Prediction of biodiversity - regression of lichen species richness on remote sensing data

The objective of the present study was to develop a model to predict lichen species richness for six test sites in the Swiss Pre-Alps following a gradient of land use intensity combining airborne remote sensing data and regression models. This study ties in with the European Union Project „BioAssess”, which aimed at quantifying patterns in biodiversity and developing „Biodiversity Assessment Tools” that can be used to rapidly assess biodiversity. For this study, lichen surveys were performed on a circular area of 1 ha in 96 sampling plots in the six test sites. Lichen relevés were made on three different substrates: trees, rocks and soil. In the first step, ecologically meaningful variables derived from airborne remote sensing data were calculated using two levels of detail. 1'st level variables were processed using both spatial and spectral information of the CIR orthoimages. 2'nd level variables - based on 1'st level variables - were implemented using additional lichen expert knowledge. In the second step, all variables were calculated for each sampling plot and correlated with the different lichen relevés. Multiple linear regression models were built, containing all extracted variables, and a stepwise variable selection was applied to optimize the final models. The predictive power of the models (correlation between predicted and measured diversity) in a reference data set can be regarded as good. The obtained R ranging from 0.48 for lichens on soil to 0.79 for lichens on trees can be regarded as satisfactory to good, respectively. The accuracy of models could be further improved by adapting the model and by using additional calibration data and sampling plots. Species richness for each pixel within the six test sites was then calculated. This ecological modeling approach also reveals two main restrictions: 1) this method only indicates the potential presence or absence of species, and 2) the models may only be useful for calculating species richness in neighboring regions with similar landscape structures

Combining remotely sensed spectral data and digital surface models for fine-scale modelling of mire ecosystems

The detection and evaluation of changes in vegetation patterns is a prerequisite for monitoring programs. The Swiss mire monitoring program aims to assess the changes in mire vegetation in order to examine the efficiency of the management measures. A promising way to explore and detect vegetation structure and vegetation change is the application of predictive vegetation mapping that combines image classification and predictive habitat distribution models. These models deal with predictor variables derived from remotely sensed spectral data and from environmental variables such as a digital surface model (DSM). Low accuracy of environmental data to predict vegetation at the local scale is due to the difficulties to capture dominant fine-scale enironmental gradients. Using high resolution spectral and topographical data sets of 50 cm pixel size and below, the study presented here aims to improve the simulation of local-scale vegetation properties. The spectral data for fine-scale modelling are based on CIR orthoimages with a ground resolution of 32 cm. Various spectral variables and spectral-textural variables were derived for the modelling process. A new method to reduce the number of predictor variables, the composite modelling is presented in this paper. In comparison to existing methods, composite modelling has the advantage of being independent of the scale of the predictor variables, and at the same time being transferable among various data sets. Mean indicator values for moisture, nutrients and light derived from vegetation data are used as response variables. Results show that the topographical variables based on relief features are less powerful predictors than the spectral variables but that combining them enhances the overall predictive power. Stratification of the data according to the tree layer and the shadow areas increases the accuracy of the model

Esfingídeos (Lepidoptera, Sphingidae) no Tabuleiro Paraibano, nordeste do Brasil: abundância, riqueza e relação com plantas esfingófilas Hawkmoths (Lepidoptera, Sphingidae) in the Tabuleiro Paraibano, northeastern Brazil: abundance, richness and relations to sphingophilous plants

<abstract language="eng">Hawkmoths (Sphingidae) are among the major pollinators in tropical communities. Here the first survey of sphingids and related plants in Northeastern Brazil is presented. The sphingids were surveyed from March 1999 through April 2000 at the Reserva Biológica Guaribas, Mamanguape, Paraíba State. On black and mix light traps, 136 hawkmoths from 24 species were captured. Individuais of Erinnyis ello (Linnaeus, 1758), Isognathus menechus (Boisduval, 1875) and Xylophanes tersa (Linnaeus, 1771) represented 58% of the samples. One half of the recorded species show wide distribution in the Neotropics. Three species were registered for more than six months. Most of the species were found only in the dry or wet season. Pollen from 34 plant species were recorded by pollen analysis of sphingid mouth parts. Hancornia speciosa (Apocynaceae) and Guettarda platipoda (Rubiaceae) were the most important food plants. Both are characteristic elements of the Tabuleiro Nordestino and present typically sphingophilous flowers. More than one half of the sphingids presented pollen from only one or two species of plants. By transporting large quantities of pollen of a few species, hawkmoths seem to be the main pollinators of nocturnal flowers in the Tabuleiro Paraibano