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Abstract
The qualitative label ‘international journal’ is used widely, including in national research quality assessments. We determined the practicability of analysing internationality quantitatively using 39 conservation biology journals, providing a single numeric index (IIJ) based on 10 variables covering the countries represented in the journals’ editorial boards, authors and authors citing the journals’ papers. A numerical taxonomic analysis refined the interpretation, revealing six categories of journals reflecting distinct international emphases not apparent from simple inspection of the IIJs alone. Categories correlated significantly with journals’ citation impact (measured by the Hirsch index), with their rankings under the Australian Commonwealth’s ‘Excellence in Research for Australia’ and with some countries of publication, but not with listing by ISI Web of Science. The assessments do not reflect on quality, but may aid editors planning distinctive journal profiles, or authors seeking appropriate outlets.
Primack, R. B., 2010. Essentials of Conservation Biology, Fifth Edition
Sinauer Associates, Sunderland MA, xv+601 pp. ISBN: 978-0-87893-640-3; hardcover price: $86.95. Instructor’s resource CD-ROM (ISBN 978-0-87893-638-0). eBook format is also available (ISBN 978-0-87893-637-3).
Farmland ponds represent habitats with a high conservation value that make a significant contribution to regional biodiversity. Understanding the influence of plant species composition and environmental variables in driving variations in animal species composition in ponds is an important issue in the fields of ecological research and conservation biology. Using variance partitioning techniques to quantify independent effects, we examined how plant species composition, local-landscape configuration and physicochemical variables interact in influencing aquatic insect and amphibian community composition. The ponds investigated in this study were located in the Site of Community Importance — Special Protected Area (Natura 2000 Network) “Monte Labbro — Alta Valle dell’Albegna” (Tuscany, central Italy). Our results showed that: (i) plant community composition (such as Carex hirta, Glicerya fluitans, Potamogeton natans, Typha latifolia) is a good predictor for amphibian but not for aquatic insect species composition; (ii) aquatic insect species composition was more strongly affected by the landscape context, whereas for amphibians the local characteristics of the ponds were determining; (iii) the physicochemical context is a poor predictor for these animal taxa; (iv) lastly, and notably, the explanatory variables explained a high proportion of the total variation in amphibian and aquatic insect species composition. Our results have important implications with respect to the creation of new ponds, which should preferentially take place close to semi-natural grasslands and other wetlands, in order to maintain greater connectivity, and away from urban areas. Moreover, larger ponds are preferable for the preservation of pond biodiversity. The management and conservation of ponds is necessary to ensure the protection of habitats, the survival of individual species and overall pond biodiversity.
Even if the establishment of nature reserves is to date a reality and the increase of protected areas is going to grow year after year, monitoring programs aiming to assess the effectiveness of the established protected areas for biodiversity conservation are still needed. That is the case for the Natura 2000 network in Europe, for which monitoring methods and programs are not yet well-established. A probabilistic sampling procedure is proposed and tested for quantifying and monitoring plant species diversity within a local network of protected areas, namely the Natura 2000 network in the Siena Province, Italy. On the basis of a sampling strategy of one 100 m 2 plot randomly located in each 1 km × 1 km cell, four Sites of Community Importance (SCIs) were investigated in 2005. The gradients in species composition at the plot scale were largely related to elevation and forest cover. The species richness values of the four SCIs were compared by means of sample-based rarefaction curves. Then, additive partitioning of species richness was applied to determine the most important spatial components in determining the total species richness of the network. Compositional differences among the plots within each SCI were the most responsible of the total species richness. These methodologies can be adopted for assessing plant species richness within a large region or within a reserve network and, if combined with additive partitioning, they can be used as a set of large scale indicators of species diversity.
Alpine grasslands harbour species-rich communities of plants and invertebrates. We examined how environmental variables and anthropogenic impact shape species richness and community structure of terrestrial gastropods in alpine grasslands in the Val Müstair (Eastern Alps, Switzerland). Gastropods were sampled using a standardised method at 76 sites spanning an elevation range from 1430 m to 2770 m. A total of 4763 specimens representing 52 species were recorded. Correspondence analysis based on presence/absence data revealed that the grassland gastropod community was structured in a complex way with elevation, wetness, grazing intensity and inclination of the sites as key factors, while abundance-based analysis identified the importance of the elevation and wetness of sites. Generalized linear model showed that species richness decreased with increasing elevation and increased with increasing soil pH. The grassland gastropod communities were characterized by a high beta diversity, as indicated by the SDR-simplex analysis. Species-specific traits of gastropods showed sensitivity to the environmental characters of the sites, as shown by a fourth-corner analysis.
As conservation of sensitive habitats is a high priority issue in European environmental policy, there is considerable interest in mapping and monitoring specific habitats of high conservation value. In this study, we discuss the potential of the Swiss mire monitoring program to monitor small area habitats in sufficient detail. The monitoring scheme combines nationwide probability sampling and predictive habitat mapping based on a field data sample. Thus, it is designed to identify spatiotemporal changes at the stand level and to derive hard statistics for the sub-national level. For feasibility reasons, the thematic focus is on semi-quantitative mean indicator values derived from vegetation records. These measures provide robust estimates of essential floristic site conditions. Regression models based on CIR aerial photographs are applied to continuously map respective measures across the sample mires. The present study explores the required investment of data for model-based mapping. Exemplary mapping results are presented and validated within a reference mire. Repeated tests show that about one hundred field records are needed to guarantee optimal prediction accuracy and reliable error estimates for all target variables. The corresponding 95% error quantiles in a test data set are below 0.7. To evaluate the benefit of high resolution orthophotos (30 cm resolution), the model prediction is compared with results obtained from coarsened images. Although the original CIR images produce the best model performance, the models based on resolutions comparable to modern satellite images still show considerable potential to assess larger areas where the use of digital aerial photographs is limited. The resulting spatially-explicit in-depth information can resolve the common thematic limitations of stand-alone remote sensing applications in conservation monitoring. As the method is applicable consistently across a range of habitat types, we argue that it has the potential to become a standard method for operational monitoring of priority habitats in European nature conservation.
Agricultural management is a major driver of changes in floral and faunal species richness of anthropogenic landscapes. Counteracting the negative impact of industrialized agriculture by providing subsidies to farmers for environmentally friendly agricultural practices, agri-environmental schemes (AES) are the most important policy instruments to protect European biodiversity in agricultural landscapes. However, as they are rarely cost-effective, there is an urgent need for evaluation and improvement. To assess the environmental effects of the Austrian AES, we mapped landscapes and vascular plants in 1998 and 2003 and birds in 2003. The sampling areas were located in the three most important types of Austrian agricultural landscape, i.e., grassland in alpine valleys and basins, mixed agriculture in mountain areas, and eastern arable land. We investigated the agri-environmental measures (AEMs) in a parcel-wise manner and analyzed their effects on landscape values and biodiversity. Reduction of agrochemicals showed positive effects on biodiversity of vascular plants in grassland and birds in arable land. Targeted measures that directly address threatened species were most effective, but had much less coverage. Contradicting developments became apparent for landscape structure and ecological infrastructures, but effects of the AES were generally larger in simple than in complex landscapes. We conclude that AEMs are currently not targeted enough to effectively halt biodiversity losses, and recommend better regionalization by offering landscape-context specific measures, stronger focus on maintenance and improvement of landscape diversity, avoidance of counterproductive development, and improvement of the coverage of specific conservation measures.
With 22 taxa reported from the country so far, Epipactis is the most species-rich orchid genus in Hungary. Many of them are rare, threatened species. To protect endangered species effectively, it is crucial to explore their ecology. Our work aimed to select and examine factors that are influencing the distribution of Epipactis species. Our data collection (2014–2018) was carried out in the Keszthely Hills, in the northeastern part of the Zala Hills and the Southern Bakony Mountains. We assigned ecologically relevant data from databases of local forestries, terrain models and geological maps to each occurrence. We examined the factors that result in the best differentiation between the studied species. At 1,261 localities, a total of 5,223 individuals of 15 taxa were found. We found three factors (tree species composition of the forest, genetic soil type, bedrock type) that significantly influenced the distribution of Epipactis species. Our results can help understand the distribution patterns of these species and allow for more effective, targeted protection of their potential habitats on a regional level.
Beta diversity, species replacement and nestedness are often examined through pairwise comparisons of sites based on presence-absence data, and the relative importance of these ecological phenomena is evaluated by operations with dissimilarity coefficients. An example is the nestedness resultant dissimilarity (NRD) procedure recently proposed by Baselga (2010, Global Ecology andBiogeography 19: 134–143) to disentangle the nestedness fraction of beta diversity from species replacement. In our view, the component terms in this measure are not scaled uniformly and the nestedness fraction cannot be quantified properly without giving clear definitions for its measurement. We suggest to distinguish among three additive fractions of the species set of two sites: number of species shared (overlap), species replacement (=spatial turnover) and richness difference. Then, absolute beta diversity is obtained as a composite of the second two fractions (known as βWB), while nestedness is derived from the first and the third. To express beta diversity and nestedness in a relativized form, the respective sums are divided by the total number of species. These allow defining a new index to measure the fraction of beta diversity which is shared by nestedness as well, and is calculated as relativized richness difference with the condition that the two sites being compared have at least one species in common. It is called diversity-nestedness intersection coefficient (F). Baselga’s nestedness resultant dissimilarity and the diversity-nestedness intersection coefficient are compared graphically using artificial and actual examples. These functions follow a mathematical relationship for perfectly nested data, otherwise their results are divergent. Discrepancy increases when beta diversity is large, especially if richness differences override species replacement effects in shaping presence-absence data structures. An advantage of F is its compatibility with a general theoretical and methodological framework for revealing pattern in presence-absence data matrices.
We present a test involving a large number of data-analytical techniques to identify a rigorous numerical classification method optimising on statistically identified faithful species. The test follows a stepwise filtering process involving various numerical-classification tools. Five steps were involved in the testing: (1) evaluation of 322 classification tools using Optim-Class 1; (2) comparison of 20 best performing methods by standardising the various performances across a range of fidelity values using OptimClass 1 and OptimClass 2, to assess the effectiveness of the agglomerative clustering and one divisive technique; (3) calculation and comparison of Uniqueness values and ISAMIC (Indicator Species Analysis Minimising Intermediate Constancies) scores of the resulting classifications; (4) comparison of different classifications by analysing the similarities of the resulting synoptic tables using faithful species, assuming that clusters with similar faithful species represent corresponding vegetation types, and (5) final selection of the single best method based on an expert review of non-geometric internal evaluators, NMDS ordinations and mapped classification solutions. A complex data set, representing many forest vegetation types and consisting of 506 relevés of 20 m × 20 m sampled in the indigenous forests of Mpumalanga Province (South Africa), was tested. Analysis of Uniqueness provided insight into which methods produced classifications that did not share faithful species. The analysis of synoptic table similarity showed that the classification results were at most 88% similar, while in the most divergent case similarity of only 50% was achieved. OptimClass eliminated poorly performing numerical-classification combinations and highlighted the best performing methods. Yet it was unable to reveal the single best performing method unequivocally across the range of fidelity values used. In such cases, we suggest the solution can be sought in relying on involving external data through expert opinion. Ordinal Clustering and TWINSPAN produced the most outlying classification results. Flexible beta clustering (β= −0.25) in combination with Bray-Curtis coefficient, standardised by sample unit totals, produced the most informative result for our data set when using informal expert-defined ecological and biogeographical judgement criteria. We recommend that the performance of a set of methods be tested prior to selecting the final classification approach.
