Extreme Food-Plant Specialisation in Megabombus Bumblebees as a Product of Long Tongues Combined with Short Nesting Seasons

© 2015 Huang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. http://creativecommons.org/licenses/by/4.0/ The attached file is the published version of the article

Barriers and bridges for intensified wood production and biodiversity conservation in NW Russia's boreal forest

Wood production and biodiversity conservation are two key objectives of sustainable forest management policy. These goals are rival and, therefore, hard to achieve at the same time and space. The aim of the thesis is to contribute to the understanding of barriers and bridges for intensified wood production and biodiversity conservation in NW Russia’s boreal forests. It was implemented by case study approach on both ecological and social systems of forest landscapes with different forest use histories in the European boreal biome. I first studied the forest use history in a forest management unit in NW Russia (paper I). Second, I analysed how production and biodiversity goals are actually balanced on the ground by comparing indicators for wood production and biodiversity conservation in NW Russia, Belarus, Latvia and Sweden (paper II). Next, in order to test the hypothesis that there are no biophysical obstacles to intensified wood production in NW Russia, I compared tree growth rates at 4 latitudes in NW Russia and Sweden (paper III). Finally, I reviewed the history of forest zoning policy, which is an influential mechanism to conserve biodiversity in Russian forests, and assessed if zoning policy change towards intensification negatively affected riparian forests, e.g. biodiversity conservation (paper IV). Results from this research shows that barriers for intensified wood production in NW Russia include limited silviculture, poor road development and conservative mind-set of decision-makers (paper I). Bridges for intensified wood production involve existing infrastructure of forest villages and available middle-aged forests (paper I) as well as equal biophysical conditions for tree growths (paper III). Biodiversity conservation goal is achieved better than wood production in NW Russia in comparison to countries with longer forest use histories (paper II). More relaxed zoning policy is considered as barrier to biodiversity conservation (paper IV). Developed zoning system (paper IV), landscape approach initiatives and remaining intact forests (paper I) provide opportunities for biodiversity conservation. This thesis suggests that balanced sustained-yield wood production together with biodiversity conservation is possible when a segregative zoning model is employed. To conclude, there is a need to engage in transdisciplinary research on the role of landscape stewardship for satisfying both wood production and biodiversity conservation objectives

An online system for calculating and delivering long-term carrying capacity information for Queensland grazing properties. Part 1: background and development

This paper (Part 1) describes the development of a new online system that estimates long-term carrying capacity (LTCC) for grazing properties across Queensland, Australia. High year-to-year and multi-year rainfall variability is a dominating feature of the climate of Queensland’s grazing lands, and poses major challenges for extensive livestock production. The use of LTCC is one approach used by graziers to reduce the impact of rainfall variability on land condition and financial performance. Over the past 30 years, scientists, graziers and their advisors have developed a simple approach to calculating LTCC ((average annual pasture growth × safe pasture utilisation) ÷ annual animal intake). This approach has been successful at a property scale (regional south-west Queensland) and in a wider application through Grazing Land Management (GLM) regional workshops. We have built on these experiences to develop an online system (as described in detail in Part 2; Zhang et al. 2021; this issue) that incorporates the simple LTCC approach with advances in technology and grazing science to provide LTCC information for Queensland grazing properties. Features of the LTCC system are: (1) assimilation of spatial datasets (cadastral data, grazing land types, climate data, remotely-sensed woody vegetation cover); (2) a pasture growth simulation model; (3) land type parameter sets of biophysical attributes; and (4) estimates of safe pasture utilisation. The ‘FORAGE LTCC report’ is a major product of the system, describing individual property information that allows detailed analysis and explanation of the components of the LTCC calculation by land type and land condition. The online system rapidly analyses property spatial data and calculates paddock/property LTCC information. For the 10 months between November 2020 and August 2021, over 4000 grazing property reports have been requested in Queensland, and has proven to be a sound basis for ‘discussion support’ with grazier managers and their advisors

Implications of land use change for the sustainability of urban areas: A case study of Stellenbosch, South Africa

Abstract: Sustainable development, an objective of urban planning, is difficult to put into practice. Data to monitor sustainable land use management is often lacking, particularly in developing countries. This paper investigates the use of earth observation data for supporting sustainable land use planning. It proposes the use of decision consequence analysis (DCA) as a simple and structured way to put sustainable development into practice. The study demonstrates how land use change (LUC) which also includes land cover, the local land use mix index (LLUM) and land use frequency (LUF) can be used as indicators of objective land use sustainability..

Ireland’s Rural Environment: Research Highlights from Johnstown Castle

ReportThis booklet gives a flavour of the current research in Teagasc Johnstown Castle Research Centre and introduces you to the staff involved. It covers the areas of Nutrient Efficiency, Gaseous emissions, Agricultural Ecology, Soils and Water quality

A conceptual-model-based sediment connectivity assessment for patchy agricultural catchments

The accelerated sediment supply from agricultural soils to riverine and lacustrine environments leads to negative off-site consequences. In particular, the sediment connectivity from agricultural land to surface waters is strongly affected by landscape patchiness and the linear structures that separate field parcels (e.g. roads, tracks, hedges, and grass buffer strips). Understanding the interactions between these structures and sediment transfer is therefore crucial for minimising off-site erosion impacts. Although soil erosion models can be used to understand lateral sediment transport patterns, model-based connectivity assessments are hindered by the uncertainty in model structures and input data. Specifically, the representation of linear landscape features in numerical soil redistribution models is often compromised by the spatial resolution of the input data and the quality of the process descriptions. Here we adapted the Water and Tillage Erosion Model and Sediment Delivery Model (WaTEM/SE-DEM) using high-resolution spatial data (2 m x 2 m) to analyse the sediment connectivity in a very patchy mesoscale catchment (73 km(2)) of the Swiss Plateau. We used a global sensitivity analysis to explore model structural assumptions about how linear landscape features (dis)connect the sediment cascade, which allowed us to investigate the uncertainty in the model structure. Furthermore, we compared model simulations of hillslope sediment yields from five sub-catchments to tributary sediment loads, which were calculated with long-term water discharge and suspended sediment measurements. The sensitivity analysis revealed that the assumptions about how the road network (dis)connects the sediment transfer from field blocks to water courses had a much higher impact on modelled sediment yields than the uncertainty in model parameters. Moreover, model simulations showed a higher agreement with tributary sediment loads when the road network was assumed to directly connect sediments from hillslopes to water courses. Our results ultimately illustrate how a high-density road network combined with an effective drainage system increases sediment connectivity from hillslopes to surface waters in agricultural landscapes. This further highlights the importance of considering linear landscape features and model structural uncertainty in soil erosion and sediment connectivity research

Sustainable Agriculture and Advances of Remote Sensing (Volume 1)

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publishing the results, among others

The LANDSUPPORT geospatial decision support system (S-DSS) vision: Operational tools to implement sustainability policies in land planning and management

Nowadays, there is contrasting evidence between the ongoing continuing and widespread environmental degradation and the many means to implement environmental sustainability actions starting from good policies (e.g. EU New Green Deal, CAP), powerful technologies (e.g. new satellites, drones, IoT sensors), large databases and large stakeholder engagement (e.g. EIP-AGRI, living labs). Here, we argue that to tackle the above contrasting issues dealing with land degradation, it is very much required to develop and use friendly and freely available web-based operational tools to support both the implementation of environmental and agriculture policies and enable to take positive environmental sustainability actions by all stakeholders. Our solution is the S-DSS LANDSUPPORT platform, consisting of a free web-based smart Geospatial CyberInfrastructure containing 15 macro-tools (and more than 100 elementary tools), co-designed with different types of stakeholders and their different needs, dealing with sustainability in agriculture, forestry and spatial planning. LANDSUPPORT condenses many features into one system, the main ones of which were (i) Web-GIS facilities, connection with (ii) satellite data, (iii) Earth Critical Zone data and (iv) climate datasets including climate change and weather forecast data, (v) data cube technology enabling us to read/write when dealing with very large datasets (e.g. daily climatic data obtained in real time for any region in Europe), (vi) a large set of static and dynamic modelling engines (e.g. crop growth, water balance, rural integrity, etc.) allowing uncertainty analysis and what if modelling and (vii) HPC (both CPU and GPU) to run simulation modelling 'on-the-fly' in real time. Two case studies (a third case is reported in the Supplementary materials), with their results and stats, covering different regions and spatial extents and using three distinct operational tools all connected to lower land degradation processes (Crop growth, Machine Learning Forest Simulator and GeOC), are featured in this paper to highlight the platform's functioning. Landsupport is used by a large community of stakeholders and will remain operational, open and free long after the project ends. This position is rooted in the evidence showing that we need to leave these tools as open as possible and engage as much as possible with a large community of users to protect soils and land

Data Mining for Source Apportionment of Trace Elements in Water and Solid Matrix

Trace elements migrate among different environment bodies with the natural geochemical reactions, and impacted by human industrial, agricultural, and civil activities. High load of trace elements in water, river and lake sediment, soil and air particle lead to potential to health of human being and ecological system. To control the impact on environment, source apportionment is a meaningful, and also a challenging task. Traditional methods to make source apportionment are usually based on geochemical techniques, or univariate analysis techniques. In recently years, the methods of multivariate analysis, and the related concepts data mining, machine learning, big data, are developing fast, which provide a novel route that combing the geochemical and data mining techniques together. These methods have been proved successful to deal with the source apportionment issue. In this chapter, the data mining methods used on this topic and implementations in recent years are reviewed. The basic method includes principal component analysis, factor analysis, clustering analysis, positive matrix fractionation, decision tree, Bayesian network, artificial neural network, etc. Source apportionment of trace elements in surface water, ground water, river and lake sediment, soil, air particles, dust are discussed