Nest predation in Afrotropical forest fragments shaped by inverse edge effects, timing of nest initiation and vegetation structure

High levels of nest predation influence the population dynamics of many tropical birds, especially when deforestation alters nest predator communities. The consequences of tropical forest fragmentation on nest predation, however, remain poorly understood, as natural predation patterns have only been well documented in a handful of tropical forests. Here, we show the results of an extensive study of predation on natural nests of Cabanis's Greenbul (Phyllastrephus cabanisi) during 3 years in a highly fragmented cloud forest in SE Kenya. Overall predation rates derived from 228 scrub nests averaged 69 %, matching the typical high predation level on tropical bird species. However, predation rates strongly varied in space and time, and a model that combined timing effects of fragment, edge, concealment, year and nest was best supported by our data. Nest predation rates consistently increased from forest edge to interior, opposing the classic edge effect on nest predation, and supporting the idea that classic edge effects are much rarer in Afrotropical forests than elsewhere. Nest concealment also affected predation rates, but the strength and direction of the relationship varied across breeding seasons and fragments. Apart from spatial variation, predation rates declined during the breeding season, although the strength of this pattern varied among breeding seasons. Complex and variable relationships with nest predation, such as those demonstrated here, suggest that several underlying mechanisms interact and imply that fixed nesting strategies may have variable-even opposing-fitness effects between years, sites and habitats

Additions to the moss flora of the Taita Hills and Mount Kasigau, Kenya

Based on our recent collections we report 43 moss species as new to the Taita Hills and Mount Kasigau in SE Kenya, 15 of the species being also new to the country. The number of moss species known from the region rises from the previously reported 85 to 128, and from 506 to 521 for the whole country. The most noteworthy findings are Fissidens splendens Brugg.-Nann., previously known only from Tanzania, and Barbella capillicaulis (Renauld & Cardot) Cardot var. capillicaulis (Renauld & Cardot) Cardot, previously reported from Mauritius, Madagascar and Uganda. The taxa reported represent the families Anomodontaceae (1 sp.), Brachytheciaceae (3 spp.), Calymperaceae (3 spp.), Dicranaceae (8 spp.), Erpodiaceae (1 sp.), Fissidentaceae (3 spp.), Hedwigiaceae (1 sp.), Hookeriaceae (1 sp.), Hypnaceae (3 spp.), Leucodontaceae (1 sp.), Meteoriaceae (3 spp.), Neckeraceae (5 spp.), Orthotrichaceae (1 sp.), Pilotrichaceae (1 sp.), Polytrichaceae (1 sp.), Pterigynandraceae (1 sp.), Pterobryaceae (2 spp.), Pylaisiadelphaceae (1 sp.), Sematophyllaceae (1 sp.), Stereophyllaceae (1 sp.), and Thuidiaceae (1 sp.).Peer reviewe

Utility of hyperspectral compared to multispectral remote sensing data in estimating forest biomass and structure variables in Finnish boreal forest

Three-quarters of Finland’s land surface area is filled with forests, which compose a great part of the country’s biomass, carbon pools and carbon sinks. In order to acquire up-to-date information on the forests, optical remote sensing techniques are commonly used. Moreover, in the future hyperspectral satellite missions will start providing data to support the needs of natural resource management practices, such as forestry. It is, however, unclear what would be the additional value from using hyperspectral data compared to multispectral in quantifying forest variables of Finnish boreal forest. In this study, we used the remote sensing data by hyperspectral AISA imager (128 bands, 400–1000 nm, resolution 0.7 m) and Sentinel-2 (10 bands, resolution 10 m) to assess the possible benefits of higher spectral resolution. As reference data, we used a new nationwide forest resource dataset (stand-level data), which has a high potential in further remote sensing applications. In addition, we used a set of independent in situ measurements (plot-level data) for validation. We applied two kernel-based machine learning regression algorithms (Gaussian process and support vector regression) to relate boreal forest variables with the remote sensing data. The variables of interest were mean height, basal area, leaf area index (LAI), stem biomass and main tree species. The regression algorithms were trained with stand-level data and estimations were evaluated with stand- and plot-level holdout sets. The estimation accuracies were examined with absolute and relative root-mean-square errors. Successful variable estimations showed that kernel-based regression algorithms are suitable tools for forest structure estimation. Based on the results, the additional value of hyperspectral remote sensing data in forest variable estimation in Finnish boreal forest is mainly related to variables with species-specific information, such as main tree species and LAI. The more interesting variables for forestry industry, such as mean height, basal area and stem biomass, can also be estimated accurately with more traditional multispectral remote sensing data.Peer reviewe

Recruiting Conventional Tree Architecture Models into State-of-the-Art LiDAR Mapping for Investigating Tree Growth Habits in Structure

Mensuration of tree growth habits is of considerable importance for understanding forest ecosystem processes and forest biophysical responses to climate changes. However, the complexity of tree crown morphology that is typically formed after many years of growth tends to render it a non-trivial task, even for the state-of-the-art 3D forest mapping technology-light detection and ranging (LiDAR). Fortunately, botanists have deduced the large structural diversity of tree forms into only a limited number of tree architecture models, which can present a-priori knowledge about tree structure, growth, and other attributes for different species. This study attempted to recruit Halle architecture models (HAMs) into LiDAR mapping to investigate tree growth habits in structure. First, following the HAM-characterized tree structure organization rules, we run the kernel procedure of tree species classification based on the LiDAR-collected point clouds using a support vector machine classifier in the leave-one-out-for-cross-validation mode. Then, the HAM corresponding to each of the classified tree species was identified based on expert knowledge, assisted by the comparison of the LiDAR-derived feature parameters. Next, the tree growth habits in structure for each of the tree species were derived from the determined HAM. In the case of four tree species growing in the boreal environment, the tests indicated that the classification accuracy reached 85.0%, and their growth habits could be derived by qualitative and quantitative means. Overall, the strategy of recruiting conventional HAMs into LiDAR mapping for investigating tree growth habits in structure was validated, thereby paving a new way for efficiently reflecting tree growth habits and projecting forest structure dynamics.Peer reviewe

Termite mound architecture regulates nest temperature and correlates with species identities of symbiotic fungi.

Background Large and complex mounds built by termites of the genus Macrotermes characterize many dry African landscapes, including the savannas, bushlands, and dry forests of the Tsavo Ecosystem in southern Kenya. The termites live in obligate symbiosis with filamentous fungi of the genus Termitomyces. The insects collect dead plant material from their environment and deposit it into their nests where indigestible cell wall compounds are effectively decomposed by the fungus. Above-ground mounds are built to enhance nest ventilation and to maintain nest interior microclimates favorable for fungal growth. Objectives In Tsavo Ecosystem two Macrotermes species associate with three different Termitomyces symbionts, always with a monoculture of one fungal species within each termite nest. As mound architecture differs considerably both between and within termite species we explored potential relationships between nest thermoregulatory strategies and species identity of fungal symbionts. Methods External dimensions were measured from 164 Macrotermes mounds and the cultivated Termitomyces species were identified by sequencing internal transcribed spacer (ITS) region of ribosomal DNA. We also recorded the annual temperature regimes of several termite mounds to determine relations between mound architecture and nest temperatures during different seasons. Results Mound architecture had a major effect on nest temperatures. Relatively cool temperatures were always recorded from large mounds with open ventilation systems, while the internal temperatures of mounds with closed ventilation systems and small mounds with open ventilation systems were consistently higher. The distribution of the three fungal symbionts in different mounds was not random, with one fungal species confined to “hot nests.” Conclusions Our results indicate that different Termitomyces species have different temperature requirements, and that one of the cultivated species is relatively intolerant of low temperatures. The dominant Macrotermes species in our study area can clearly modify its mound architecture to meet the thermal requirements of several different symbionts. However, a treacherous balance seems to exist between symbiont identity and mound architecture, as the maintenance of the thermophilic fungal species obviously requires reduced mound architecture that, in turn, leads to inadequate gas exchange. Hence, our study concludes that while the limited ventilation capacity of small mounds sets strict limits to insect colony growth, in this case, improving nest ventilation would invariable lead to excessively low nest temperatures, with negative consequences to the symbiotic fungus.Peer reviewe