Morphology and phenology influence plant–pollinator network structure, but whether they generate more stable pairwise interactions with higher pollination success remains unknown. Here we evaluate ...the importance of morphological trait matching, phenological overlap and specialisation for the spatio‐temporal stability (measured as variability) of plant–pollinator interactions and for pollination success, while controlling for species' abundance. To this end, we combined a 6‐year plant–pollinator interaction dataset, with information on species traits, phenologies, specialisation, abundance and pollination success, into structural equation models. Interactions among abundant plants and pollinators with well‐matched traits and phenologies formed the stable and functional backbone of the pollination network, whereas poorly matched interactions were variable in time and had lower pollination success. We conclude that phenological overlap could be more useful for predicting changes in species interactions than species abundances, and that non‐random extinction of species with well‐matched traits could decrease the stability of interactions within communities and reduce their functioning.
Morphology and phenology influence plant‐pollinator network structure, but whether they generate more stable pairwise interactions with higher pollination success is unknown. We show that interactions among abundant plants and pollinators with well‐matched traits and phenologies formed the stable and functional backbone of the pollination network, whereas poorly‐matched interactions were variable in time and had lower pollination success.
Biological invasions are a major driver of ecosystem change but causes of variation in their environmental impacts over space and time remain poorly understood. Most approaches used to quantify the ...impacts of non‐native species assume there are interactions among per capita (i.e. individual level) effects, species abundance and the area occupied by the species. However, studies rarely evaluate these factors and their interactions and often fail to recognize that the magnitude of impacts can be highly context dependent. Understanding what drives the context dependence of non‐native species impacts can improve our understanding and predictions of ecosystem change and better inform options for mitigation.
Conifers, especially pines, are among the most problematic non‐native plant species globally. We use Pinaceae to illustrate how context dependence in biodiversity and environmental impacts of non‐native plant species can be generated by at least four processes: nonlinear density effects; intraspecific variation in functional traits; shifts in impacts over time; and persistence of impacts as biological or ecosystem legacies following non‐native species removal. Using this understanding, we develop a framework to better quantify interactions of impacts along environmental gradients (e.g. soil fertility, climate, ecosystem age).
We demonstrate how impacts of non‐native species can occur at both low and high density, and that failing to account for intraspecific variation in effect traits can lead to significant errors in the prediction of impacts. By incorporating context dependence in regard to density and functional traits, we can measure how the interaction of this context dependence will shift along environmental gradients. Moreover, disentangling the roles of species and abundance along such gradients will provide new insights into the net effects of both the native and non‐native components of communities. We use a working example of our framework that incorporates all four processes to demonstrate how to measure fire risk impacts of Pinus contorta.
We show that ecosystem impacts of non‐native tree species are not fixed but rather vary predictably along major environmental gradients. Moreover, removal of non‐native species through management provides an important tool for revealing biological and ecosystem legacy effects. Although we focus here on relatively well‐documented Pinaceae, the new insights into context dependence of impacts can be widely applied across species, environments and regions.
A free Plain Language Summary can be found within the Supporting Information of this article.
A free Plain Language Summary can be found within the Supporting Information of this article.
Species have strong indirect effects on others, and predicting these effects is a central challenge in ecology. Prey species sharing an enemy (predator or parasitoid) can be linked by apparent ...competition, but it is unknown whether this process is strong enough to be a community-wide structuring mechanism that could be used to predict future states of diverse food webs. Whether species abundances are spatially coupled by enemy movement across different habitats is also untested. Here, using a field experiment, we show that predicted apparent competitive effects between species, mediated via shared parasitoids, can significantly explain future parasitism rates and herbivore abundances. These predictions are successful even across edges between natural and managed forests, following experimental reduction of herbivore densities by aerial spraying of insecticide over 20 hectares. This result shows that trophic indirect effects propagate across networks and habitats in important, predictable ways, with implications for landscape planning, invasion biology and biological control.
Different modelling approaches have been used to relate the structure of mutualistic interactions with the stability of communities. However, inconsistencies arise when we compare modelling outcomes ...with the patterns of interactions observed in empirical studies. To shed light on these inconsistencies, we explored the network structure–stability relationship by incorporating the cost of mutualistic interactions, a long ignored feature of mutualisms, into population dynamics models. We assessed the changes in the relationship between network structure (species richness, connectance, modularity) and community stability (species persistence, resilience), and between network structure and community structural attributes (average abundance), using models with increasing levels of cost for mutualistic communities. We found that adding the potential cost of mutualistic interactions affected the strength of the network structure–stability relationship. Our results revive the question of whether the structure of mutualistic networks determines community stability.
The environmental filtering of species traits can influence the identity of their interaction partners and the contribution of species interactions to ecosystem functioning, but the extent to which ...this process is influenced by landscape composition and configuration remains unclear. We combined a field experiment with an agent‐based model to assess how landscape structure and local flower patch isolation affect pollinator body‐size distribution and plant–pollinator interactions, sampled at different spatial extents. We then evaluated how these changes in pollinator functional (i.e. body‐size) diversity influence plant reproduction. We observed higher pollinator functional diversity in less‐isolated patches, which promoted plant reproduction via a relationship between functional diversity and interaction complementarity. This complementarity occurred partly because larger pollinators interacted with more plant species. Moreover, we showed that patch configuration at the landscape level can change the direction of these local‐scale patch isolation effects on pollinator body‐size distribution, functional diversity and plant–pollinator interactions. Importantly, these relationships were robust to sampling spatial extent. Thus, management strategies to promote pollination should account for local resources and landscape structure, because response, effect and interaction traits like body size connect landscape filtering effects with local community responses and outcomes of interaction‐based functions.
Biological pest control (i.e. 'biocontrol') agents can have direct and indirect non-target impacts, and predicting these effects (especially indirect impacts) remains a central challenge in ...biocontrol risk assessment. The analysis of ecological networks offers a promising approach to understanding the community-wide impacts of biocontrol agents (via direct and indirect interactions). Independently, species traits and phylogenies have been shown to successfully predict species interactions and network structure (alleviating the need to collect quantitative interaction data), but whether these approaches can be combined to predict indirect impacts of natural enemies remains untested. Whether predictions of interactions (i.e. direct effects) can be made equally well for generalists vs. specialists, abundant vs. less abundant species, and across different habitat types is also untested for consumer-prey interactions. Here, we used two machine-learning techniques (random forest and k-nearest neighbour; KNN) to test whether we could accurately predict empirically-observed quantitative host-parasitoid networks using trait and phylogenetic information. Then, we tested whether the accuracy of machine-learning-predicted interactions depended on the generality or abundance of the interacting partners, or on the source (habitat type) of the training data. Finally, we used these predicted networks to generate predictions of indirect effects via shared natural enemies (i.e. apparent competition), and tested these predictions against empirically observed indirect effects between hosts. We found that random-forest models predicted host-parasitoid pairwise interactions (which could be used to predict attack of non-target host species) more successfully than KNN. This predictive ability depended on the generality of the interacting partners for KNN models, and depended on species' abundances for both random-forest and KNN models, but did not depend on the source (habitat type) of data used to train the models. Further, although our machine-learning informed methods could significantly predict indirect effects, the explanatory power of our machine-learning models for indirect interactions was reasonably low. Combining machine-learning and network approaches provides a starting point for reducing risk in biocontrol introductions, and could be applied more generally to predicting species interactions such as impacts of invasive species.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Success of invasive non-native plant species management is usually measured as changes in the abundance of the invasive plant species or native plant species following invader management, but more ...complex trophic responses to invader removal are often ignored or assumed. Moreover, the effects of invader removal at different stages of the invasion process is rarely evaluated, despite a growing recognition that invader impacts are density or stage-dependent. Therefore, the effectiveness of invasive species management for restoring community structure and function across trophic levels remains poorly understood. We determined how soil nematode diversity and community composition respond to removal of the globally invasive tree species Pinus contorta at different stages of invasion by reanalysing and expanding an earlier study including uninvaded vegetation (seedlings removed continuously), early invader removal (saplings removed), late removal (trees removed), and no removal (invaded). These treatments allowed us to evaluate the stage-dependent belowground trophic responses to biological invasion and removal. We found that invaded plots had half the nematode taxa richness compared to uninvaded plots, and that tree invasion altered the overall composition of the nematode community. Differences in nematode community composition between uninvaded nematode communities and those under the tree removal strategy tended to dilute higher up the food chain, whereas the composition of uninvaded vs. sapling removal strategies did not differ significantly. Conversely, the composition of invaded compared to uninvaded nematode communities differed across all trophic levels, altering the community structure and function. Specifically, invaded communities were structurally simplified compared to uninvaded communities, and had a higher proportion of short life cycle nematodes, characteristic of disturbed environments. We demonstrate that a shift in management strategies for a globally invasive tree species from removing trees to earlier removal of saplings is needed for maintaining the composition and structure of soil nematode communities to resemble uninvaded conditions.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
We estimated home remedy use (HRU) prevalence and associated factors in adults who present symptoms, disease, or accidents using the National Household Survey 2019. The estimation was performed in a ...population that did not access a health care facility. We conducted an analytical cross-sectional study in adults over 18 years of age. The dependent variable was HRU (Yes/No) as the main reason for not going to health care facilities. We collected these variables: age, sex, education, marital status, ethnicity, region of residence, chronic diseases or disability, and health insurance. The HRU prevalence was associated with older participants, who lived in the highlands or the jungle, belonged to Quechua or Aymara ethnic groups, and had comprehensive health insurance. In contrast, there was a lower HRU prevalence for those enrolled in private insurance. The HRU was associated with various socio-demographic factors in adults with any symptoms, illness, or accidents not attending health centers.
Species coexisting in ecological communities interact in multiple ways to form complex networks. We review the growing literature on ecological interaction networks to address several key issues ...about this conceptual and methodological approach. We start by asking the most basic question: Why study networks and whether a network approach is (or is not) useful to understand the ecology of interacting species, the functioning and stability of the communities they belong to, and their response to global change drivers. We also discuss the multiple meanings of network nodes as individuals, populations and species, the different ways of quantifying node roles, the multiple meanings of links as presence/absence of interactions, per capita interaction strengths and species-level effects, and the available approaches to study networks with different types of interactions. Then, we review the structural patterns emerging in ecological interaction networks and the mechanisms driving network structure and function, identifying both what we already know and the knowledge gaps that we still need to fill in. We also discuss sampling effects and their influence in distorting observed network patterns. Finally, we review how different drivers of global environmental change influence the structure, dynamics and stability of ecological networks. With this review we hope to offer a balanced overview of what we have learned in the study of ecological interaction networks and point to several key avenues of research for the next decade.
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