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Figure 3 from: Ziv G, Beckmann M, Bullock J, Cord A, Delzeit R, Domingo C, Dreßler G, Hagemann N, Masó J, Müller B, Neteler M, Sapundzhieva A, Stoev P, Stenning J, Trajković M, Václavík T (2020) BESTMAP: behavioural, Ecological and Socio-economic Tools for Modelling Agricultural Policy. Research Ideas and Outcomes 6: e52052. https://doi.org/10.3897/rio.6.e52052

Figure 3 The Land Systems Archetypes mapped by Levers et al. (2018) for one of BESTMAP case study areas in Catalonia. Land Systems Archetypes were derived at 3km resolution for Europe based on CORINE land cover, downscaled ecological and economic data. BESTMAP aims to map Farming System Archetypes (FSAs) at the farm level.

Figure 7 from: Ziv G, Beckmann M, Bullock J, Cord A, Delzeit R, Domingo C, Dreßler G, Hagemann N, Masó J, Müller B, Neteler M, Sapundzhieva A, Stoev P, Stenning J, Trajković M, Václavík T (2020) BESTMAP: behavioural, Ecological and Socio-economic Tools for Modelling Agricultural Policy. Research Ideas and Outcomes 6: e52052. https://doi.org/10.3897/rio.6.e52052

Figure 7 BESTMAP workflow to map FSAs, defined as having a characteristic bundle of ESS (and dis-services e.g. pollution), biodiversity, socio-economic and behavioural characteristics. Geospatial datasets (the Case Study Base Layer) will be used to approximate these FSAs (the "Proto-FSAs" clusters) in the first step; followed by interviews, field data collection and modelling to assign values on ESS, biodiversity, socio-economic aspects and behaviour for a stratified sample of proto-FSAs farms. The proto-FSAs will likely share "socio-economic-environmental" space and will be split/merged to minimize (but not eliminate) that overlap by changing the clustering in Case Study Base Layer space, defining the final FSAs and their spatial distribution.

Figure 9 from: Ziv G, Beckmann M, Bullock J, Cord A, Delzeit R, Domingo C, Dreßler G, Hagemann N, Masó J, Müller B, Neteler M, Sapundzhieva A, Stoev P, Stenning J, Trajković M, Václavík T (2020) BESTMAP: behavioural, Ecological and Socio-economic Tools for Modelling Agricultural Policy. Research Ideas and Outcomes 6: e52052. https://doi.org/10.3897/rio.6.e52052

Figure 9 BESTMAP dashboard mock-up, visualizing the impact of the policies through the policy indicators.

Figure 8 from: Ziv G, Beckmann M, Bullock J, Cord A, Delzeit R, Domingo C, Dreßler G, Hagemann N, Masó J, Müller B, Neteler M, Sapundzhieva A, Stoev P, Stenning J, Trajković M, Václavík T (2020) BESTMAP: behavioural, Ecological and Socio-economic Tools for Modelling Agricultural Policy. Research Ideas and Outcomes 6: e52052. https://doi.org/10.3897/rio.6.e52052

Figure 8 BESTMAP ABM concept. Individual farmer's decision-making is modelled using the RAA (orange elements). Each farmer agent has an internal state that is characterized by goals & needs (profit maximization, safety first, risk aversion, etc.), knowledge (education, farming strategies, beliefs about consequences of different practices, etc.), values (social norms, strength of beliefs, value of conservation or biodiversity, etc.) and assets (monetary resources, production means, information, etc.). These shape farmer's attitude towards different behaviours (e.g. pro or contra organic farming), perceived norm (social pressure or other farmer's behaviour) and perceived behavioural control (agent's capacity to perform certain behaviours). Every farmer agent can perceive the state of the farming system (e.g. yields, ESS provision), the behaviour of other farmers as well as exogenous drivers (e.g. price or policy changes). Evaluating these lead to changes in attitudes or perceived norm, which can expand or reduce its behavioural options. Each behavioural option represents an intention to perform a certain behaviour (e.g. adopting a different crop choice or setting aside land). Using a multi-objective utility function, the farmer agent will select a specific behavioural option that will change the state of its farm's fields and provide some economic benefit to the farmer. Specifically, farmer's decisions can lead to changes in cropping system, farm and field size, or in farm ownership, which allows us to analyse/model structural change.

Figure 4 from: Ziv G, Beckmann M, Bullock J, Cord A, Delzeit R, Domingo C, Dreßler G, Hagemann N, Masó J, Müller B, Neteler M, Sapundzhieva A, Stoev P, Stenning J, Trajković M, Václavík T (2020) BESTMAP: behavioural, Ecological and Socio-economic Tools for Modelling Agricultural Policy. Research Ideas and Outcomes 6: e52052. https://doi.org/10.3897/rio.6.e52052

Figure 4 BESTMAP conceptual framework for policy impact assessment modelling, combining actions at EU scale (green), and activities within a representative set of case studies (orange; demonstrated in five areas within BESTMAP). The framework combines workshops (ovals), modelling (rounded rectangles) and interviews (ovals) producing several datasets (curved rectangles) and an online interactive dashboard (barrel shape). At the EU level, BESTMAP will co-design with policy-makers and stakeholders policy scenarios which, with existing scenarios of climate change and other global events, will input into the global economic models (here DART-BIO CGE model, but also MAGNET, CAPRI etc.). Georeferenced data layers ("Case Study Base Layers"; CBL) will be collated from existing sources including the Land Parcel Identification System (LPIS), Farm Accountancy Data Network (FADN), CORINE land-use/land cover, INSPIRE Geoportal, Copernicus Land Monitoring, national/regional datasets and existing remote sensing products. This will be used to define 'prototype' Farm System Archetypes (FSAs) which help stratify and design the interview campaign in each of the case studies. BESTMAP will collect demographic, behavioural characteristics and socio-economic information using semi-structured interviews (with harmonized protocols). Ecosystem services models (here the InVEST biophysically based models, but simpler e.g. capacity matrix or more complex e.g. SWAT models can in be used in the future) will estimate ESS, environmental/climatic impacts and biodiversity provided by different farming units. The 'bundle' of ESS, geo-statistically modelled socio-economics and behavioural characteristics will add to the CBL and define the final typology of FSAs in the case study. A generic ABM template based on the RAA behavioural theory will be locally adapted by the same interviews/surveys, and driven by the outputs (prices and costs of commodities, energy etc.) from the global economic model (DART-BIO) as well as narratives and drivers arising from a national/regional workshop interpreting the EU-level policy. The change in FSAs resulting from those scenarios via the ABM will translate to change in ESS and socio-economics defining the FSA 'bundles' including their uncertainty. Those impacts at the case study level will be translated into stakeholders-defined policy indicators (e.g. of the SDGs) and visualized using a standard interactive web-based data portal policy dashboard, the use of which will be the focus of training and dissemination activities.

Figure 1 from: Ziv G, Beckmann M, Bullock J, Cord A, Delzeit R, Domingo C, Dreßler G, Hagemann N, Masó J, Müller B, Neteler M, Sapundzhieva A, Stoev P, Stenning J, Trajković M, Václavík T (2020) BESTMAP: behavioural, Ecological and Socio-economic Tools for Modelling Agricultural Policy. Research Ideas and Outcomes 6: e52052. https://doi.org/10.3897/rio.6.e52052

Figure 1 Top-level conceptual framework of BESTMAP. BESTMAP combining existing global/EU scale (green) models used by the EC, in particular Partial Equilibrium (PE) and Computable General Equilibrium (CGE) models with regional analyses (yellow) by formalizing engagement with stakeholders to define scenarios; using existing geospatial data and empirical data collection to map farming systems based on a novel concept of Farming System Archetypes (FSA) namely farms with a characteristic bundle of ESS, biodiversity, socio-economics and behavioural characteristics of decision-making agents (i.e. farmers); linking economic (typically global) large scale economic based PE/CGE to agent-based models; describing changes in FSAs's distribution, ESS, biodiversity and socio-economics in a representative sample of case study areas.

Figure 5 from: Ziv G, Beckmann M, Bullock J, Cord A, Delzeit R, Domingo C, Dreßler G, Hagemann N, Masó J, Müller B, Neteler M, Sapundzhieva A, Stoev P, Stenning J, Trajković M, Václavík T (2020) BESTMAP: behavioural, Ecological and Socio-economic Tools for Modelling Agricultural Policy. Research Ideas and Outcomes 6: e52052. https://doi.org/10.3897/rio.6.e52052

Figure 5 BESTMAP demonstration case studies: overview of CS locations in the context of Land Systems Archetypes mapped by Levers et al. 2018 at 3km resolution for Europe. No land system archetypes shown for Serbia as the mapping included EU countries only. Insets show the exact boundaries of CS areas.

Figure 2 from: Ziv G, Beckmann M, Bullock J, Cord A, Delzeit R, Domingo C, Dreßler G, Hagemann N, Masó J, Müller B, Neteler M, Sapundzhieva A, Stoev P, Stenning J, Trajković M, Václavík T (2020) BESTMAP: behavioural, Ecological and Socio-economic Tools for Modelling Agricultural Policy. Research Ideas and Outcomes 6: e52052. https://doi.org/10.3897/rio.6.e52052

Figure 2 The conceptualization of Farming System Archetypes (FSAs) in BESTMAP is an extension of land-use intensity framework from Erb et al. 2013 to include the land manager/farmer and its behavioural characteristics (based here on the RAA). FSA have a typical 'bundle' of ESS, outputs, outcomes, inputs and farmer characteristics.

opencc-zeroDec 2017View details →

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Data from: Ecological conditions alter cooperative behaviour and its costs in a chemically defended sawfly

The evolution of cooperation and social behaviour is often studied in isolation from the ecology of organisms. Yet, the selective environment under which individuals evolve is much more complex in nature, consisting of ecological and abiotic interactions in addition to social ones. Here we measured the life-history costs of cooperative chemical defence in a gregarious social herbivore, Diprion pini pine sawfly larvae, and how these costs vary under different ecological conditions. We ran a rearing experiment where we manipulated diet (resin content) and attack intensity by repeatedly harassing larvae to produce a chemical defence. We show that forcing individuals to allocate more to cooperative defence (high attack intensity) incurred a clear cost by decreasing individual survival and potency of chemical defence. Cooperative behaviour and the magnitude of its costs were further shaped by host plant quality. Number of individuals participating in group defence, immune responses, and female growth decreased on a high resin diet under high attack intensity. We also found some benefits of cheating: non-defending males had higher growth rates across treatments. Together these results suggest that ecological interactions can shape the adaptive value of cooperative behaviour and maintain variation in the frequency of cooperation and cheating.

opencc-by-4.0Feb 2023View details →

Figure 8 in Chrysoritis Butler (Papilionoidea: Lycaenidae: Aphnaeinae) - Part II: Natural history: morphology, ecology, and behaviour, with accounts of larval ecology and insights into the aphytophagous C. dicksoni (Gabriel)

Figure 8 – Lateral setae of 1st instars. (a) C. blencathrae (Waaihoek), (b) C. plutus (Kango, Oudtshoorn).

opencc-by-4.0Feb 2023View details →

Figure 4 in Chrysoritis Butler (Papilionoidea: Lycaenidae: Aphnaeinae) - Part II: Natural history: morphology, ecology, and behaviour, with accounts of larval ecology and insights into the aphytophagous C. dicksoni (Gabriel)

Figure 4 – Micrographs of wing scales taken at two magnifications under dorso-lateral lighting, intending to invoke iridescence if any. Images show the centre of the right hind wing (between veins Rs and M1). (A) & (B): C. violescens, an iridescent species. (C) & (D): C. aridus, a non iridescent species. (E) & (F): C. amatola stat. nov. a non iridescent species. Sample and location information as in previous figure. Bottom images show adult males of C. amatola & C. violescens. Images A–F by S. van Noort.

opencc-by-4.0Feb 2023View details →

Figure 14 – C in Chrysoritis Butler (Papilionoidea: Lycaenidae: Aphnaeinae) - Part II: Natural history: morphology, ecology, and behaviour, with accounts of larval ecology and insights into the aphytophagous C. dicksoni (Gabriel)

Figure 14 – C. dicksoni final instar with Crematogaster peringueyi ant attending the DNO. Eggs of C. dicksoni are shown in Fig. 7D.

opencc-zeroMar 2022View details →

Supplementary material 1 from: Ferreira EM, Valerio F, Medinas D, Fernandes N, Craveiro J, Costa P, Silva JP, Carrapato C, Mira A, Santos SM (2022) Assessing behaviour states of a forest carnivore in a road-dominated landscape using Hidden Markov Models. In: Santos S, Grilo C, Shilling F, Bhardwaj M, Papp CR (Eds) Linear Infrastructure Networks with Ecological Solutions. Nature Conservation 47: 155-175. https://doi.org/10.3897/natureconservation.47.72781

Figures S1–S3

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Disparate movement behaviour and feeding ecology in sympatric ecotypes of Atlantic cod

Co-existence of ecotypes, genetically divergent population units, is a widespread phenomenon, potentially affecting ecosystem functioning and local food web stability. In coastal Skagerrak, Atlantic cod (Gadus morhua) occur as two such co-existing ecotypes. We applied a combination of acoustic telemetry, genotyping and stable isotope analysis to 72 individuals to investigate movement ecology and food niche of putative local "Fjord" and putative oceanic "North Sea" ecotypes – thus named based on previous molecular studies. Genotyping and individual origin assignment suggested 41 individuals were Fjord and 31 were North Sea ecotypes. Both ecotypes were found throughout the fjord. Seven percent of Fjord ecotype individuals left the study system during the study while 42 % of North Sea individuals left, potentially homing to natal spawning grounds. Home range sizes were similar for the two ecotypes but highly variable among individuals. Fjord ecotype cod had significantly higher δ13C and δ15N stable isotope values than North Sea ecotype cod, suggesting they exploited different food niches. The results suggest coexisting ecotypes may possess innate differences in feeding- and movement ecologies and may thus fill different functional roles in marine ecosystems. This highlights the importance of conserving interconnected populations to ensure stable ecosystem functioning and food web structures.

opencc-zeroJun 2022View details →

Fig. 10 in Impact of ecotourism on the fish fauna of Bonito region (Mato Grosso do Sul State, Brazil): ecological, behavioural and physiological measures

Fig. 10. Temporal variation (mean and SEM) of cortisol levels in holding-water Moenkhausia bonita challenged with an intra-peritoneal injection of porcine ACTH. Triangles, darker line = ACTH; squares, lighter line = RINGER (control).

Fig. 5 in Impact of ecotourism on the fish fauna of Bonito region (Mato Grosso do Sul State, Brazil): ecological, behavioural and physiological measures

Fig. 5. Variation of behaviour patterns between No Tourism and Tourism sites for Crenicichla lepidota (mean and SEM); (a) Feeding; (b) Agonistic activity; (c) Escape behaviour. Lighter bars = No Tourism site; darker bars = Tourism site. (Mann-Whitney U-test). N= 35; * p <0.05; ** p <0.01; ***p <0.0001.

Fig. 6 in Impact of ecotourism on the fish fauna of Bonito region (Mato Grosso do Sul State, Brazil): ecological, behavioural and physiological measures

Fig. 6. Variation of behaviour patterns between before (8h00) and after (9h00) the first disturbance of tourists in the river (mean and SEM) for Crenicichla lepidota. (a) Tourism; (b) No Tourism. Lighter bars = 8h00; darker bars = 9h00 (Mann-Whitney U-test). N = 35; *p <0.05; **p <0.01;***p <0.0001.

Fig. 7 in Impact of ecotourism on the fish fauna of Bonito region (Mato Grosso do Sul State, Brazil): ecological, behavioural and physiological measures

Fig. 7. Variation of behaviour patterns between No Tourism and Tourism site for M. bonita (mean and SEM); (a) Feeding; (b) Escape. Lighter bars = No Tourism; darker bars = Tourism. (Mann -Whitney U-test). N= 70; *p <0.05; **p <0.01; ***p <0.0001.

Fig. 4 in Impact of ecotourism on the fish fauna of Bonito region (Mato Grosso do Sul State, Brazil): ecological, behavioural and physiological measures

Fig. 4. Multidimensional scaling ordinations showing local differences in (a) No Tourism site and (b) Tourism site. Each individual point represents a replicate sample (census). Circles = No Tourism site; Triangles = Tourism site.