T.A. Groen

Monitoring basin - scale land cover changes in Kagera Basin of Lake Victoria using ancillary data and remote sensing

Abstract The Kagera Basin is a high value ecosystem in the Lake Victoria watershed because of the hydrological and food services it provides. The basin has faced large scale human induced land use and land cover changes (LUCC), but quantitative data is to date lacking. A combination of ancillary data and satellite imagery were interpreted to construct LUCC dynamics for the last century. This study is an initial step towards assessing the impact of LUCC on sustainable agriculture and water quality in the watershed. The results show that large trends of LUCC have rapidly occurred over the last 100 years. The most dominant LUCC processes were gains in farmland areas (not detectable in 1901 to 60% in 2010) and a net reduction in dense forest (7% to 2.6%), woodlands (51% to 6.9%) and savannas (35% to 19.6%) between 1901 and 2010. Forest degradation rapidly occurred during 1974 and 1995 but the forest re-grew between 1995 and 2010 due to forest conservation efforts. Afforestation efforts have resulted in plantation forest increases between 1995 and 2010. The rates of LUCC observed are higher than those reported in Sub Saharan Africa (SSA) and other parts of the world. This is one of the few studies in SSA at a basin scale that combines multi-source spatio-temporal data on land cover to enable long-term quantification of land cover changes. In the discussion we address future research needs for the area based on the results of this study. These research needs include quantifying the impacts of land cover change on nutrient and sediment dynamics, soil organic carbon stocks, and changes in biodiversity.

Optimal Foraging for Multiple Resources in Several Food Species

The concentrations of resources in forage are not perfectly balanced to the needs of an animal, and food species differ in these concentrations. Under many circumstances, animals should thus forage on multiple food species to attain the maximum and most balanced intake of several resources. In this article we present a model to extend optimal foraging theory to incorporate concurrent foraging for multiple resources from several food species. A balancing of resources is achieved by representing the amount of a resource as the time during which it is used. Optimization is considered at two hierarchical levels: the time spent in a patch and the proportion of patches of each food species included in the foraging path. Our model results show that the balancing of resource intake can be achieved at the level of the foraging path, while the maximization of intake can be realized at the nested patch level. The choice for a food species should be dependent on the differences in intake and resource ratios between the food species. Under free choice of food species, the optimal patch residence time is subject not to differences between patches but to the local intake rate.

Soil clay content and fire frequency affect clustering in trees in South African savannas

In this paper, we investigate which factors determine tree clustering in Southern African savannas. This was tested by measuring clustering of trees using the T-squared sampling method in plots of the Kruger National Park experimental burning programme in South Africa. Fire return interval is the main treatment in these plots, but also several auxiliary determining parameters like clay content in the soil, diameter of tree canopies, understorey composition, tree species diversity and average annual rainfall were measured while sampling. In the Kruger National Park 48 plots distributed over four different landscape types and with three different burning treatments (never, once every 3 y and annually) were sampled. First, we related the clustering of trees to these environmental variables. When looking at the most abundant species in each plot, the analysis revealed that clustering is mainly correlated with clay content in the soil. This analysis also showed that fire frequency had a positive effect on the clustering of tree species that are not very abundant. We suggest that less abundant species might be less resistant to fire and therefore adopt a mechanism of clustering to exclude grass fires under their canopy. Finally, we tested the effect of clustering on the impact of fire on trees by analysing the relationship between the distance of a tree to its nearest neighbour and its canopy diameter. We found that clustering reduces the damaging effect of fire on trees. Our study contributes to understanding of savanna functioning by showing which processes are relevant in the distribution of savanna trees.

Using a genetic algorithm as an optimal band selector in the mid and thermal infrared (2.5-14 µm) to discriminate vegetation species

Genetic variation between various plant species determines differences in their physio-chemical makeup and ultimately in their hyperspectral emissivity signatures. The hyperspectral emissivity signatures, on the one hand, account for the subtle physio-chemical changes in the vegetation, but on the other hand, highlight the problem of high dimensionality. The aim of this paper is to investigate the performance of genetic algorithms coupled with the spectral angle mapper (SAM) to identify a meaningful subset of wavebands sensitive enough to discriminate thirteen broadleaved vegetation species from the laboratory measured hyperspectral emissivities. The performance was evaluated using an overall classification accuracy and Jeffries Matusita distance. For the multiple plant species, the targeted bands based on genetic algorithms resulted in a high overall classification accuracy (90%). Concentrating on the pairwise comparison results, the selected wavebands based on genetic algorithms resulted in higher Jeffries Matusita (J-M) distances than randomly selected wavebands did. This study concludes that targeted wavebands from leaf emissivity spectra are able to discriminate vegetation species.

Ecology of West Nile virus across four European countries: review of weather profiles, vector population dynamics and vector control response

West Nile virus (WNV) represents a serious burden to human and animal health because of its capacity to cause unforeseen and large epidemics. Until 2004, only lineage 1 and 3 WNV strains had been found in Europe. Lineage 2 strains were initially isolated in 2004 (Hungary) and in 2008 (Austria) and for the first time caused a major WNV epidemic in 2010 in Greece with 262 clinical human cases and 35 fatalities. Since then, WNV lineage 2 outbreaks have been reported in several European countries including Italy, Serbia and Greece. Understanding the interaction of ecological factors that affect WNV transmission is crucial for preventing or decreasing the impact of future epidemics. The synchronous co-occurrence of competent mosquito vectors, virus, bird reservoir hosts, and susceptible humans is necessary for the initiation and propagation of an epidemic. Weather is the key abiotic factor influencing the life-cycles of the mosquito vector, the virus, the reservoir hosts and the interactions between them. The purpose of this paper is to review and compare mosquito population dynamics, and weather conditions, in three ecologically different contexts (urban/semi-urban, rural/agricultural, natural) across four European countries (Italy, France, Serbia, Greece) with a history of WNV outbreaks. Local control strategies will be described as well. Improving our understanding of WNV ecology is a prerequisite step for appraising and optimizing vector control strategies in Europe with the ultimate goal to minimize the probability of WNV infection.

Proxies for soil organic carbon derived from remote sensing

The possibility of carbon storage in soils is of interest because compared to vegetation it contains more carbon. Estimation of soil carbon through remote sensing based techniques can be a cost effective approach, but is limited by available methods. This study aims to develop a model based on remotely sensed variables (elevation, forest type and above ground biomass) to estimate soil carbon stocks. Field observations on soil organic carbon, species composition, and above ground biomass were recorded in the subtropical forest of Chitwan, Nepal. These variables were also estimated using LiDAR data and a WorldView 2 image. Above ground biomass was estimated from the LiDAR image using a novel approach where the image was segmented to identify individual trees, and for these trees estimates of DBH and Height were made. Based on AIC (Akaike Information Criterion) a regression model with above ground biomass derived from LiDAR data, and forest type derived from WorldView 2 imagery was selected to estimate soil organic carbon (SOC) stocks. The selected model had a coefficient of determination (R2) of 0.69. This shows the scope of estimating SOC with remote sensing derived variables in sub-tropical forests.

Remotely-Sensed Early Warning Signals of a Critical Transition in a Wetland Ecosystem

The response of an ecosystem to external drivers may not always be gradual and reversible. Discontinuous and sometimes irreversible changes, called ‘regime shifts’ or ‘critical transitions’, can occur. The likelihood of such shifts is expected to increase for a variety of ecosystems, and it is difficult to predict how close an ecosystem is to a critical transition. Recent modelling studies identified indicators of impending regime shifts that can be used to provide early warning signals of a critical transition. The identification of such transitions crucially depends on the ability to monitor key ecosystem variables, and their success may be limited by lack of appropriate data. Moreover, empirical demonstrations of the actual functioning of these indicators in real-world ecosystems are rare. This paper presents the first study which uses remote sensing data to identify a critical transition in a wetland ecosystem. In this study, we argue that a time series of remote sensing data can help to characterize and determine the timing of a critical transition. This can enhance our abilities to detect and anticipate them. We explored the potentials of remotely sensed vegetation (NDVI), water (MNDWI), and vegetation-water (VWR) indices, obtained from time series of MODIS satellite images to characterize the stability of a wetland ecosystem, Dorge Sangi, near the lake Urmia, Iran, that experienced a regime shift recently. In addition, as a control case, we applied the same methods to another wetland ecosystem in Lake Arpi, Armenia which did not experience a regime shift. We propose a new composite index (MVWR) based on combining vegetation and water indices, which can improve the ability to anticipate a critical transition in a wetland ecosystem. Our results revealed that MVWR in combination with autocorrelation at-lag-1 could successfully provide early warning signals for a critical transition in a wetland ecosystem, and showed a significantly improved performance compared to either vegetation (NDVI) or water (MNDWI) indices alone.

Climatic niche breadth can explain variation in geographical range size of alpine and subalpine plants

ABSTRACT Understanding the environmental factors determining the distribution of species with different range sizes can provide valuable insights for evolutionary ecology and conservation biology in the face of expected climate change. However, little is known about what determines the variation in geographical and elevational ranges of alpine and subalpine plant species. Here, we examined the relationship between geographical and elevational range sizes for 80 endemic rhododendron species in China using Spearman’s rank-order correlation. We ran the species distribution model – maximum entropy modelling (MaxEnt) – with 27 environmental variables. The importance of each variable to the model prediction was compared for species groups with different geographical and elevational range sizes. Our results showed that the correlation between geographical and elevational range sizes of rhododendron species was not significant. Climate-related variables were found to be the most important factors in shaping the distributional ranges of alpine and subalpine plant species across China. Species with geographically and elevationally narrow ranges had distinct niche requirements. For geographical ranges, the narrow-ranged species showed less tolerance to niche conditions than the wide-ranged species. For elevational ranges, compared with the wide-ranged species, the narrow-ranged species showed an equivalent niche breadth, but occurred at different niche position along the environmental gradient. Our findings suggest that over large spatial extents the elevational range size can be a complementary trait of alpine and subalpine plant species to geographical range size. Climatic niche breadth, especially the range of seasonal variability, can explain species’ geographical range sizes. Changes in climate may influence the distribution of rhododendrons, with the effects likely being felt most by species with either a narrow geographical or narrow elevational range.

Tree Line Change Detection Using Historical Hexagon Mapping Camera Imagery and Google Earth Data

Monitoring the response of tree lines to climatic change requires long time series. Therefore ground-based studies, initially designed for other purposes, are used, causing a bias in the sampling design. Using historical satellite data might overcome this bias. This study explores the usability of historical spy-satellite imagery from the United States Hexagon missions to detect changes in tree lines. We find that both vertical and horizontal errors are within acceptable boundaries (± 18.0 m in horizontal direction and 5.5 m in vertical direction) to detect change. This opens opportunities to explore tree line changes globally with a more robust sampling strategy.

Spectroscopic determination of leaf traits using infrared spectra

Abstract Leaf traits characterise and differentiate single species but can also be used for monitoring vegetation structure and function. Conventional methods to measure leaf traits, especially at the molecular level (e.g. water, lignin and cellulose content), are expensive and time-consuming. Spectroscopic methods to estimate leaf traits can provide an alternative approach. In this study, we investigated high spectral resolution (6612 bands) emissivity measurements from the short to the long wave infrared (1.4-16.0 μm) of leaves from 19 different plant species ranging from herbaceous to woody, and from temperate to tropical types. At the same time, we measured 14 leaf traits to characterise a leaf, including chemical (e.g., leaf water content, nitrogen, cellulose) and physical features (e.g., leaf area and leaf thickness). We fitted partial least squares regression (PLSR) models across the SWIR, MWIR and LWIR for each leaf trait. Then, reduced models (PLSRred) were derived by iteratively reducing the number of bands in the model (using a modified Jackknife resampling method with a Martens and Martens uncertainty test) down to a few bands (4–10 bands) that contribute the most to the variation of the trait. Most leaf traits could be determined from infrared data with a moderate accuracy (65  R cv 2 R cv 2 R cv 2 above 0.80 for the full range models). Leaf thickness, cellulose and lignin were predicted with reasonable accuracy from a combination of single infrared bands. Nevertheless, for all leaf traits, a combination of a few bands yields moderate to accurate estimations.