Search

Quantifying ecosystem structure is of key importance for ecology, conservation, restoration, and biodiversity monitoring because the diversity, geographic distribution and abundance of animals, plants and other organisms is tightly linked to the physical structure of vegetation and associated microclimates. Light Detection And Ranging (LiDAR) — an active remote sensing technique — can provide detailed and high resolution information on ecosystem structure because the laser pulse emitted from the sensor and its subsequent return signal from the vegetation (leaves, branches, stems) delivers three-dimensional point clouds from which metrics of vegetation structure (e.g. ecosystem height, cover, and structural complexity) can be derived. However, processing 3D LiDAR point clouds into geospatial data products of ecosystem structure remains challenging across broad spatial extents due to the large volume of national or regional point cloud datasets (typically multiple terabytes consisting of hundreds of billions of points). Here, we present a high-throughput workflow called ‘Laserfarm’ enabling the efficient, scalable and distributed processing of multi-terabyte LiDAR point clouds from national and regional airborne laser scanning (ALS) surveys into geospatial data products of ecosystem structure. Laserfarm is a free and open-source, end-to-end workflow which contains modular pipelines for the re-tiling, normalization, feature extraction and rasterization of point cloud information from ALS and other LiDAR surveys. The workflow is designed with horizontal scalability and can be deployed with distributed computing on different infrastructures, e.g. a cluster of virtual machines. We demonstrate the Laserfarm workflow by processing a country-wide multi-terabyte ALS dataset of the Netherlands (covering ∼34,000 km2 with ∼700 billion points and ∼ 16 TB uncompressed LiDAR point clouds) into 25 raster layers at 10 m resolution capturing ecosystem height, cover and structural complexity at a national extent. The Laserfarm workflow, implemented in Python and available as Jupyter Notebooks, is applicable to other LiDAR datasets and enables users to execute automated pipelines for generating consistent and reproducible geospatial data products of ecosystems structure from massive amounts of LiDAR point clouds on distributed computing infrastructures, including cloud computing environments. We provide information on workflow performance (including total CPU times, total wall-time estimates and average CPU times for single files and LiDAR metrics) and discuss how the Laserfarm workflow can be scaled to other LiDAR datasets and computing environments, including remote cloud infrastructures. The Laserfarm workflow allows a broad user community to process massive amounts of LiDAR point clouds for mapping vegetation structure, e.g. for applications in ecology, biodiversity monitoring and ecosystem restoration.

The energy exchange between the Earth's surface and atmosphere results in a highly dynamic habitat through which birds move. Thermal uplift is an atmospheric feature which many birds are able to exploit in order to save energy in flight, but which is governed by complex surface-atmosphere interactions. In mosaic landscapes consisting of multiple land uses, the spatial distribution of thermal uplift is expected to be heterogenous and birds may use the landscape selectively to maximise flight over areas where thermal soaring opportunities are best. Flight generalists such as the lesser black-backed gull, Larus fuscus, are expected to be less reliant on thermal uplift than obligate soaring birds. Nevertheless, gulls may select flight behaviours and routes in response to or in anticipation of thermal uplift in order to reduce their transport costs, even in landscapes where thermal uplift isn't prevalent. We explore thermal soaring over land in lesser black-backed gulls by using high-resolution GPS tracking to characterise individual instances of thermal soaring and detailed energy exchange modelling to map the thermal landscape which gulls experience. We determine that lesser black-backed gulls are regularly able to undertake thermal soaring, even in a wet temperate landscape below sea level. By examining the relationship between lesser black-backed gull flight, thermal uplift and land use, we determine that built up areas, particularly towns and cities, provide thermal uplift hotspots which lesser black-backed gulls preferentially make use of, resulting in more opportunities for energy saving flight through thermal soaring.

Past climate changes provide important clues for advancement of studies on current global change biology. We have tested large-scale biogeographic patterns through four marine groups from twelve Atlantic Ocean archipelagos and searched for patterns between species richness/endemism and littoral area, age, isolation, latitude and mean annual sea-surface temperatures. Species richness is strongly correlated with littoral area. Two reinforcing effects take place during glacial episodes: i) species richness is expected to decrease (in comparison with interglacial periods) due to the local disappearance of sandy/muddy-associated species; ii) because littoral area is minimal during glacial episodes, area per se induces a decrease on species richness (by extirpation/extinction of marine species) as well as affecting speciation rates. Maximum speciation rates are expected to occur during the interglacial periods, whereas immigration rates are expected to be higher at the LGM. Finally, sea-level changes are a paramount factor influencing marine biodiversity of animals and plants living on oceanic islands.

Mapping and quantifying 3D vegetation structure is essential for assessing and monitoring ecosystem structure and function within wetlands. Airborne Laser Scanning (ALS) is a promising data source for developing indicators of 3D vegetation structure, but derived metrics are often not compared with 3D structural field measurements and the acquisition of ALS data is rarely standardized across different remote sensing surveys. Here, we compare a set of Light Detection And Ranging (LiDAR) metrics derived from ALS datasets with varying characteristics to a standardized set of field measurements of vegetation height, biomass and Leaf Area Index (LAI) across three Hungarian lakes (Lake Balaton, Lake Fertő and Lake Tisza). The ALS datasets differed in whether the recording type was full waveform (FWF) or discrete return, and in their point density (4 pt/m2 and 21 pt/m2). A total of eight LiDAR metrics captured radiometric information as well as descriptors of vegetation cover, height and vertical variability. Multivariate regression models with field-based measurements of vegetation height, biomass or LAI as response variable and LiDAR metrics as predictors showed major differences between ALS recording types, and were affected by differences in spatial resolution, temporal offset and seasonality between field and ALS data acquisition. Vegetation height could be estimated with high to intermediate accuracy (FWF ALS data only: R2 = 0.84; combination of ALS datasets: R2 = 0.67), demonstrating its potential as a robust indicator of 3D vegetation structure across different ALS datasets. In contrast, the estimation of biomass and LAI in these wetlands was sensitive to variation in ALS characteristics and to the discrepancies between field and ALS data in terms of spatial resolution, temporal offset and seasonality (biomass: R2 = 0.20–0.22; LAI: R2 = 0.08–0.30). We recommend the use of FWF ALS data within wetlands because it captures more vegetation structural details in dense reed and marshland vegetation. We further suggest that ecologists and remote sensing scientist should better coordinate the simultaneous and standardized acquisition of field and ALS data for testing the robustness of quantitative descriptors of vegetation cover, height and vertical variability within wetlands. This is important for establishing operational and spatially contiguous ALS-based indicators of 3D ecosystem structure across wetlands.

Two payments for forest ecosystem services (PFES) schemes under one common legal-institutional coordination mechanism but different historical-institutional background and organizational design are analyzed to measure and explain their transaction costs (TC). Data on TC related to payment transfers and conditionality compliance are collected using a combination of in-depth interviews with local PFES scheme coordinators, site visits, and secondary data analysis. The two PFES schemes show substantial differences in TC despite the fact that they emerged from the same legal-institutional framework due to differences in participation rates, types of forest ecosystem services providers, and payment characteristics. (C) 2017 Published by Elsevier B.V.

Inter-individual variance in longevity (or any other demographic outcome) may arise from heterogeneity or from individual stochasticity. Heterogeneity refers to differences among individuals in the demographic rates experienced at a given age or stage. Stochasticity refers to variation due to the random outcome of demographic rates applied to individuals with the same properties. The variance due to individual stochasticity can be calculated from a Markov chain description of the life cycle. The variance due to heterogeneity can be calculated from a multistate model that incorporates the heterogeneity. We show how to use this approach to decompose the variance in longevity into contributions from stochasticity and heterogeneous frailty for male and female cohorts from Sweden (1751-1899), France (1816-1903), and Italy (1872-1899), and also for a selection of period data for the same countries.Heterogeneity in mortality is described by the gamma-Gompertz-Makeham model, in which a gamma distributed "frailty'' modifies a baseline Gompertz-Makeham mortality schedule. Model parameters were estimated by maximum likelihood for a range of starting ages. The estimates were used to construct an age×frailty-classified matrix model, from which we compute the variance of longevity and its components due to heterogeneous frailty and to individual stochasticity. The estimated fraction of the variance in longevity due to heterogeneous frailty (averaged over time) is less than 10% for all countries and for both sexes. These results suggest that most of the variance in human longevity arises from stochasticity, rather than from heterogeneous frailty.

Trait data represent the basis for ecological and evolutionary research and have relevance for biodiversity conservation, ecosystem management and earth system modelling. The collection and mobilization of trait data has strongly increased over the last decade, but many trait databases still provide only species-level, aggregated trait values (e.g. ranges, means) and lack the direct observations on which those data are based. Thus, the vast majority of trait data measured directly from individuals remains hidden and highly heterogeneous, impeding their discoverability, semantic interoperability, digital accessibility and (re-)use. Here, we integrate quantitative measurements of verbatim trait information from plant individuals (e.g. lengths, widths, counts and angles of stems, leaves, fruits and inflorescence parts) from multiple sources such as field observations and herbarium collections. We develop a workflow to harmonize heterogeneous trait measurements (e.g. trait names and their values and units) as well as additional information related to taxonomy, measurement or fact and occurrence. This data integration and harmonization builds on vocabularies and terminology from existing metadata standards and ontologies such as the Ecological Trait-data Standard (ETS), the Darwin Core (DwC), the Thesaurus Of Plant characteristics (TOP) and the Plant Trait Ontology (TO). A metadata form filled out by data providers enables the automated integration of trait information from heterogeneous datasets. We illustrate our tools with data from palms (family Arecaceae), a globally distributed (pantropical), diverse plant family that is considered a good model system for understanding the ecology and evolution of tropical rainforests. We mobilize nearly 140,000 individual palm trait measurements in an interoperable format, identify semantic gaps in existing plant trait terminology and provide suggestions for the future development of a thesaurus of plant characteristics. Our work thereby promotes the semantic integration of plant trait data in a machine-readable way and shows how large amounts of small trait data sets and their metadata can be integrated into standardized data products.

Many species of large herbivore rely on agricultural land for their feeding habitats, but available food resources are highly variable in space and time. The conservation and management of farmland-dependant herbivores would therefore benefit from predictions about how species will respond to changes in their environment. We developed an individual-based model (IBM) to provide such predictions for three overwintering avian herbivore species that feed on agricultural land: Bewick's swans (Cygnus columbianus bewickii), whooper swans (Cygnus cygnus), and mute swans (Cygnus olor). Our validated model predicted how potential future changes in food availability and competition would affect (i) the proportion of the current swan population that could be supported, (ii) the proportion of swans that successfully departed on migration at the end of winter, (iii) swan daily foraging effort, and (iv) late winter crop biomasses. Regardless of competitor numbers or food availability, all individuals were predicted to avoid starvation and depart successfully. Individual swans offset higher competition and reduced food availability by increasing the proportion of daylight spent foraging. Our simulations indicate that swans have considerable capacity to buffer against losses of food resources and increased competition by increasing their foraging effort, but this may result in additional grazing damage to agricultural crops. Our findings suggest that the recent c.40% decline in Bewick's swan numbers was unlikely to be linked to changes in winter food resources or competition.

In this paper we outline the stakeholder-led approaches in the development of biological data products to support effective conservation, management and policy development. The requirements of a broad range of stakeholders and iterative, structured processes framed the development of tools, models and maps that support the FAIR (Findable, Accessible, Interoperable, Reusable) data principles. By structuring the resultant data products around the emerging biological Essential Ocean Variables, and through the engagement with a broad range of end-users, the EMODnet (European Marine Observation and Data Network) Biology project has delivered a suite of demonstration data products. These products are presented in the European Atlas of Marine Life, an online resource demonstrating the value of open marine biodiversity data and help to answer fundamental and policy-driven questions related to managing the natural and anthropogenic impacts in European waters.