scholarly journals Indirect Effects of Pesticides on Natural Enemies

10.5772/48649 ◽  
2012 ◽  
Author(s):  
Raymond Cloyd
Keyword(s):  
Natural Enemies ◽  
Indirect Effects

scholarly journals Dwarf Mistletoe Infection Interacts with Tree Growth Rate to Produce Opposing Direct and Indirect Effects on Resin Duct Defenses in Lodgepole Pine

Forests ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 222 ◽  
Author(s):  
Scott Ferrenberg
Keyword(s):  
Growth Rate ◽  
Natural Enemies ◽  
Indirect Effects ◽  
Annual Ring ◽  
Annual Growth ◽  
Tree Age ◽  
Direct And Indirect Effects ◽  
Dwarf Mistletoe ◽  
Resin Ducts ◽  
Resin Duct

Research Highlights: I sought to disentangle the influences of tree age, growth rate, and dwarf mistletoe infection on resin duct defenses in lodgepole pine, Pinus contorta Douglas ex Loudon, revealing the presence of direct positive and indirect negative effects of mistletoe on defenses. Background and Objectives: For protection against natural enemies, pines produce and store oleoresin (resin) in ‘resin ducts’ that occur throughout the tree. Dwarf mistletoe, Arceuthobium americanum Nutt. ex Engelm. (hereafter “mistletoe”), is a widespread parasitic plant affecting the pines of western North America. Infection by mistletoe can suppress pine growth and increase the probability of insect attack—possibly due to a reduction in resin duct defenses or in the potency of chemical defenses at higher levels of mistletoe infection, as reported in Pinus banksiana Lamb. However, the influence of mistletoe infection on defenses in other pine species remains unclear. I hypothesized that mistletoe infection would induce greater resin duct defenses in P. contorta while simultaneously suppressing annual growth, which was expected to reduce defenses. Materials and Methods: Using increment cores from P. contorta trees occurring in a subalpine forest of Colorado, USA, I quantified tree age, annual growth, annual resin duct production (#/annual ring), and cross-sectional area (mm2 of resin ducts/annual ring). Results: Mistletoe infection increased with tree age and had a direct positive relationship with resin duct defenses. However, mistletoe infection also had an indirect negative influence on defenses via the suppression of annual growth. Conclusions: Through the combined direct and indirect effects, mistletoe infection had a net positive impact on resin duct production but a net negative impact on the total resin duct area. This finding highlights the complexity of pine defense responses to natural enemies and that future work is needed to understand how these responses influence overall levels of resistance and the risk of mortality.

scholarly journals Meta-Analysis Suggests Differing Indirect Effects of Viral, Bacterial, and Fungal Plant Pathogens on the Natural Enemies of Insect Herbivores

Insects ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 765
Author(s):  
Ussawit Srisakrapikoop ◽  
Tara J. Pirie ◽  
Mark D. E. Fellowes
Keyword(s):  
Natural Enemies ◽  
Indirect Effects ◽  
Plant Pathogens ◽  
Meta Analysis ◽  
Trophic Levels ◽  
Plant Diseases ◽  
Plant Quality ◽  
Insect Herbivore ◽  
Insect Herbivores ◽  
Fungal Plant Pathogens

Indirect effects are ubiquitous in nature, and have received much attention in terrestrial plant–insect herbivore–enemy systems. In such tritrophic systems, changes in plant quality can have consequential effects on the behavior and abundance of insect predators and parasitoids. Plant quality as perceived by insect herbivores may vary for a range of reasons, including because of infection by plant pathogens. However, plant diseases vary in their origin (viral, bacterial or fungal) and as a result may have differing effects on plant physiology. To investigate if the main groups of plant pathogens differ in their indirect effects on higher trophic levels, we performed a meta-analysis using 216 measured responses from 29 primary studies. There was no overall effect of plant pathogens on natural enemy traits as differences between pathogen types masked their effects. Infection by fungal plant pathogens showed indirect negative effects on the performance and preference of natural enemies via both chewing and piercing-sucking insect herbivore feeding guilds. Infection by bacterial plant pathogens had a positive effect on the natural enemies (parasitoids) of chewing herbivores. Infection by viral plant pathogens showed no clear effect, although parasitoid preference may be positively affected by their presence. It is important to note that given the limited volume of studies to date on such systems, this work should be considered exploratory. Plant pathogens are very common in nature, and tritrophic systems provide an elegant means to examine the consequences of indirect interactions in ecology. We suggest that further studies examining how plant pathogens affect higher trophic levels would be of considerable value.

scholarly journals Review of the direct and indirect effects of warming and drought on scale insect pests of forest systems

2020 ◽  
Author(s):  
Steven D Frank
Keyword(s):  
Population Growth ◽  
Natural Enemies ◽  
Indirect Effects ◽  
Insect Pests ◽  
Scale Insect ◽  
Interactive Effects ◽  
Scale Insects ◽  
Insect Species ◽  
Direct And Indirect Effects ◽  
Tree Quality

Abstract Higher temperatures and drought are key aspects of global change with the potential to alter the distribution and severity of many arthropod pests in forest systems. Scale insects (Hemiptera: Coccoidea) infest many tree species and are among the most important pests of trees in urban and rural forests, plantations and other forest systems. Infestations of native or exotic scale insects can kill or sicken trees with economic and ecosystem-wide consequences. Warming can have direct effects on the life history, fitness and population dynamics of many scale insect species by increasing development rate, survival or fecundity. These direct benefits can increase the geographic distribution of scale insects and their consequences for tree health. Warming and drought can affect scale insects indirectly by altering the quality of their host trees. Additive or interactive effects of warming and drought can change tree quality in such a way that it increases scale insect fitness and population growth. However, the effects are species- and context-dependent with some scale insect species negatively affected by drought-induced changes in tree quality. Warming and drought are often coincident in urban forests and predicted to co-occur in many parts of the world under climate change scenarios. The individual and interactive effects of these factors require further research to inform predictions and management of scale insect pests. Warming also indirectly affects scale insects by altering interactions with natural enemies. This includes changes in natural enemy phenology, community composition and abundance. In addition, warming can alter scale insect phenology or voltinism causing asynchrony with natural enemies or population growth too rapid for natural enemies to suppress. Direct and indirect effects of warming and drought on scale insects can increase the potential for some exotic species to become established and for some native species to become invasive. Unfortunately, much research on scale insects is confined to a few particularly important native or exotic pests which limits our ability to predict the effects of warming on many current or potential pests. More research is required to understand how warming and drought affect scale insects, scale insect management and the forest systems they inhabit.

scholarly journals Predicting direct and indirect non-target impacts of biocontrol agents using machine-learning approaches

2020 ◽  
Author(s):  
Hannah J. Kotula ◽  
Guadalupe Peralta ◽  
Carol M. Frost ◽  
Jacqui H. Todd ◽  
Jason M. Tylianakis
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Natural Enemies ◽  
Indirect Effects ◽  
Habitat Type ◽  
Indirect Interactions ◽  
Biocontrol Agents ◽  
Biological Pest Control ◽  
Starting Point ◽  
Indirect Impacts

AbstractBiological 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 and across different habitat types is also untested for consumer-prey interactions, though previous work on mutualist networks suggests that interactions among generalists may be more difficult to predict. 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, abundance, and phylogenetic information. Then, we tested whether the accuracy of machine-learning-predicted interactions depended on the generality 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, and this predictive ability depended on the generality of the interacting partners, but not 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 for both direct and indirect interactions was reasonably low. Combining machine-learning and network approaches provides a starting point for reducing risk in biocontrol.

Quality or quantity: the direct and indirect effects of host plants on herbivores and their natural enemies

Oecologia ◽  
2004 ◽  
Vol 142 (3) ◽  
pp. 413-420 ◽  
Author(s):  
Peter Stiling ◽  
Daniel C. Moon
Keyword(s):  
Natural Enemies ◽  
Indirect Effects ◽  
Host Plants ◽  
Direct And Indirect Effects

scholarly journals Predicting direct and indirect non-target impacts of biocontrol agents using machine-learning approaches

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0252448
Author(s):  
Hannah J. Kotula ◽  
Guadalupe Peralta ◽  
Carol M. Frost ◽  
Jacqui H. Todd ◽  
Jason M. Tylianakis
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Natural Enemies ◽  
Indirect Effects ◽  
Species Interactions ◽  
Habitat Type ◽  
Indirect Interactions ◽  
Biocontrol Agents ◽  
Biological Pest Control ◽  
Indirect Impacts

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.

scholarly journals Indirect Effect of Pesticides on Insects and Other Arthropods

Toxics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 177
Author(s):  
Francisco Sánchez-Bayo
Keyword(s):  
Natural Enemies ◽  
Indirect Effects ◽  
Species Interactions ◽  
Nutrient Recycling ◽  
Algae Blooms ◽  
Pest Outbreaks ◽  
Ecological Mechanisms ◽  
Periphyton Biomass ◽  
Predatory Insects ◽  
Intended Use

Pesticides released to the environment can indirectly affect target and non-target species in ways that are often contrary to their intended use. Such indirect effects are mediated through direct impacts on other species or the physical environment and depend on ecological mechanisms and species interactions. Typical mechanisms are the release of herbivores from predation and release from competition among species with similar niches. Application of insecticides to agriculture often results in subsequent pest outbreaks due to the elimination of natural enemies. The loss of floristic diversity and food resources that result from herbicide applications can reduce populations of pollinators and natural enemies of crop pests. In aquatic ecosystems, insecticides and fungicides often induce algae blooms as the chemicals reduce grazing by zooplankton and benthic herbivores. Increases in periphyton biomass typically result in the replacement of arthropods with more tolerant species such as snails, worms and tadpoles. Fungicides and systemic insecticides also reduce nutrient recycling by impairing the ability of detritivorous arthropods. Residues of herbicides can reduce the biomass of macrophytes in ponds and wetlands, indirectly affecting the protection and breeding of predatory insects in that environment. The direct impacts of pesticides in the environment are therefore either amplified or compensated by their indirect effects.

Does Ego-Resilience Impact Friendship Outcomes?

2016 ◽  
Vol 37 (2) ◽  
pp. 128-134 ◽  
Author(s):  
Elizabeth B. Lozano ◽  
Mahzad Hojjat ◽  
Judith Sims-Knight
Keyword(s):  
Social Support ◽  
Young Adults ◽  
Indirect Effects ◽  
Positive Emotions ◽  
Positive Outcomes ◽  
Ego Resilience ◽  
Maintenance Behaviors ◽  
Received Social Support ◽  
Trait Resilience ◽  
The Relationship

Abstract. The present study examined the relationship between resilience and positive outcomes in friendships of young adults. SEM and bootstrapping analyses were performed to test whether positive emotions mediate the relationship between ego-resilience and enhanced friendship outcomes. Findings revealed indirect effects for friendship closeness, maintenance behaviors, and received social support. Our findings demonstrate the importance of positive emotions and its connection with trait resilience in the realm of friendships.

Interpersonal Stress and Poor Health

2010 ◽  
Vol 15 (1) ◽  
pp. 3-11 ◽  
Author(s):  
Mette M. Aanes ◽  
Maurice B. Mittelmark ◽  
Jørn Hetland
Keyword(s):  
Psychological Distress ◽  
Depressive Symptoms ◽  
Social Relationships ◽  
Indirect Effects ◽  
Social Connectedness ◽  
Somatic Symptoms ◽  
Total Effect ◽  
Subjective Feeling ◽  
Interpersonal Stress ◽  
The Relationship

This paper investigated whether the lack of social connectedness, as measured by the subjective feeling of loneliness, mediates the well-known relationship between interpersonal stress and psychological distress. Furthermore, a relationship between interpersonal stress and somatic symptoms was hypothesized. The study sample included 3,268 women and 3,220 men in Western Norway. The main findings were that interpersonal stress was significantly related to psychological distress as well as to somatic symptoms, both directly and indirectly via paths mediated by loneliness. The size of the indirect effects varied, suggesting that the importance of loneliness as a possible mediator differs for depressive symptoms, anxiety symptoms, and somatic symptoms. In the case of depressive symptoms, more than 75% of the total effect was mediated through loneliness, while in the case of somatic symptoms just over 40% of the total effect was mediated through loneliness. This study supports the hypotheses that social connectedness mediates a relationship between interpersonal stress and psychological distress. The study also provides the first link between interpersonal stress, as measured by the Bergen Social Relationships Scale, and somatic symptoms, extending earlier research on the relationship between interpersonal stress and psychological distress.

Direct and indirect effects of Cd2+ on photosynthesis in sugar beet (Beta vulgaris)

1991 ◽  
Vol 83 (1) ◽  
pp. 129-135 ◽  
Author(s):  
Maria Greger ◽  
Erling Ogren
Keyword(s):  
Sugar Beet ◽  
Beta Vulgaris ◽  
Indirect Effects ◽  
Direct And Indirect Effects
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