Creating novel food webs on introduced Australian acacias: indirect effects of galling biological control agents

Itching for an Answer: Gall-Forming Biological Control Agent Contains an Itch Mite Species Found at Localities Known for Periodic ‘Bite Outbreaks’

Biological control is an attractive option for controlling invasive plant species that are difficult to manage otherwise. However, the release of a non-native species as a biological control agent carries risks. The most obvious risk relates to impacts on plant species other than the plant species targeted for control. There are, however, also other risks. We report on a potential unintended impact of Dasineura dielsi, a gall-forming biological control agent that was released against Acacia cyclops in South Africa in 2003. We confirmed that the galls formed by D. dielsi on A. cyclops harbor mites in the genus Pyemotes (P. cf. ventricosus) within their gall structures, which are parasites of various insect species, but are also known to cause dermatitis in humans. Sporadic biting incidences have been reported in at least two locations in South Africa. The implications are that manual clearing of A. cyclops may expose humans to itch mites and to risks of bites. Gall-forming insects and fungi are known to create niches for herbivores and other gall-associated fauna. Although every possible food web interaction cannot be predicted, enough evidence exists to require that agent screening to include non-target risks other than those pertaining to non-host plants. Testing only whether agents are compromised by interactions with non-target plant species is not sufficient during agent evaluation. If such associations are known from the native range and therefore can form in the introduced range, then any known risk to health and socio-economic activities should be disclosed. We argue for the general development of objective assessment of such risks compared with the benefits potentially accruing from successful biological control of the target plant.

Evaluating unintended consequences of intentional species introductions and eradications for improved conservation management

Increasingly intensive strategies to maintain biodiversity and ecosystem function are being deployed in response to global anthropogenic threats, including intentionally introducing and eradicating species via assisted migration, rewilding, biological control, invasive species eradications, and gene drives. These actions are highly contentious because of their potential for unintended consequences. We conducted a global literature review of these conservation actions to quantify how often unintended outcomes occur and to elucidate their underlying causes. To evaluate conservation outcomes, we developed a community assessment framework for systematically mapping the range of possible interaction types for 111 case studies. Applying this tool, we quantified the number of interaction types considered in each study and documented the nature and strength of intended and unintended outcomes. Intended outcomes were reported in 51% of cases, a combination of intended outcomes and unintended outcomes in 26%, and strictly unintended outcomes in 10%. Hence, unintended outcomes were reported in 36% of all cases evaluated. In evaluating overall conservations outcomes (weighing intended vs. unintended effects), some unintended effects were fairly innocuous relative to the conservation objective, whereas others resulted in serious unintended consequences in recipient communities. Studies that assessed a greater number of community interactions with the target species reported unintended outcomes more often, suggesting that unintended consequences may be underreported due to insufficient vetting. Most reported unintended outcomes arose from direct effects (68%) or simple density-mediated or indirect effects (25%) linked to the target species. Only a few documented cases arose from more complex interaction pathways (7%). Therefore, most unintended outcomes involved simple interactions that could be predicted and mitigated through more formal vetting. Our community assessment framework provides a tool for screening future conservation actions by mapping the recipient community interaction web to identify and mitigate unintended outcomes from intentional species introductions and eradications for conservation.

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

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.

Potential Distribution and Niche Differentiation of Spodoptera frugiperda in Africa

The fall armyworm, Spodoptera frugiperda (J.E. Smith) is a serious agricultural pest. The species originates from the tropical and subtropical regions of the Americas and has now become established in many countries. Its strong migratory ability is the key factor in the rapidly expanding range of S. frugiperda in Africa, where food security faces unprecedented challenges. Exploring potential distributions and niche differentiation of S. frugiperda could provide new insights into the nature of climate niche shifts and our ability to anticipate further invasions. In this study, the occurrence population records (native, source, global, and African) and environmental variables of S. frugiperda were selected to fit ecological niche models (ENMs), with an evaluation of niche conservatism during its invasion of Africa. The results showed that the potential distributions of S. frugiperda are mainly in tropical and subtropical areas in Africa. The climate spaces occupied by its native population and introduced African population broadly overlap. Although, climate niches were conserved during invasion of Africa, many climate spaces were unoccupied, suggesting a high remaining invasion potential in Africa. The selection of the biogeographic realm is an important factor in model construction, and has a great influence on the transferability of the models. Indeed, the global model produced the best performance, following the source and native models.

Characterizing ecological interaction networks to support risk assessment in classical biological control of weeds

A key element in weed biological control is the selection of a biological control agent that minimizes the risks of non-target attack and indirect effects on the recipient community. Network ecology is a promising approach that could help decipher tritrophic interactions in both the native and the invaded ranges, to complement quarantine-based host-specificity tests and gain insights on potential interactions of biological control agents. This review highlights practical questions addressed by networks, including 1) biological control agent selection, based on specialization indices, 2) risk assessment of biological control agent release into a novel environment, via particular patterns of association such as apparent competition between agent(s) and native herbivore(s), 3) network comparisons through structural metrics, 4) potential of network modelling and 5) limits of network construction methods.

Global warming promotes biological invasion of a honey bee pest

Climate change and biological invasions are two major global environmental challenges. Both may interact, e.g. via altered impact and distribution of invasive alien species. Even though invasive species play a key role for compromising the health of honey bees, the impact of climate change on the severity of such species is still unknown. The small hive beetle (SHB, Aethina tumida, Murray) is a parasite of honey bee colonies. It is endemic to sub-Saharan Africa and has established populations on all continents except Antarctica. Since SHBs pupate in soil, pupation performance is governed foremost by two abiotic factors, soil temperature and moisture, which will be affected by climate change. Here, we investigated SHB invasion risk globally under current and future climate scenarios. We modelled survival and development time during pupation (=pupal performance) in response to soil temperature and soil moisture using published and novel experimental data. Presence data on SHB distribution were used for model validation. We then linked the model with global soil data in order to classify areas (resolution: 10 arcmin; i.e. 18.6 km at the equator) as unsuitable, marginal and suitable for SHB pupation performance. Under the current climate, the results show that many areas globally yet uninvaded are actually suitable, suggesting considerable SHB invasion risk. Future scenarios of global warming project a vehement increase in climatic suitability for SHB and corresponding potential for invasion, especially in the temperate regions of the Northern hemisphere, thereby creating demand for enhanced and adapted mitigation and management. Our analysis shows, for the first time, effects of global warming on a honey bee pest and will help areas at risk to prepare adequately. In conclusion, this is a clear case for global warming promoting biological invasion of a pest species with severe potential to harm important pollinator species globally.

Biological invasions, climate change and genomics

The rate of biological invasions is expected to increase as the effects of climate change on biological communities become widespread. Climate change enhances habitat disturbance which facilitates the establishment of invasive species, which in turn provides opportunities for hybridization and introgression. These effects influence local biodiversity that can be tracked through genetic and genomic approaches. Metabarcoding and metagenomic approaches provide a way of monitoring some types of communities under climate change for the appearance of invasives. Introgression and hybridization can be followed by the analysis of entire genomes so that rapidly changing areas of the genome are identified and instances of genetic pollution monitored. Genomic markers enable accurate tracking of invasive species' geographic origin well beyond what was previously possible. New genomic tools are promoting fresh insights into classic questions about invading organisms under climate change, such as the role of genetic variation, local adaptation and climate pre-adaptation in successful invasions. These tools are providing managers with often more effective means to identify potential threats, improve surveillance and assess impacts on communities. We provide a framework for the application of genomic techniques within a management context and also indicate some important limitations in what can be achieved.