The intrinsic vulnerability of networks to epidemics

Contact networks are convenient models to investigate epidemics, with nodes and links representing potential hosts and infection pathways, respectively. The outcomes of outbreak simulations on networks are driven both by the underlying epidemic model, and by the networks’ structural properties, so that the same pathogen can generate different epidemic dynamics on different networks. Here we ask whether there are general properties that make a contact network intrinsically vulnerable to epidemics (that is, regardless of specific epidemiological parameters). By conducting simulations on a large set of modelled networks, we show that, when a broad range of network topologies is taken into account, the effect of specific network properties on outbreak magnitude is stronger than that of fundamental pathogen features such as transmission rate, infection duration, and immunization ability. Then, by focusing on a large set of real world networks of the same type (potential contacts between field voles, Microtus agrestis), we showed how network structure can be used to accurately assess the relative, intrinsic vulnerability of networks towards a specific pathogen, even when those have limited topological variability. These results have profound implications for how we prevent disease outbreaks; in many real world situations, the topology of host contact networks can be described and used to infer intrinsic vulnerability. Such an approach can increase preparedness and inform preventive measures against emerging diseases for which limited epidemiological information is available, enabling the identification of priority targets before an epidemic event

Functional traits predict species co-occurrence patterns in a North American Odonata metacommunity

The probability of occurrence of a given species in a target locality and assemblage is conditioned not only by environmental/climatic variables but also by the presence of other species (i.e., species co-occurrence). This framework, already complex in nature, becomes even more complicated if one considers the functional traits of species that, in turn, might influence the structure of metacommunities in various ways. Depending on the ecological and environmental setting, functional similarity (i.e., convergence in morphological and ecological traits) between species might either reduce their co-occurrence due to high niche overlap driving negative interactions or promote it if the similar traits are associated with local habitat suitability. Similarly, functional divergence might either promote species co-occurrence by limiting negative interactions through niche separation or reduce it through trait mediated environmental filtering. Therefore, discriminating between these alternative scenarios—predicting whether two species will tend to co-occur or not based on their traits—is extremely challenging. Here, we develop a novel protocol to tackle the challenge, and we demonstrate its effectiveness by showing that ecological species traits can predict species co-occurrence in a large dataset of North American Odonata. To this end, we first used the Hierarchical Modeling of Species Communities framework to quantify the pairwise species co-occurrence after controlling for environmental and climatic factors. Then, we used machine learning to generate models which proved capable of predict accurately the observed co-occurrence patterns from species functional traits. Our approach offers a generalizable analytical framework with the potential to clarify long-standing ecological questions

Monogenoidean parasites of fishes associated with coral reefs in the Ras Mohammed National Park, Egypt: preliminary results

AbstractA parasitological survey of the monogenoids of 14 species of common fishes associated with the local coral reefs of the Ras Mohammed National Park, National Parks of Egypt South Sinai Sector, Egypt, was carried out from May 2003 to May 2005. The monogenoids collected during the survey included 17 species: 8 previously described species, 7 new species in established genera, and 2 new species belonging to new genera

Small room for compromise between oil palm cultivation and primate conservation in Africa

Despite growing awareness about its detrimental effects on tropical biodiversity, land conversion to oil palm continues to increase rapidly as a consequence of global demand, profitability, and the income opportunity it offers to producing countries. Although most industrial oil palm plantations are located in Southeast Asia, it is argued that much of their future expansion will occur in Africa. We assessed how this could affect the continent’s primates by combining information on oil palm suitability and current land use with primate distribution, diversity, and vulnerability. We also quantified the potential impact of large-scale oil palm cultivation on primates in terms of range loss under different expansion scenarios taking into account future demand, oil palm suitability, human accessibility, carbon stock, and primate vulnerability. We found a high overlap between areas of high oil palm suitability and areas of high conservation priority for primates. Overall, we found only a few small areas where oil palm could be cultivated in Africa with a low impact on primates (3.3 Mha, including all areas suitable for oil palm). These results warn that, consistent with the dramatic effects of palm oil cultivation on biodiversity in Southeast Asia, reconciling a large-scale development of oil palm in Africa with primate conservation will be a great challenge

Assessing the potential distribution of insect pests: case studies on large pine weevil (Hylobius abietis L) and horse-chestnut leaf miner (Cameraria ohridella) under present and future climate conditions in European forests†

Forest insect pests represent a serious threat to European forests and their negative effects could be exacerbated by climate change. This paper illustrates how species distribution modelling integrated with host tree species distribution data can be used to assess forest vulnerability to this threat. Two case studies are used: large pine weevil (Hylobius abietis L) and horse-chestnut leaf miner (Cameraria ohridella Deschka & Dimic) both at pan-European level. The proposed approach integrates information from different sources. Occurrence data of insect pests were collected from the Global Biodiversity Information Facility (GBIF), climatic variables for present climate and future scenarios were sourced, respectively, from WorldClim and from the Research Program on Climate Change, Agriculture and Food Security (CCAFS), and distributional data of host tree species were obtained from the European Forest Data Centre (EFDAC), within the Forest Information System for Europe (FISE). The potential habitat of the target pests was calculated using the machine learning algorithm of Maxent model. On the one hand, the results highlight the potential of species distribution modelling as a valuable tool for decision makers. On the other hand, they stress how this approach can be limited by poor pest data availability, emphasizing the need to establish a harmonised open European database of geo-referenced insect pest distribution data. Evaluation de la repartition potentielle des insectes nuisibles: etudes de cas sur le grand charancon du pin (Hylobius abietis L.) et sur la mineuse du marronnier (Cameraria ohridella) dans les conditions climatiques actuelles et futures dans les forets europeennes Les insectes nuisibles des forets representent une menace serieuse pour les forets europeennes et leurs effets negatifs pourraient etre aggraves par le changement climatique. Cet article illustre l'utilisation de la modelisation de la repartition des especes, integree aux donnees de repartition des arbres-hotes, pour evaluer la vulnerabilite des forets a cette menace. Deux etudes de cas sont utilisees, toutes deux au niveau paneuropeen, pour le grand charancon du pin (Hylobius abietis L.) et la mineuse du marronnier (Cameraria ohridella Deschka & Dimic). L'approche proposee utilise des informations de differentes sources. Les donnees sur la presence des insectes nuisibles proviennent du service mondial d'information sur la biodiversite ('Global Biodiversity Information Facility', GBIF), les variables climatiques pour le climat actuel et des scenarios futurs ont ete obtenues, respectivement, a partir de WorldClim et du Programme de recherche sur le changement climatique, l'agriculture et la securite alimentaire (CCAFS), et les donnees sur la repartition des arbres-hotes ont ete obtenues aupres du Centre europeen de donnees sur les forets (EFDAC), qui fait partie du systeme d'information forestiere pour l'Europe ('Forest Information System for Europe', FISE). L'habitat potentiel des ravageurs etudies a ete calcule en utilisant l'algorithme d'apprentissage automatique du modele Maxent. D'une part, les resultats indiquent que la modelisation de la repartition des especes peut devenir un outil precieux pour les decideurs. D'autre part, ils indiquent que cette approche peut etre limitee par le manque de donnees sur les organismes nuisibles, renforcant ainsi la necessite de creer une base de donnees europeenne harmonisee et ouverte pour les donnees geo-referencees sur la repartition des insectes nuisibles. Oцeнкa пoтeнциaльнoгo pacпpocтpaнeния вpeдныx нaceкoмыx нa пpимepe бoльшoгo cocнoвoгo дoлгoнocикa (Hylobius abietis L) и лиcтoвoгo минёpa кoнcкoгo кaштaнa (Cameraria ohridella) пpи cyщecтвyющиx и бyдyщиx климaтичecкиx ycлoвияx в eвpoпeйcкиx лeca

Unveiling the complexity and ecological function of aquatic macrophyte–animal networks in coastal ecosystems

Network theory offers innovative tools to explore the complex ecological mechanisms regulating species associations and interactions. Although interest in ecological networks has grown steadily during the last two decades, the application of network approaches has been unequally distributed across different study systems: while some kinds of interactions (e.g. plant-pollinator and host-parasite) have been extensively investigated, others remain relatively unexplored. Among the latter, aquatic macrophyte-animal associations in coastal environments have been largely neglected, despite their major role in littoral ecosystems. The ubiquity of macrophyte systems, their accessibility and multi-faceted ecological, economical and societal importance make macrophyte-animal systems an ideal subject for ecological network science. In fact, macrophyte-animal networks offer an aquatic counterpart to terrestrial plant-animal networks. In this review, we show how the application of network analysis to aquatic macrophyte-animal associations has the potential to broaden our understanding of how coastal ecosystems function. Network analysis can also provide a key to understanding how such ecosystems will respond to on-going and future threats from anthropogenic disturbance and environmental change. For this, we: (i) identify key issues that have limited the application of network theory and modelling to aquatic animal-macrophyte associations; (ii) illustrate through examples based on empirical data how network analysis can offer new insights on the complexity and functioning of coastal ecosystems; and (iii) provide suggestions for how to design future studies and establish this new research line into network ecology

A network approach for managing ecosystem services and improving food and nutrition security on smallholder farms

Smallholder farmers are some of the poorest and most food insecure people on Earth. Their high nutritional and economic reliance on home-grown produce makes them particularly vulnerable to environmental stressors such as pollinator loss or climate change which threaten agricultural productivity. Improving smallholder agriculture in a way that is environmentally sustainable and resilient to climate change is a key challenge of the 21st century. Ecological intensification, whereby ecosystem services are managed to increase agricultural productivity, is a promising solution for smallholders. However, smallholder farms are complex socio-ecological systems with a range of social, ecological and environmental factors interacting to influence ecosystem service provisioning. To truly understand the functioning of a smallholder farm and identify the most effective management options to support household food and nutrition security, a holistic, systems-based understanding is required. In this paper, we propose a network approach to understand, visualise and model the complex interactions occurring among wild species, crops and people on smallholder farms. Specifically, we demonstrate how networks may be used to (a) identify wild species with a key role in supporting, delivering or increasing the resilience of an ecosystem service; (b) quantify the value of an ecosystem service in a way that is relevant to the food and nutrition security of smallholders; and (c) understand the social interactions that influence the management of shared ecosystem services. Using a case study based on data from rural Nepal, we demonstrate how this framework can be used to connect wild plants, pollinators and crops to key nutrients consumed by humans. This allows us to quantify the nutritional value of an ecosystem service and identify the wild plants and pollinators involved in its provision, as well as providing a framework to predict the effects of environmental change on human nutrition. Our framework identifies mechanistic links between ecosystem services and the nutrients consumed by smallholder farmers and highlights social factors that may influence the management of these services. Applying this framework to smallholder farms in a range of socio-ecological contexts may provide new, sustainable and equitable solutions to smallholder food and nutrition security. A free Plain Language Summary can be found within the Supporting Information of this article