variation between internal and external evaluation of GLMs compared to GAMs further suggests that overfitting may reduce transferability. Overall, a limited geographical transferability calls for caution when projecting niche-based models for assessing the fate of species in future environments. [source] Mesoscale distribution patterns of Amazonian understorey herbs in relation to topography, soil and watershedsJOURNAL OF ECOLOGY, Issue 5 2005FLÁVIA R. C. COSTA Summary 1Many authors have suggested that topography and soils are the major determinants of species distributions and community patterns at small or regional scales, but few studies addressed the patterns at mesoscales. We used Reserva Ducke (100 km2) as a model to analyse the effects of soil, topography and watersheds on the variation of the herb community composition, and to determine the relative importance of the environmental factors on species composition. 2Taxonomic groups are frequently used as surrogates in studies of biodiversity distribution and complementarity, but their efficacy is controversial. We therefore studied the correlations between the distributional patterns of three different herb groups (Marantaceae, pteridophytes and ,others') and their responses to environmental predictors. 3Terrestrial herbs were sampled in 59 plots of 250 × 2 m, systematically distributed over the reserve. Plots followed isoclines of altitude, to minimize the internal variation of soil. Composition of the total herb community and of the three herb groups was summarized with PCoA. 4Soil structure, represented by PCA axes, was the main determinant of the variation in herb composition for all groups, but slope affected only pteridophytes. Soil and topography explained less than one-third of the variance in community data. Herb composition was significantly different between watersheds, but watersheds differ only slightly in soil parameters. Our results indicate high turnover in species composition, on spatial scales of 5,10 km in central Amazonia, which is not necessarily associated with soil change. 5Compositional patterns of the three groups analysed were significantly correlated, but with low values for the correlation coefficient. Although composition was correlated, the responses to environmental predictors differed among groups, and the use of one group as a surrogate will miss around 50% of the variation in other groups. 6Although important, soil and topography alone cannot predict herb community structure. Knowledge of geographical, historical or other landscape features, such as watershed morphology, may therefore be necessary to predict the turnover patterns over mesoscales. Moreover, the same factors may not have the same effectiveness as predictors of the structure of seemingly similar biological groups. [source] Landscape features and crustacean prey as predictors of the Southern river otter distribution in Chile.ANIMAL CONSERVATION, Issue 6 2009M. A. Sepúlveda Abstract Understanding the processes that affect freshwater ecosystems at the watershed level is fundamental for the conservation and management of river otters. During 2 consecutive years, we surveyed the occurrence of the Southern river otter Lontra provocax and its main prey (crustaceans) in a watershed of 9900 km2 in the Chilean temperate forest. We modeled predator and prey distributions with a variety of statistical techniques by relating a set of environmental predictors to species occurrence records. Otter and crustaceans were associated with areas of intermediate to low human disturbance with a mosaic of riparian vegetation densities, mainly at low altitudes. The singularity of the Andean Range, with a very marked elevation gradient and oligotrophic watercourses in the higher areas, created more vulnerable conditions for otter presence because prey abundances were limited in those areas. Human impacts affected otter populations at a landscape scale through the presence of main roads, as these were mostly located in lower parts of the watershed where otters have their primary habitat. These results point to the importance of land management and protection of low-elevation areas where otters still occur to ensure the long-term viability of its freshwater populations. [source] | ||||||||||||||||