Data_Sheet_3_Individual species and site dynamics are the main drivers of spatial scaling of stability in aquatic communities.CSV

IntroductionAny measure of ecological stability scales with the spatial and temporal extent of the data on which it is based. The magnitude of stabilization effects at increasing spatial scale is determined by the degree of synchrony between local and regional species populations.MethodsWe applied two recently developed approaches to quantify these stabilizing effects to time series records from three aquatic monitoring data sets differing in environmental context and organism type.Results and DiscussionWe found that the amount and general patterns of stabilization with increasing spatial scale only varied slightly across the investigated species groups and systems. In all three data sets, the relative contribution of stabilizing effects via asynchronous dynamics across space was higher than compensatory dynamics due to differences in biomass fluctuations across species and populations. When relating the stabilizing effects of individual species and sites to species and site-specific characteristics as well as community composition and aspects of spatial biomass distribution patterns, however, we found that the effects of single species and sites showed large differences and were highly context dependent, i.e., dominant species can but did not necessarily have highly stabilizing or destabilizing effects on overall community biomass. The sign and magnitude of individual contributions depended on community structure and the spatial distribution of biomass and species in space. Our study therefore provides new insights into the mechanistic understanding of ecological stability patterns across scales in natural species communities.</p

Data_Sheet_1_Individual species and site dynamics are the main drivers of spatial scaling of stability in aquatic communities.CSV

IntroductionAny measure of ecological stability scales with the spatial and temporal extent of the data on which it is based. The magnitude of stabilization effects at increasing spatial scale is determined by the degree of synchrony between local and regional species populations.MethodsWe applied two recently developed approaches to quantify these stabilizing effects to time series records from three aquatic monitoring data sets differing in environmental context and organism type.Results and DiscussionWe found that the amount and general patterns of stabilization with increasing spatial scale only varied slightly across the investigated species groups and systems. In all three data sets, the relative contribution of stabilizing effects via asynchronous dynamics across space was higher than compensatory dynamics due to differences in biomass fluctuations across species and populations. When relating the stabilizing effects of individual species and sites to species and site-specific characteristics as well as community composition and aspects of spatial biomass distribution patterns, however, we found that the effects of single species and sites showed large differences and were highly context dependent, i.e., dominant species can but did not necessarily have highly stabilizing or destabilizing effects on overall community biomass. The sign and magnitude of individual contributions depended on community structure and the spatial distribution of biomass and species in space. Our study therefore provides new insights into the mechanistic understanding of ecological stability patterns across scales in natural species communities.</p

Data_Sheet_5_Individual species and site dynamics are the main drivers of spatial scaling of stability in aquatic communities.docx

IntroductionAny measure of ecological stability scales with the spatial and temporal extent of the data on which it is based. The magnitude of stabilization effects at increasing spatial scale is determined by the degree of synchrony between local and regional species populations.MethodsWe applied two recently developed approaches to quantify these stabilizing effects to time series records from three aquatic monitoring data sets differing in environmental context and organism type.Results and DiscussionWe found that the amount and general patterns of stabilization with increasing spatial scale only varied slightly across the investigated species groups and systems. In all three data sets, the relative contribution of stabilizing effects via asynchronous dynamics across space was higher than compensatory dynamics due to differences in biomass fluctuations across species and populations. When relating the stabilizing effects of individual species and sites to species and site-specific characteristics as well as community composition and aspects of spatial biomass distribution patterns, however, we found that the effects of single species and sites showed large differences and were highly context dependent, i.e., dominant species can but did not necessarily have highly stabilizing or destabilizing effects on overall community biomass. The sign and magnitude of individual contributions depended on community structure and the spatial distribution of biomass and species in space. Our study therefore provides new insights into the mechanistic understanding of ecological stability patterns across scales in natural species communities.</p

Data_Sheet_4_Individual species and site dynamics are the main drivers of spatial scaling of stability in aquatic communities.docx

IntroductionAny measure of ecological stability scales with the spatial and temporal extent of the data on which it is based. The magnitude of stabilization effects at increasing spatial scale is determined by the degree of synchrony between local and regional species populations.MethodsWe applied two recently developed approaches to quantify these stabilizing effects to time series records from three aquatic monitoring data sets differing in environmental context and organism type.Results and DiscussionWe found that the amount and general patterns of stabilization with increasing spatial scale only varied slightly across the investigated species groups and systems. In all three data sets, the relative contribution of stabilizing effects via asynchronous dynamics across space was higher than compensatory dynamics due to differences in biomass fluctuations across species and populations. When relating the stabilizing effects of individual species and sites to species and site-specific characteristics as well as community composition and aspects of spatial biomass distribution patterns, however, we found that the effects of single species and sites showed large differences and were highly context dependent, i.e., dominant species can but did not necessarily have highly stabilizing or destabilizing effects on overall community biomass. The sign and magnitude of individual contributions depended on community structure and the spatial distribution of biomass and species in space. Our study therefore provides new insights into the mechanistic understanding of ecological stability patterns across scales in natural species communities.</p

Data_Sheet_2_Individual species and site dynamics are the main drivers of spatial scaling of stability in aquatic communities.CSV

IntroductionAny measure of ecological stability scales with the spatial and temporal extent of the data on which it is based. The magnitude of stabilization effects at increasing spatial scale is determined by the degree of synchrony between local and regional species populations.MethodsWe applied two recently developed approaches to quantify these stabilizing effects to time series records from three aquatic monitoring data sets differing in environmental context and organism type.Results and DiscussionWe found that the amount and general patterns of stabilization with increasing spatial scale only varied slightly across the investigated species groups and systems. In all three data sets, the relative contribution of stabilizing effects via asynchronous dynamics across space was higher than compensatory dynamics due to differences in biomass fluctuations across species and populations. When relating the stabilizing effects of individual species and sites to species and site-specific characteristics as well as community composition and aspects of spatial biomass distribution patterns, however, we found that the effects of single species and sites showed large differences and were highly context dependent, i.e., dominant species can but did not necessarily have highly stabilizing or destabilizing effects on overall community biomass. The sign and magnitude of individual contributions depended on community structure and the spatial distribution of biomass and species in space. Our study therefore provides new insights into the mechanistic understanding of ecological stability patterns across scales in natural species communities.</p

Taxa counts and observer agreements.

<p>The taxa with their human and gold standard label amounts. Gold standard labels are computed as the centroid of a group of closely neighbouring human labels of the same taxon. Only groups with 3 human labels were taken into account. The background labels were randomly distributed and were all used as gold labels. Additionally, the inter- and intra-observer agreements are given by average and standard deviation pone.0038179.g001.tif(std-dev.) for the five experts.</p

The combination of human labels to gold standard labels.

<p>The left image shows a small white sea anemone with two human labels (as circles) which is not enough to create a gold standard label as a supporter count of was required (see text for details). The image in the middle shows a <i>Kolga hyalina</i> labeled by experts and its resulting gold standard label in between (as a cross). The right image shows a <i>Bathycrinus carpenterii</i> with human labels for the crown (blue) as well as the stalk (yellow). Both human label cliques have supporter and thus two gold standard labels are created.</p

The complete (semi-)automated detection process.

<p>Different transects with several thousand images are stored in the BIIGLE online platform (top left). These images can be accessed by experts via the WWW (bottom left). For this experiment, a subset of one transect (marked green on the upper left) was shown to five experts to create a manually labelled training set for a group of pre-defined taxa. Those manual labels were at first used to optimize an image pre-processing for illumination correction (top middle). Afterwards, high dimensional feature vectors were extracted at the label positions to gain a training and test set for SVM optimization (bottom middle). The trained SVMs were then applied pixel-wise to the full field of view, to obtain a confidence value for each pixel and taxon (top right). These confidence values were then post-processed into a classification map, where each pixel is assigned to one taxon which allows taxon counts per image. These taxon counts can then be plotted along the length of the transect (bottom right).</p

Illustration of the pre-processing.

<p>Image A is an original sample taken from the HAUSGARTEN IV transect. B - F show the effect of different kernel sizes <i>M</i> for the Gaussian filter. The kernel sizes are as follows: B: <i>M</i> = 11, C: <i>M</i> = 101, D: <i>M</i> = 701, E: <i>M</i> = 1101, F: <i>M</i> = 1401). The curves show the output of the cluster-indices, plotted against <i>M</i>. The first value (<i>M</i> = 0) represents the unfiltered image. The curves are as follows: blue: Chalinski-Harabasz, green: Index-I, yellow: Davies-Boudlin, pink: intra-cluster variance, red: inter-cluster variance. The bold, black line is the mean of the five measures. The cluster indices were normalized to the interval [0.1] and show a good correlation, supporting a reasonable selection of the value <i>M</i> = 701.</p

Classification performance by supporter.

<p>Given are the Sensitivity (SE) and Positive Predictive Value (PPV) for all taxa, compared to different supporter values <i>k</i> for the gold standard. While the SE is increasing with increasing supporter value, the PPV is performing inversely. This shows, that the automated detection is more likely to find objects with a high observer agreement.</p