Michael J. O. Pocock

The robustness and restoration of a network of ecological networks.

Networks of Networks Quantitative networks, such as those represented by food webs, have become an important way of investigating the structure of ecological communities, but thus far only encompass a small subset of species. Pocock et al. (p. 973) have linked seven different types of ecological networks to form a network of networks. They found that although networks varied in their robustness to species loss, they did not strongly co-vary; that is, what happens to one network is unrelated to what happens to another. The networks studied were identified from an agroecosystem in the southwestern UK, a habitat in which biodiversity has suffered substantially. This study succeeded in revealing which species are potential targets for restoration of ecological function in this and other systems. Analysis of seven interconnected networks on a farm reveals that they vary in their fragility, but that they do not covary. Understanding species’ interactions and the robustness of interaction networks to species loss is essential to understand the effects of species’ declines and extinctions. In most studies, different types of networks (such as food webs, parasitoid webs, seed dispersal networks, and pollination networks) have been studied separately. We sampled such multiple networks simultaneously in an agroecosystem. We show that the networks varied in their robustness; networks including pollinators appeared to be particularly fragile. We show that, overall, networks did not strongly covary in their robustness, which suggests that ecological restoration (for example, through agri-environment schemes) benefitting one functional group will not inevitably benefit others. Some individual plant species were disproportionately well linked to many other species. This type of information can be used in restoration management, because it identifies the plant taxa that can potentially lead to disproportionate gains in biodiversity.

Horizon scanning for invasive alien species with the potential to threaten biodiversity in Great Britain

Invasive alien species (IAS) are considered one of the greatest threats to biodiversity, particularly through their interactions with other drivers of change. Horizon scanning, the systematic examination of future potential threats and opportunities, leading to prioritization of IAS threats is seen as an essential component of IAS management. Our aim was to consider IAS that were likely to impact on native biodiversity but were not yet established in the wild in Great Britain. To achieve this, we developed an approach which coupled consensus methods (which have previously been used for collaboratively identifying priorities in other contexts) with rapid risk assessment. The process involved two distinct phases: Preliminary consultation with experts within five groups (plants, terrestrial invertebrates, freshwater invertebrates, vertebrates and marine species) to derive ranked lists of potential IAS. Consensus-building across expert groups to compile and rank the entire list of potential IAS. Five hundred and ninety-one species not native to Great Britain were considered. Ninety-three of these species were agreed to constitute at least a medium risk (based on score and consensus) with respect to them arriving, establishing and posing a threat to native biodiversity. The quagga mussel, Dreissena rostriformis bugensis, received maximum scores for risk of arrival, establishment and impact; following discussions the unanimous consensus was to rank it in the top position. A further 29 species were considered to constitute a high risk and were grouped according to their ranked risk. The remaining 63 species were considered as medium risk, and included in an unranked long list. The information collated through this novel extension of the consensus method for horizon scanning provides evidence for underpinning and prioritizing management both for the species and, perhaps more importantly, their pathways of arrival. Although our study focused on Great Britain, we suggest that the methods adopted are applicable globally.

Measures of immune function of wild mice, Mus musculus

The immune function of wild animals has been rather little studied. Wild animals’ immune function may differ from that of laboratory bred animals because of their different environments. This idea follows from the concept of resource partitioning in which animals distribute scarce resources to all aspects of life, including to costly immune responses. A logical extension of this idea is that there may be substantial interindividual variation in the immune function of wild animals. To begin to investigate this, we compared the immune function of a laboratory bred mouse strain (C57BL/6, a widely used mouse strain that makes potent immune responses) and wild caught Mus musculus. We found that by most measures of immune function, the wild caught mice had greater immune function. Specifically, wild mice had greater concentrations and more avid antigen‐specific IgG responses, as well as higher concentrations of total IgG and IgE, compared with those laboratory bred mice. Moreover, flow cytometric analysis showed a comparatively greater overall level of activation of the cells of the immune system in wild mice. Lastly, we observed that immune function was substantially more variable among wild caught mice than among the laboratory bred mice. The next research challenge is to understand which aspects of an individual animal’s life determine its immune function.

Pollination by nocturnal Lepidoptera, and the effects of light pollution: a review

1. Moths (Lepidoptera) are the major nocturnal pollinators of flowers. However, their importance and contribution to the provision of pollination ecosystem services may have been under‐appreciated. Evidence was identified that moths are important pollinators of a diverse range of plant species in diverse ecosystems across the world.

Identifying key knowledge needs for evidence-based conservation of wild insect pollinators: A collaborative cross-sectoral exercise

In response to evidence of insect pollinator declines, organisations in many sectors, including the food and farming industry, are investing in pollinator conservation. They are keen to ensure that their efforts use the best available science. We convened a group of 32 ‘conservation practitioners’ with an active interest in pollinators and 16 insect pollinator scientists. The conservation practitioners include representatives from UK industry (including retail), environmental non‐government organisations and nature conservation agencies. We collaboratively developed a long list of 246 knowledge needs relating to conservation of wild insect pollinators in the UK. We refined and selected the most important knowledge needs, through a three‐stage process of voting and scoring, including discussions of each need at a workshop. We present the top 35 knowledge needs as scored by conservation practitioners or scientists. We find general agreement in priorities identified by these two groups. The priority knowledge needs will structure ongoing work to make science accessible to practitioners, and help to guide future science policy and funding. Understanding the economic benefits of crop pollination, basic pollinator ecology and impacts of pesticides on wild pollinators emerge strongly as priorities, as well as a need to monitor floral resources in the landscape.

Networking Agroecology: Integrating the Diversity of Agroecosystem Interactions

Worldwide demand for food will increase dramatically in the future as global human population grows. Increasing efficiency of crop production is unlikely to be sufficient to meet the demand, presenting a long-term threat to humanity’s ‘well-being’. Knowledge of the system-level behaviour of agroecosystems, however, remains surprisingly limited, reflecting the agricultural focus on particular species. This is starting to change towards an ecosystem and network-based approach, following the recent revolution in thinking about resource use and sustainability in our other global food production industry: commercial fisheries. Agroecosystems appear to retain plasticity of ecological processes that might be manipulated for productivity and sustainability. Network structure and dynamics have substantial impacts on ecosystem performance, but evidence from agroecosystems lags behind network theory. Here, we provide an introduction to network theory and application in agroecosystems, identify network metrics for management and environmental change, and, finally, we highlight gaps in our current knowledge and key research themes. These themes include: is the structure of agroecological networks affected by sampling; how do ecosystem services ‘emerge’ empirically from ecological organization, function and network properties; how do spatial and temporal scale and resolution influence system performance; and, can network agroecology be used to design systems that maximize ecosystem services?

The robustness of a network of ecological networks to habitat loss.

There have been considerable advances in our understanding of the tolerance of species interaction networks to sequential extinctions of plants and animals. However, communities of species exist in a mosaic of habitats, and the vulnerability of habitats to anthropogenic change varies. Here, we model the cascading effects of habitat loss, driven by plant extinctions, on the robustness of multiple animal groups. Our network is constructed from empirical observations of 11 animal groups in 12 habitats on farmland. We simulated sequential habitat removal scenarios: randomly; according to prior information; and with a genetic algorithm to identify best- and worst-case permutations of habitat loss. We identified two semi-natural habitats (waste ground and hedgerows together comprising < 5% of the total area of the farm) as disproportionately important to the integrity of the overall network. Our approach provides a new tool for network ecologists and for directing the management and restoration of multiple-habitat sites.

FORUM: Ecological networks: the missing links in biomonitoring science

Summary Monitoring anthropogenic impacts is essential for managing and conserving ecosystems, yet current biomonitoring approaches lack the tools required to deal with the effects of stressors on species and their interactions in complex natural systems. Ecological networks (trophic or mutualistic) can offer new insights into ecosystem degradation, adding value to current taxonomically constrained schemes. We highlight some examples to show how new network approaches can be used to interpret ecological responses. Synthesis and applications. Augmenting routine biomonitoring data with interaction data derived from the literature, complemented with ground‐truthed data from direct observations where feasible, allows us to begin to characterise large numbers of ecological networks across environmental gradients. This process can be accelerated by adopting emerging technologies and novel analytical approaches, enabling biomonitoring to move beyond simple pass/fail schemes and to address the many ecological responses that can only be understood from a network‐based perspective.

Networking our way to better ecosystem service provision

The ecosystem services (EcoS) concept is being used increasingly to attach values to natural systems and the multiple benefits they provide to human societies. Ecosystem processes or functions only become EcoS if they are shown to have social and/or economic value. This should assure an explicit connection between the natural and social sciences, but EcoS approaches have been criticized for retaining little natural science. Preserving the natural, ecological science context within EcoS research is challenging because the multiple disciplines involved have very different traditions and vocabularies (common-language challenge) and span many organizational levels and temporal and spatial scales (scale challenge) that define the relevant interacting entities (interaction challenge). We propose a network-based approach to transcend these discipline challenges and place the natural science context at the heart of EcoS research.

Merging DNA metabarcoding and ecological network analysis to understand and build resilient terrestrial ecosystems

Summary 1. Significant advances in both mathematical and molecular approaches in ecology offer unprecedented opportunities to describe and understand ecosystem functioning. Ecological networks describe interactions between species, the underlying structure of communities and the function and stability of ecosystems. They provide the ability to assess the robustness of complex ecological communities to species loss, as well as a novel way of guiding restoration. However, empirically quantifying the interactions between entire communities remains a significant challenge. 2. Concomitantly, advances in DNA sequencing technologies are resolving previously intractable questions in functional and taxonomic biodiversity and provide enormous potential to determine hitherto difficult to observe species interactions. Combining DNA metabarcoding approaches with ecological network analysis presents important new opportunities for understanding large-scale ecological and evolutionary processes, as well as providing powerful tools for building ecosystems that are resilient to environmental change. 3. We propose a novel ‘nested tagging’ metabarcoding approach for the rapid construction of large, phylogenetically structured species-interaction networks. Taking tree–insect–parasitoid ecological networks as an illustration, we show how measures of network robustness, constructed using DNA metabarcoding, can be used to determine the consequences of tree species loss within forests, and forest habitat loss within wider landscapes. By determining which species and habitats are important to network integrity, we propose new directions for forest management. 4. Merging metabarcoding with ecological network analysis provides a revolutionary opportunity to construct some of the largest, phylogenetically structured species-interaction networks to date, providing new ways to: (i) monitor biodiversity and ecosystem functioning; (ii) assess the robustness of interacting communities to species loss; and (iii) build ecosystems that are more resilient to environmental change.