Taxonomy and Species Richness of Amazonian Pimplinae and Rhyssinae (hymenoptera: ichneumonidae)

The Amazonian region, the biggest rain forest of our planet, is known for its extraordinary biodiversity. Most of this diversity is still unexplored and new species of different taxa are regularly found there. In this region, as in most areas of the world, insects are some of the most abundant organisms. Therefore, studying this group is important to promote the conservation of these highly biodiverse ecosystems of the planet. Among insects, parasitoid wasps are especially interesting because they have potential for use as biodiversity indicators and biological control agents in agriculture and forestry. The parasitoid wasp family Ichneumonidae is one of the most species rich groups among the kingdom Animalia. This group is still poorly known in many areas of the world; the Amazonian region is a clear example of this situation. Ichneumonids have been thought to be species poor in Amazonia and other tropical areas. However, recent studies are suggesting that parasitoid wasps may be quite abundant in Amazonia and possibly in most tropical areas of the world. The aim of my doctoral thesis is to study the species richness and taxonomy of two of the best known ichneumonid subfamilies in the Neotropical region, Pimplinae and Rhyssinae. To do this I conducted two extensive sampling programs in the Peruvian Amazonia. I examined also a large number of Neotropical ichneumonids deposited to different natural history museums. According to the results of my thesis, the species richness of these parasitoids in the Amazonian region is considerably higher than previously reported. In my research, I firstly further develop the taxonomy of these parasitoids by describing many new species and reporting several new faunistic records (I, II, III). In this first part I focus on two genera (Xanthopimpla and Epirhyssa) which were thought to be rather species poor. My thesis demonstrates that these groups are actually rather species rich in the Amazonian region. Secondly, I concentrate on the species richness of these parasitoids in a global comparison showing that the Neotropical region and especially the Peruvian Amazonia is one of the most species rich areas of Pimpliformes ichneumonids (V). Furthermore, I demonstrate that with the data available to date no clear latitudinal gradient in species richness is visible. Thirdly, increasing the macroecological knowledge of these parasitoids I show that some previously unreported ichneumonid subfamilies are present in the Amazonian region (IV). These new insights and the results of the global comparison of ichneumonid inventories suggest that the previous belief of low diversity in the tropics is most likely related to a lack of sampling effort in the region. Overall, my research increases the knowledge of Neotropical ichneumonids highlighting the importance of Peruvian Amazonia as one of the diversity hotspots of parasitoid wasps.

Alankosademetsien kasvilajiston vaihtelun kartoittaminen satelliittikuvilta

Seloste väitöskirjasta: Remote sensing of floristic patterns in the lowland rain forest landscape. Dissertationes Forestales 59.

Forest cover and its change in Unguja Island, Zanzibar

Tropical forests are sources of many ecosystem services, but these forests are vanishing rapidly. The situation is severe in Sub-Saharan Africa and especially in Tanzania. The causes of change are multidimensional and strongly interdependent, and only understanding them comprehensively helps to change the ongoing unsustainable trends of forest decline. Ongoing forest changes, their spatiality and connection to humans and environment can be studied with the methods of Land Change Science. The knowledge produced with these methods helps to make arguments about the actors, actions and causes that are behind the forest decline. In this study of Unguja Island in Zanzibar the focus is in the current forest cover and its changes between 1996 and 2009. The cover and changes are measured with often used remote sensing methods of automated land cover classification and post-classification comparison from medium resolution satellite images. Kernel Density Estimation is used to determine the clusters of change, sub-area –analysis provides information about the differences between regions, while distance and regression analyses connect changes to environmental factors. These analyses do not only explain the happened changes, but also allow building quantitative and spatial future scenarios. Similar study has not been made for Unguja and therefore it provides new information, which is beneficial for the whole society. The results show that 572 km2 of Unguja is still forested, but 0,82–1,19% of these forests are disappearing annually. Besides deforestation also vertical degradation and spatial changes are significant problems. Deforestation is most severe in the communal indigenous forests, but also agroforests are decreasing. Spatially deforestation concentrates to the areas close to the coastline, population and Zanzibar Town. Biophysical factors on the other hand do not seem to influence the ongoing deforestation process. If the current trend continues there should be approximately 485 km2 of forests remaining in 2025. Solutions to these deforestation problems should be looked from sustainable land use management, surveying and protection of the forests in risk areas and spatially targeted self-sustainable tree planting schemes.

Use of airborne laser scanner data in demanding forest conditions

Most of the applications of airborne laser scanner data to forestry require that the point cloud be normalized, i.e., each point represents height from the ground instead of elevation. To normalize the point cloud, a digital terrain model (DTM), which is derived from the ground returns in the point cloud, is employed. Unfortunately, extracting accurate DTMs from airborne laser scanner data is a challenging task, especially in tropical forests where the canopy is normally very thick (partially closed), leading to a situation in which only a limited number of laser pulses reach the ground. Therefore, robust algorithms for extracting accurate DTMs in low-ground-point-densitysituations are needed in order to realize the full potential of airborne laser scanner data to forestry. The objective of this thesis is to develop algorithms for processing airborne laser scanner data in order to: (1) extract DTMs in demanding forest conditions (complex terrain and low number of ground points) for applications in forestry; (2) estimate canopy base height (CBH) for forest fire behavior modeling; and (3) assess the robustness of LiDAR-based high-resolution biomass estimation models against different field plot designs. Here, the aim is to find out if field plot data gathered by professional foresters can be combined with field plot data gathered by professionally trained community foresters and used in LiDAR-based high-resolution biomass estimation modeling without affecting prediction performance. The question of interest in this case is whether or not the local forest communities can achieve the level technical proficiency required for accurate forest monitoring. The algorithms for extracting DTMs from LiDAR point clouds presented in this thesis address the challenges of extracting DTMs in low-ground-point situations and in complex terrain while the algorithm for CBH estimation addresses the challenge of variations in the distribution of points in the LiDAR point cloud caused by things like variations in tree species and season of data acquisition. These algorithms are adaptive (with respect to point cloud characteristics) and exhibit a high degree of tolerance to variations in the density and distribution of points in the LiDAR point cloud. Results of comparison with existing DTM extraction algorithms showed that DTM extraction algorithms proposed in this thesis performed better with respect to accuracy of estimating tree heights from airborne laser scanner data. On the other hand, the proposed DTM extraction algorithms, being mostly based on trend surface interpolation, can not retain small artifacts in the terrain (e.g., bumps, small hills and depressions). Therefore, the DTMs generated by these algorithms are only suitable for forestry applications where the primary objective is to estimate tree heights from normalized airborne laser scanner data. On the other hand, the algorithm for estimating CBH proposed in this thesis is based on the idea of moving voxel in which gaps (openings in the canopy) which act as fuel breaks are located and their height is estimated. Test results showed a slight improvement in CBH estimation accuracy over existing CBH estimation methods which are based on height percentiles in the airborne laser scanner data. However, being based on the idea of moving voxel, this algorithm has one main advantage over existing CBH estimation methods in the context of forest fire modeling: it has great potential in providing information about vertical fuel continuity. This information can be used to create vertical fuel continuity maps which can provide more realistic information on the risk of crown fires compared to CBH.