Search

Small and challenging to identify, bryophyte species are often under-represented in large-scale studies of vegetation patterns. Bryophyte occurrence is likely to be influenced by multiple processes and little is known about the relative importance of the different factors across diverse landscapes. Species–environment relationships of 15 common terrestrial moss species in native forests of South and Stewart islands, New Zealand, were examined. Models were developed for each species to test relationships between species distribution and 22 environmental predictors, using Generalised Regression Analysis and Spatial Prediction (GRASP). The New Zealand nationwide vegetation survey LUCAS, although showing several limitations, provided data to investigate factors that may influence bryophyte distribution along large gradients. Combined biotic (primarily total vegetation cover) and abiotic (mean annual temperature and rainfall) effects best contributed to distribution models of all but two moss species. Canopy tree species at a site were also important predictors in the spatial distribution of several individual moss species, possibly through altering the physical environment and resource availability, although both bryophytes and vascular plant species may be influenced independently by the same factors. The study highlights the importance of different factors influencing bryophyte distributions and identifies several factors controlling distributions that require further investigation.

Successful biodiversity management, including the selection and subsequent management of protected areas, depends in large measure on classifications showing land areas with similar ecosystem character. In contrast to widely used, qualitative land-classification techniques, we used a numerical classification of explicit spatial layers describing aspects of New Zealand's climate and landforms. We chose input variables for their strong functional links with major physiological processes of trees and high statistical correlations with geographic distributions of individual tree species as determined from previous studies. Higher-level divisions of the resulting classification were dominated by macroclimatic variation associated with change in both latitude and orographic protection provided by New Zealand's main mountain ranges, but variation in landform became more important at finer scales of classification. Classification units showed marked variation in the proportional extent of both indigenous vegetation cover and land set aside for conservation purposes. Indigenous ecosystem remnants of the highest priority for increased protection occurred in warm, lowland domains, particularly in drier environments, where both indigenous cover and protected areas are of minimal geographic extent. Such results underline the considerable potential of an environmental classification to provide a landscape context for systematic conservation management, particularly in environments where the natural ecosystem pattern has been severely modified by human activity.

Summary 1Ecologists have long recognized that plants occurring in areas of low rainfall or soil nutrients tend to have smaller leaves than those in more favourable regions. 2Working with a large data set (690 species at 47 sites spread widely through south-east Australia) for which this reduction has been described previously, we investigated the morphology of leaf size reduction, asking whether any patterns observed were consistent across evolutionary lineages or between environmental gradients. 3Leaf length, width and surface areas were measured; leaf traits such as pubescence or lobing were also scored qualitatively. There was no correlation between soil phosphorus and rainfall across sites. Further, there was no evidence that pubescence, lobing or other traits assessed served as alternatives to reduction of leaf size at the low ends of either environmental gradient. 4Leaf size reduction occurred through many combinations of change in leaf width and length, even within lineages. Thus consistent patterns in the method of leaf size reduction were not found, although broad similarities between rainfall and soil P gradients were apparent.

This paper is an introduction to a Special Issue on ‘regression models for spatial predictions’ published in Biodiversity and Conservation following an international workshop held in Switzerland in 2001 (http://leba.unige.ch/workshop). This introduction describes how the exponential growth in computing power has improved our ability to reach spatially explicit assessment of biodiversity and to develop cost-effective conservation management. New questions arising from these modern approaches are listed, while papers presenting examples of applications are briefly introduced.

The utility of explicit spatial predictions for biodiversity assessment is investigated with New Zealand fern flora. Distributions of 43 species were modelled from climatic and landform variables and predicted across New Zealand using generalised additive models (GAM). An original package of functions called generalised regression analysis and spatial prediction (GRASP) was developed to perform the analyses. On average, for the 43 models, the contributions of environmental variables indicate that mean annual temperature is the most important factor at this broad regional scale. Both annual solar radiation and its seasonality had higher correlations than temperature seasonality. Measures of water availability such as ratio of rainfall to potential evapotranspiration, air saturation deficit and soil water deficit presented significant contributions. Lithology was a better predictor than slope and drainage. These results are similar to those obtained from analyses of the distributions of New Zealand tree species and are consistent with the hypothesis that both tree and fern diversity are highest on sites conducive to high productivity. In order to identify hotspots of fern diversity, spatial predictions of individual species were summed up. The resulting map gave a very similar result to the direct prediction of their corresponding richness (number of species by plot out of 43 spp.). As a consequence, and where individual species models were not all available, the number of species within different species assemblages was directly modelled. Predicted richness hotspots of total species (out of 122 spp.), selected species (out of 43 and 21 spp.) and common species (out of 23 spp.) present very similar spatial patterns and are highly correlated. Richness of uncommon species (out of 39 spp.) was also accurately predicted, but presented a different spatial pattern. The number of rare species (out of 60 spp.) was not correctly modelled. Even though the lack of data for rare species clearly limits the application of this approach, fern community composition of more common species can be partially reconstructed from individual species predictions. This case study offers therefore a consistent approach not only for biodiversity hotspots identification, but also for setting targets to biodiversity assessment and restoration programs.