fuzzySim: Fuzzy similarity in species distributions

fuzzySim is an R package for calculating fuzzy similarity in species occurrence patterns. It includes functions for data preparation, such as converting species lists (long format) to presence-absence tables (wide format), obtaining unique abbreviations of species names, or transposing (parts of) complex data frames; and sample data sets for providing practical examples. It can convert binary presence-absence to fuzzy occurrence data, using e.g. trend surface analysis, inverse distance interpolation or prevalence-independent environmental favourability modelling, for multiple species simultaneously. It then calculates fuzzy similarity among (fuzzy) species distributions and/or among (fuzzy) regional species compositions. Currently available similarity indices are Jaccard, Sørensen, Simpson, and Baroni-Urbani & Buser.

Soil quality assessment in conservation agriculture systems

Soil is a key resource that provides the basis of food production and sustains and delivers several ecosystems services including regulating and supporting services such as water and climate regulation, soil formation and the cycling of nutrients carbon and water. During the last decades, population growth, dietary changes and the subsequent pressure on food production, have caused severe damages on soil quality as a consequence of intensive, high input-based agriculture. While agriculture is supposed to maintain and steward its most important resource base, it compromises soil quality and fertility through its impact on erosion, soil organic matter and biodiversity decline, compaction, etc., and thus the necessary yield increases for the next decades. New or improved cropping systems and agricultural practices are needed to ensure a sustainable use of this resource and to fully take the advantages of its associated ecosystem services. Also, new and better soil quality indicators are crucial for fast and in-field soil diagnosis to help farmers decide on the best management practices to adopt under specific pedo-climatic conditions. Conservation Agriculture and its fundamental principles: minimum (or no) soil disturbance, permanent organic soil cover and crop rotation /intercropping certainly figure among the possibilities capable to guarantee sustainable soil management. The iSQAPER project – Interactive Soil Quality Assessment in Europe and China for Agricultural Productivity and Environmental Resilience – is tackling this problem with the development of a Soil Quality application (SQAPP) that links soil and agricultural management practices to soil quality indicators and will provide an easy-to-use tool for farmers and land managers to judge their soil status. The University of Évora is the leader of WP6 - Evaluating and demonstrating measures to improve Soil Quality. In this work package, several promising soil and agricultural management practices will be tested at selected sites and evaluated using the set of soil quality indicators defined for the SQAPP tool. The project as a whole and WP6 in specific can contribute to proof and demonstrate under different pedoclimatic conditions the impact of Conservation Agriculture practices on soil quality and function as was named the call under which this project was submitted.

Local Understandings of Soil Fertility, Rainfall and Conservation Agriculture in Laikipia, Kenya: A Qualitative Analysis.

Conservation Agriculture (CA) is mostly referred to in the literature as having three principles at the core of its identity: minimum soil disturbance, permanent organic soil cover and crop diversity. This farming package has been described as suitable to improve yields and livelihoods of smallholders in semi-arid regions of Kenya, which since the colonial period have been heavily subjected to tillage. Our study is based on a qualitative approach that followed local meanings and understandings of soil fertility, rainfall and CA in Ethi and Umande located in the semi-arid region of Laikipia, Kenya. Farm visits, 53 semistructured interviews, informal talks were carried out from April to June 2015. Ethi and Umande locations were part of a resettlement programme after the independence of Kenya that joined together people coming from different farming contexts. Since the 1970–80s, state and NGOs have been promoting several approaches to control erosion and boost soil fertility. In this context, CA has also been promoted preferentially since 2007. Interviewees were well acquainted with soil erosion and the methods to control it. Today, rainfall amount and distribution are identified as major constraints to crop performance. Soil fertility is understood as being under control since farmers use several methods to boost it (inorganic fertilisers, manure, terraces, agroforestry, vegetation barriers). CA is recognised to deliver better yields but it is not able to perform well under severe drought and does not provide yields as high as ‘promised’ in promotion campaigns. Moreover, CA is mainly understood as “cultivating with chemicals”, “kulima na dawa”, in kiswahili. A dominant view is that CA is about minimum tillage and use of pre-emergence herbicides. It is relevant to reflect about what kind of CA is being promoted and if elements like soil cover and crop rotation are given due attention. CA based on these two ideas, minimum tillage and use of herbicides, is hard to stand as a programme to be promoted and up-scaled. Therefore CA appears not to be recognised as a convincing approach to improve the livelihoods in Laikipia.

An R package for simulating metapopulation dynamics and range expansion under environmental change

The metapopulation paradigm is central in ecology and conservation biology to understand the dynamics of spatially-structured populations in fragmented landscapes. Metapopulations are often studied using simulation modelling, and there is an increasing demand of user-friendly software tools to simulate metapopulation responses to environmental change. Here we describe the MetaLandSim R package, mwhich integrates ideas from metapopulation and graph theories to simulate the dynamics of real and virtual metapopulations. The package offers tools to (i) estimate metapopulation parameters from empirical data, (ii) to predict variation in patch occupancy over time in static and dynamic landscapes, either real or virtual, and (iii) to quantify the patterns and speed of metapopulation expansion into empty landscapes. MetaLandSim thus provides detailed information on metapopulation processes, which can be easily combined with land use and climate change scenarios to predict metapopulation dynamics and range expansion for a variety of taxa and ecological systems.