Spatial autocorrelation of assemblages of benthic invertebrates and its relationship to environmental factors in two upland rivers in southeastern Australia

The nature of spatial autocorrelation of biota may reveal much about underlying ecological and biological factors responsible for producing those patterns, especially dispersal processes (drift, adult flight, etc.). We report here on assemblage‐level autocorrelation in the benthic‐invertebrate assemblages (retained in sieves of 300 µm mesh) of riffles in two adjacent, relatively pristine rivers in southeastern Victoria, Australia (40‐km reaches of the Wellington and Wonnangatta Rivers). These are related to patterns of autocorrelation in physical and catchment conditions (‘environmental variables’) in the vicinity of the sampling points. Both the invertebrate assemblages and environmental variables were autocorrelated at small scales (= 8 km) in the Wellington River in one of the sampling years (1996). Dissimilarities of invertebrate assemblages were correlated with dissimilarities of environmental variables in both sampling years (1996 and 1997) in that river. Environmental variables were autocorrelated in the Wonnangatta River, but this was not expressed as autocorrelation in the assemblages of invertebrates, which were not autocorrelated at any scale studied. Individual environmental variables showed different spatial patterns between the two rivers. These results suggest that individual rivers have their own idiosyncratic patterns and one cannot assume that even similar, geographically adjacent rivers will have the same patterns, which is a difficulty for ecological assessment and restoration.     

Proportionate spatial sampling and equal‐time sampling of mobile animals: A dilemma for inferring areal dependence

Abstract Patch or island area is one of the most frequently used variables for inference in conservation biology and biogeography, and is often used in ecological applications. Given that all of these disciplines deal with large spatial scales, exhaustive censusing is not often possible, especially when there are large numbers of patches (e.g. for replication and control purposes). Therefore, data for patches or islands are usually collected by sampling. We argue that if area is to be used as an inferential factor, then the objects under study (i.e. the patches) must be characterized on an areal basis. This necessarily means that fixed‐area sampling is inadequate (e.g. a single standard quadrat or transect set within patches irrespective of the patch area) and that some form of area‐proportionate sampling is needed (e.g. a fixed areal proportion of each patch is surveyed by random allocation of standard quadrats across each patch). However, use of area‐proportionate sampling is not usually dissociated from the increased temporal intensity of sampling that arises from using this approach. The dilemma we see is deciding how much of the area‐specificity of variables such as species richness, rare‐species indices or probabilities of occurrence of individual species is related to the area‐proportionate survey protocol and how much is due to the temporal intensity of surveys. We undertook a study in which we balanced temporal and spatial effects by increasing the time spent surveying smaller patches of vegetation to account for the area‐ratio difference. The estimated species richness of birds of the box–ironbark system of central Victoria, Australia, was found to depend strongly upon area when area‐proportionate sampling alone was performed. When time‐balancing was imposed upon area‐proportionate sampling, the differences between smaller (10‐ha) and larger (40‐ha) areas were much reduced or effectively disappeared. We show that species found in the additional surveys used to conduct the time‐balancing were significantly less abundant than species recorded in area‐proportionate sampling. This effect is probably most severe for mobile animals, but may emerge in other forms of sampling.     

SUMMARY OF GREEN PLANT PHYLOGENY AND CLASSIFICATION

Abstract— A cladogram of green plants involving all major extant groups of green algae, bryophytes, pteridophytes, and seed plants is presented. It is partly based on contributions by B. Mishler and S. Churchill, H. Wagner, and P. Crane. The relationships of green plants to other green organisms ( Prochloron , euglenophytes) are discussed. The characters and subclades of the cladogram are briefly discussed, with an attempt to indicate weak points. The possibility of including some major extinct groups is considered. A cladistic classification consistent with the cladogram is presented. Grades are abandoned as taxa and major clades like the division Chlorophyta (green algae excluding micro-monadophytes and charophytes sensu Mattox and Stewart), the division Streptophyta (charophytes + embryophytes), the subdivision Embryophytina (land plants or embryophytes), the superclass Tracheidatae (tracheophytes), and the class Spermatopsida (seed plants) are recognized.     

Interactions among stressors may be weak: Implications for management of freshwater macroinvertebrate communities

Aim

Ecological models that do not account for interactions among stressors, if interactions are important, could be inaccurate and lead to inefficient conservation strategies. Conversely, if interactions are not important (i.e., stressors operate largely independently), then actions concentrating on a stressor‐by‐stressor basis would be warranted. Here, we investigated whether interactions among multiple stressors affected widely used indices of freshwater macroinvertebrate biodiversity, which are sensitive to environmental change at management‐relevant scales (i.e., reaches and catchments).

Location

State of Victoria, south‐eastern Australia.

Methods

We used a 7,418‐sample dataset for stream macroinvertebrates from 2,165 sites distributed over 237,630 km² for 20 years. We calculated the interactive effects on stream macroinvertebrates of stressors operating at different scales, namely vegetation loss at the catchment and reach scales and hydrological change and salinization at the local scale. The importance of interactions among multiple stressors was assessed by comparing the cross‐validated predictive performance of models with and without multiple stressor interaction terms.

Results

Cross‐validated models explained 31%–63% of the variation in the macroinvertebrate responses. The most important stressors were catchment vegetation loss (the proportion of remaining native vegetation cover) and salinity. The inclusion of interaction terms did not increase cross‐validated predictive performance, which indicates that there was little evidence that interactions among stressors were important for explaining variation in commonly used freshwater macroinvertebrate condition indices.

Main conclusions

Interactions among vegetation, salinity and hydrological change stressors may not always be of importance for determining patterns of stream macroinvertebrate biodiversity, so that such interactions may not necessarily be critical considerations for catchment and reach scale management, at least if based on these or comparable condition indices. The mitigation of the impacts of vegetation loss, salinization and hydrological change stressors one‐by‐one probably is sufficient to guide conservation activities and might be advantageous if socio‐political contexts make it difficult to address interactions among stressors.

     

Fish community changes after minimum flow increase: testing quantitative predictions in the Rhône River at Pierre-Bénite, France

1. Many aspects of the flow regime influence the structure of stream communities, among which the minimum discharge left in rivers has received particular attention. However, instream habitat models predicting the ecological impacts of discharge management often lack biological validation and spatial generality, particularly for large rivers with many fish species. 2. The minimum flow at Pierre‐Bénite, a reach of the Rhône river bypassed by artificial channels, was increased from 10 to 100 m³ s^?1 in August 2000 (natural mean discharge 1030 m³ s^?1), resulting in a fivefold increase in average velocity at minimum flow. Fish were electrofished in several habitat units on 12 surveys between 1995 and 2004. 3. Principal components analysis revealed a significant change in the relative abundance of fish species. The relative abundance of species preferring fast‐flowing and/or deep microhabitats increased from two‐ to fourfold after minimum flow increase. A change in community structure confirmed independent quantitative predictions of an instream habitat model. This change was significantly linked to minimum flow increase, but not to any other environmental variables describing high flows or temperature at key periods of fish life cycle. The rapidity of the fish response compared with the lifespan of individual species can be explained by a differential response of specific size classes. 4. The fish community at Pierre‐Bénite is in a transitional stage and only continued monitoring will indicate if the observed shift in community structure is perennial. We expect that our case study will be compared with other predictive tests of the impacts of flow restoration in large rivers, in the Rhône catchment and elsewhere.     

Bayesian clustering with AutoClass explicitly recognises uncertainties in landscape classification

Clustering of multivariate data is a commonly used technique in ecology, and many approaches to clustering are available. The results from a clustering algorithm are uncertain, but few clustering approaches explicitly acknowledge this uncertainty. One exception is Bayesian mixture modelling, which treats all results probabilistically, and allows comparison of multiple plausible classifications of the same data set. We used this method, implemented in the AutoClass program, to classify catchments (watersheds) in the Murray Darling Basin (MDB), Australia, based on their physiographic characteristics (e.g. slope, rainfall, lithology). The most likely classification found nine classes of catchments. Members of each class were aggregated geographically within the MDB. Rainfall and slope were the two most important variables that defined classes. The second-most likely classification was very similar to the first, but had one fewer class. Increasing the nominal uncertainty of continuous data resulted in a most likely classification with five classes, which were again aggregated geographically. Membership probabilities suggested that a small number of cases could be members of either of two classes. Such cases were located on the edges of groups of catchments that belonged to one class, with a group belonging to the second-most likely class adjacent. A comparison of the Bayesian approach to a distance-based deterministic method showed that the Bayesian mixture model produced solutions that were more spatially cohesive and intuitively appealing. The probabilistic presentation of results from the Bayesian classification allows richer interpretation, including decisions on how to treat cases that are intermediate between two or more classes, and whether to consider more than one classification. The explicit consideration and presentation of uncertainty makes this approach useful for ecological investigations, where both data and expectations are often highly uncertain.