Temporal and spatial variation in the Nazaré Canyon (Western Iberian margin): Inter-annual and canyon heterogeneity effects on meiofauna biomass and diversity

The Nazaré Canyon on the Portuguese Margin (NE Atlantic) was sampled during spring-summer for three consecutive years (2005–2007), permitting the first inter-annual study of the meiofaunal communities at the Iberian Margin at two abyssal depths (~3500 m and ~4400 m). Using new and already published data, the meiofauna standing stocks (abundance and biomass) and nematode structural and functional diversity were investigated in relation to the sediment biogeochemistry (e.g. organic carbon, nitrogen, chlorophyll a, phaeopigments) and grain size. A conspicuous increase in sand content from 2005 to 2006 and decrease of phytodetritus at both sites, suggested the occurrence of one or more physical disturbance events. Nematode standing stocks and trophic diversity decreased after these events, seemingly followed by a recovery/recolonisation period in 2007, which was strongly correlated with an increase in the quantity and bioavailability of phytodetrital organic matter supplied. Changes in meiofauna assemblages, however, also differed between stations, likely because of the contrasting hydrodynamic and food supply conditions. Higher meiofauna and nematode abundances, biomass and trophic complexity were found at the shallowest canyon station, where the quantity, quality and bioavailability of food material were higher than at the deeper site. The present results suggest that even though inter-annual variations in the sedimentary environment can regulate the meiofauna in the abyssal Nazaré Canyon, heterogeneity between sampling locations in the canyon were more pronounced.

Scale-dependent foraging ecology of a marine top predator modelled using passive acoustic data

1.Understanding which environmental factors drive foraging preferences is critical for the development of effective management measures, but resource use patterns may emerge from processes that occur at different spatial and temporal scales. Direct observations of foraging are also especially challenging in marine predators, but passive acoustic techniques provide opportunities to study the behaviour of echolocating species over a range of scales. 2.We used an extensive passive acoustic data set to investigate the distribution and temporal dynamics of foraging in bottlenose dolphins using the Moray Firth (Scotland, UK). Echolocation buzzes were identified with a mixture model of detected echolocation inter-click intervals and used as a proxy of foraging activity. A robust modelling approach accounting for autocorrelation in the data was then used to evaluate which environmental factors were associated with the observed dynamics at two different spatial and temporal scales. 3.At a broad scale, foraging varied seasonally and was also affected by seabed slope and shelf-sea fronts. At a finer scale, we identified variation in seasonal use and local interactions with tidal processes. Foraging was best predicted at a daily scale, accounting for site specificity in the shape of the estimated relationships. 4.This study demonstrates how passive acoustic data can be used to understand foraging ecology in echolocating species and provides a robust analytical procedure for describing spatio-temporal patterns. Associations between foraging and environmental characteristics varied according to spatial and temporal scale, highlighting the need for a multi-scale approach. Our results indicate that dolphins respond to coarser scale temporal dynamics, but have a detailed understanding of finer-scale spatial distribution of resources.

Spatial Variability of Durum Wheat Yield as Related to Soil Parameters in an Organic Field

The yield in organic farming is generally much lower than its potential, which is due to its specificity. The objective of the present study was to quantify the yield spatial variation of wheat and relate it to soil parameters in an organic farm located in the north of the Negev Desert. Soil samples were gathered in a triangular grid at three time intervals. Yields were measured at 73 georeferenced points before the actual harvest. Several thematic maps of soil and yield parameters were produced using geographic information system and geostatistical methods. The strongest spatial correlation was found in the weight of 1000 grains and the weakest was in carbon flow. Temporal relationships were found between soil nitrate concentration, soil water content, and leaf area index. Wheat yield varied from 1.11 to 2.84 Mg ha(-1) and this remarkable variation indicates that the spatial analysis of soil and yield parameters is significant in organic agriculture.

The Importance of Regional, System-Wide and Local Spatial Scales in Structuring Temperate Estuarine Fish Communities

An extensive literature base worldwide demonstrates how spatial differences in estuarine fish assemblages are related to those in the environment at (bio)regional, estuary-wide or local (within-estuary) scales. Few studies, however, have examined all three scales, and those including more than one have often focused at the level of individual environmental variables rather than scales as a whole. This study has identified those spatial scales of environmental differences, across regional, estuary-wide and local levels, that are most important in structuring ichthyofaunal composition throughout south-western Australian estuaries. It is the first to adopt this approach for temperate microtidal waters. To achieve this, we have employed a novel approach to the BIOENV routine in PRIMER v6 and a modified global BEST test in an alpha version of PRIMER v7. A combination of all three scales best matched the pattern of ichthyofaunal differences across the study area (rho = 0.59; P = 0.001), with estuary-wide and regional scales accounting for about twice the variability of local scales. A shade plot analysis showed these broader-scale ichthyofaunal differences were driven by a greater diversity of marine and estuarine species in the permanently-open west coast estuaries and higher numbers of several small estuarine species in the periodically-open south coast estuaries. When interaction effects were explored, strong but contrasting influences of local environmental scales were revealed within each region and estuary type. A quantitative decision tree for predicting the fish fauna at any nearshore estuarine site in south-western Australia has also been produced. The estuarine management implications of the above findings are highlighted.

Spatio-temporal Bayesian network models with latent variables for revealing trophic dynamics and functional networks in fisheries ecology

Ecosystems consist of complex dynamic interactions among species and the environment, the understanding of which has implications for predicting the environmental response to changes in climate and biodiversity. However, with the recent adoption of more explorative tools, like Bayesian networks, in predictive ecology, few assumptions can be made about the data and complex, spatially varying interactions can be recovered from collected field data. In this study, we compare Bayesian network modelling approaches accounting for latent effects to reveal species dynamics for 7 geographically and temporally varied areas within the North Sea. We also apply structure learning techniques to identify functional relationships such as prey–predator between trophic groups of species that vary across space and time. We examine if the use of a general hidden variable can reflect overall changes in the trophic dynamics of each spatial system and whether the inclusion of a specific hidden variable can model unmeasured group of species. The general hidden variable appears to capture changes in the variance of different groups of species biomass. Models that include both general and specific hidden variables resulted in identifying similarity with the underlying food web dynamics and modelling spatial unmeasured effect. We predict the biomass of the trophic groups and find that predictive accuracy varies with the models' features and across the different spatial areas thus proposing a model that allows for spatial autocorrelation and two hidden variables. Our proposed model was able to produce novel insights on this ecosystem's dynamics and ecological interactions mainly because we account for the heterogeneous nature of the driving factors within each area and their changes over time. Our findings demonstrate that accounting for additional sources of variation, by combining structure learning from data and experts' knowledge in the model architecture, has the potential for gaining deeper insights into the structure and stability of ecosystems. Finally, we were able to discover meaningful functional networks that were spatially and temporally differentiated with the particular mechanisms varying from trophic associations through interactions with climate and commercial fisheries.

The effects of dispersal mode on the spatial distribution patterns of intertidal molluscs.

1. As many species of marine benthic invertebrates have a limited capacity for movement as adults, dispersal mode is often considered as a determinant of geographical ranges, genetic structure and evolutionary history. Species that reproduce without a larval stage can only disperse by floating or rafting. It is proposed that the colonization processes associated with such direct developing species result in spatial distributions that show relatively greater fine scale patchiness than the distributions of species with a larval dispersal stage. This hypothesis was tested by collecting molluscs at different spatial scales in the Isle of Man. 2. Spatial distribution patterns supported the predictions based on dispersal mode. Estimated variance components for species with larval dispersal suggested that the majority of the spatial variation was associated with variation between shores. In comparison, there was relatively more variability within shores for abundance counts of species with direct development. 3. Multivariate analyses reflected the univariate results. An assemblage of direct developers provided a better discrimination between sites (100 m separation) but the group of species with larval dispersal gave a clearer separation of shores (separated by several km). 4. The fine scale spatial structure of direct developing species was reflected in higher average species diversity within quadrats. Species richness also reflected dispersal mode, with a higher fraction of the regional species pool present for direct developers in comparison to species with larval dispersal. This may reflect the improved local persistence of taxa that avoid the larval dispersal stage.

Spatial genetic structuring in a vagile species, the European wood mouse

We examined the genetic structure of natural populations of the European wood mouse Apodemus sylvaticus at the microgeographic ( 30 km) scales. Ecological and behavioural studies indicate that this species exhibits considerable dispersal relative to its home-range size. Thus, there is potential for high gene flow over larger geographic areas. As levels of population genetic structure are related to gene flow, we hypothesized that population genetic structuring at the microgeographic level should be negligible, increasing only with geographic distance. To test this, four sites were sampled within a microgeographic scale with two additional samples at the macrogeographic level. Individuals (n=415) were screened and analysed for seven polymorphic microsatellite loci. Contrary to our hypothesis, significant levels of population structuring were detected at both scales. Comparing genetic differentiation with geographic distance suggests increasing genetic isolation with distance. However, this distance effect was non-significant being confounded by surprisingly high levels of differentiation among microgeographic samples. We attribute this pattern of genetic differentiation to the effect of habitat fragmentation, splitting large populations into components with small effective population sizes resulting in enhanced genetic drift. Our results indicate that it is incorrect to assume genetic homogeneity among populations even where there is no evidence of physical barriers and dispersal can occur freely. In the case of A. sylvaticus, it is not clear whether dispersal does not occur across habitat barriers or behavioural dispersal occurs without consequent gene flow.

The spatial distribution of Holocene cryptotephras in north-west Europe since 7 ka: implications for understanding ash fall events from Icelandic eruptions

We present distribution maps for all cryptotephras (distal volcanic ash layers) younger than 7 ka that have been reported from three or more lakes or peatlands in north-west Europe. All but one of the tephras originates from Iceland; the exception has been attributed to Jan Mayen. We find strong spatial patterning in tephra occurrence at the landscape scale; most, but not all of the tephra occurrences are significantly spatially clustered, which likely reflects atmospheric and weather patterns at the time of the eruptions. Contrary to expectations based on atmospheric modelling studies, tephras appear to be at least as abundant in Ireland and northern Scotland as in Scandinavia. Rhyolitic and other felsic tephras occur in lakes and peatlands throughout the study region, but andesitic and basaltic tephras are largely restricted to lake sites in the Faroe Islands and Ireland. Explanations of some of these patterns will require further research on the effects of different methodologies for locating and characterizing cryptotephras. These new maps will help to guide future investigations in tephrochronology and volcanic hazard analysis.

Red-shifts and red herrings in geographical ecology

I draw attention to the need for ecologists to take spatial structure into account more seriously in hypothesis testing. If spatial autocorrelation is ignored, as it usually is, then analyses of ecological patterns in terms of environmental factors can produce very misleading results. This is demonstrated using synthetic but realistic spatial patterns with known spatial properties which are subjected to classical correlation and multiple regression analyses. Correlation between an autocorrelated response variable and each of a set of explanatory variables is strongly biased in favour of those explanatory variables that are highly autocorrelated - the expected magnitude of the correlation coefficient increases with autocorrelation even if the spatial patterns are completely independent. Similarly, multiple regression analysis finds highly autocorrelated explanatory variables "significant" much more frequently than it should. The chances of mistakenly identifying a "significant" slope across an autocorrelated pattern is very high if classical regression is used. Consequently, under these circumstances strongly autocorrelated environmental factors reported in the literature as associated with ecological patterns may not actually be significant. It is likely that these factors wrongly described as important constitute a red-shifted subset of the set of potential explanations, and that more spatially discontinuous factors (those with bluer spectra) are actually relatively more important than their present status suggests. There is much that ecologists can do to improve on this situation. I discuss various approaches to the problem of spatial autocorrelation from the literature and present a randomisation test for the association of two spatial patterns which has advantages over currently available methods.

Hierarchical Bayesian models in ecology: Reconstructing species interaction networks from non-homogeneous species abundance data

The relationships among organisms and their surroundings can be of immense complexity. To describe and understand an ecosystem as a tangled bank, multiple ways of interaction and their effects have to be considered, such as predation, competition, mutualism and facilitation. Understanding the resulting interaction networks is a challenge in changing environments, e.g. to predict knock-on effects of invasive species and to understand how climate change impacts biodiversity. The elucidation of complex ecological systems with their interactions will benefit enormously from the development of new machine learning tools that aim to infer the structure of interaction networks from field data. In the present study, we propose a novel Bayesian regression and multiple changepoint model (BRAM) for reconstructing species interaction networks from observed species distributions. The model has been devised to allow robust inference in the presence of spatial autocorrelation and distributional heterogeneity. We have evaluated the model on simulated data that combines a trophic niche model with a stochastic population model on a 2-dimensional lattice, and we have compared the performance of our model with L1-penalized sparse regression (LASSO) and non-linear Bayesian networks with the BDe scoring scheme. In addition, we have applied our method to plant ground coverage data from the western shore of the Outer Hebrides with the objective to infer the ecological interactions. (C) 2012 Elsevier B.V. All rights reserved.