Synthetic transcription factor engineering for cell and gene therapy.

Synthetic transcription factors (synTFs) that control beneficial transgene expression are an important method to increase the safety and efficacy of cell and gene therapy. Reliance on synTF components from non-human sources has slowed progress in the field because of concerns about immunogenicity and inducer drug properties. Recent advances in human-derived DNA-binding domains (DBDs) and transcriptional activation domains (TADs) paired with novel control modules responsive to clinically approved small molecules have poised the synTF field to overcome these hurdles. Advances include controllers inducible by autonomous signaling inputs and more complex, multi-input synTF circuits. Demonstrations of advanced control strategies with human-derived transcription factor components in clinically relevant vectors and in vivo models will facilitate progression into the clinic.

Predicting the impact of invasive trees from different measures of abundance.

Biological invasions produce negative impacts worldwide, causing massive economic costs and ecological impacts. Knowing the relationship between invasive species abundance and the magnitude of their impacts (abundance-impact curves) is critical to designing prevention and management strategies that effectively tackle these impacts. However, different measures of abundance may produce different abundance-impact curves. Woody plants are among the most transformative invaders, especially in grassland ecosystems because of the introduction of hitherto absent life forms. In this study, our first goal was to assess the impact of a woody invader, Pinus contorta (hereafter pine), on native grassland productivity and livestock grazing in Patagonia (Argentina), building abundance-impact curves. Our second goal, was to compare different measure of pine abundance (density, basal area and canopy cover) as predictors of pine's impact on grassland productivity. Our third goal, was to compare abundance-impact curves among the mentioned measures of pine abundance and among different measures of impact: total grassland productivity, palatable productivity and sheep stocking rate (the number of sheep that the grassland can sustainably support). Pine canopy cover, closely followed by basal area, was the measure of abundance that best explained the impact on grassland productivity, but the shape of abundance impact curves differed between measures of abundance. While increases in pine density and basal area always reduced grassland productivity, pine canopy cover below 30% slightly increased grassland productivity and higher values caused an exponential decline. This increase in grassland productivity with low levels of pine canopy cover could be explained by the amelioration of stressful abiotic conditions for grassland species. Different measures of impact, namely total productivity, palatable productivity and sheep stocking rate, drew very similar results. Our abundance-impact curves are key to guide the management of invasive pines because a proper assessment of how many invasive individuals (per surface unit) are unacceptable, according to environmental or economic impact thresholds, is fundamental to define when to start management actions.

Identifying Priorities, Targets, and Actions for the Long-term Social and Ecological Management of Invasive Non-Native Species.

Formulating effective management plans for addressing the impacts of invasive non-native species (INNS) requires the definition of clear priorities and tangible targets, and the recognition of the plurality of societal values assigned to these species. These tasks require a multi-disciplinary approach and the involvement of stakeholders. Here, we describe procedures to integrate multiple sources of information to formulate management priorities, targets, and high-level actions for the management of INNS. We follow five good-practice criteria: justified, evidence-informed, actionable, quantifiable, and flexible. We used expert knowledge methods to compile 17 lists of ecological, social, and economic impacts of lodgepole pines (Pinus contorta) and American mink (Neovison vison) in Chile and Argentina, the privet (Ligustrum lucidum) in Argentina, the yellow-jacket wasp (Vespula germanica) in Chile, and grasses (Urochloa brizantha and Urochloa decumbens) in Brazil. INNS plants caused a greater number of impacts than INNS animals, although more socio-economic impacts were listed for INNS animals than for plants. These impacts were ranked according to their magnitude and level of confidence on the information used for the ranking to prioritise impacts and assign them one of four high-level actions-do nothing, monitor, research, and immediate active management. We showed that it is possible to formulate management priorities, targets, and high-level actions for a variety of INNS and with variable levels of available information. This is vital in a world where the problems caused by INNS continue to increase, and there is a parallel growth in the implementation of management plans to deal with them.

Modeling CRISPR gene drives for suppression of invasive rodents using a supervised machine learning framework.

Invasive rodent populations pose a threat to biodiversity across the globe. When confronted with these invaders, native species that evolved independently are often defenseless. CRISPR gene drive systems could provide a solution to this problem by spreading transgenes among invaders that induce population collapse, and could be deployed even where traditional control methods are impractical or prohibitively expensive. Here, we develop a high-fidelity model of an island population of invasive rodents that includes three types of suppression gene drive systems. The individual-based model is spatially explicit, allows for overlapping generations and a fluctuating population size, and includes variables for drive fitness, efficiency, resistance allele formation rate, as well as a variety of ecological parameters. The computational burden of evaluating a model with such a high number of parameters presents a substantial barrier to a comprehensive understanding of its outcome space. We therefore accompany our population model with a meta-model that utilizes supervised machine learning to approximate the outcome space of the underlying model with a high degree of accuracy. This enables us to conduct an exhaustive inquiry of the population model, including variance-based sensitivity analyses using tens of millions of evaluations. Our results suggest that sufficiently capable gene drive systems have the potential to eliminate island populations of rodents under a wide range of demographic assumptions, though only if resistance can be kept to a minimal level. This study highlights the power of supervised machine learning to identify the key parameters and processes that determine the population dynamics of a complex evolutionary system.

How important is individual foraging specialisation in invasive predators for native-prey population viability?

Predation by invasive species is a major threat to the persistence of naïve prey. Typically, this negative effect is addressed by suppressing the population size of the invasive predator to a point where the predation pressure does not hinder the viability of the prey. However, this type of intervention may not be effective whenever a few specialised predators are the cause of the decline. We investigated the effects of varying levels of specialised invasive stoats (Mustela erminea) abundance on the long-term viability of simulated kiwi (Apteryx spp.) populations. We explored four scenarios with different proportions of highly specialised stoats, which were those that had a ≥ 0.75 probability of predating kiwi eggs and chicks if they were within their home range: (i) a stoat population composed mostly of generalists (mean: 0.5 probability of predation across the population); (ii) 5% of highly specialised stoats and the remaining being generalists; (iii) 10% of highly specialised stoats and the remaining being generalists; and, (iv) half highly specialised stoats and half generalists. We found that stoat home range sizes, rather than stoat density or the density of highly specialised stoats, was the main driver of kiwi population trends. Stoats with large home ranges were more likely to predate kiwi eggs and chicks as these were more likely to fall within a large home range. More broadly, our findings show how the daily individual ranging and foraging behaviour of an invasive predator can scale-up to shape population trends of naïve prey.

Evaluating the effects of landscape structure on the recovery of an invasive vertebrate after population control.

CONTEXT: Effective landscape control of invasive species is context-dependent due to the interplay between the landscape structure, local population dynamics, and metapopulation processes. We use a modelling approach incorporating these three elements to explore the drivers of recovery of populations of invasive species after control. OBJECTIVES: We aim to improve our understanding of the factors influencing the landscape-level control of invasive species. METHODS: We focus on the case study of invasive brushtail possum (Trichosurus vulpecula) control in New Zealand. We assess how 13 covariates describing the landscape, patch, and population features influence the time of population recovery to a management density threshold of two possums/ha. We demonstrate the effects of those covariates on population recovery under three scenarios of population growth: logistic growth, strong Allee effects, and weak Allee effects. RESULTS: Recovery times were rapid regardless of the simulated population dynamics (average recovery time < 2 years), although populations experiencing Allee effects took longer to recover than those growing logistically. Our results indicate that habitat availability and patch area play a key role in reducing times to recovery after control, and this relationship is consistent across the three simulated scenarios. CONCLUSIONS: The control of invasive possum populations in patchy landscapes would benefit from a patch-level management approach (considering each patch as an independent management unit), whereas simple landscapes would be better controlled by taking a landscape-level view (the landscape as the management unit). Future research should test the predictions of our models with empirical data to refine control operations.

A concise guide to developing and using quantitative models in conservation management.

Quantitative models are powerful tools for informing conservation management and decision-making. As applied modeling is increasingly used to address conservation problems, guidelines are required to clarify the scope of modeling applications and to facilitate the impact and acceptance of models by practitioners. We identify three key roles for quantitative models in conservation management: (a) to assess the extent of a conservation problem; (b) to provide insights into the dynamics of complex social and ecological systems; and, (c) to evaluate the efficacy of proposed conservation interventions. We describe 10 recommendations to facilitate the acceptance of quantitative models in conservation management, providing a basis for good practice to guide their development and evaluation in conservation applications. We structure these recommendations within four established phases of model construction, enabling their integration within existing workflows: (a) design (two recommendations); (b) specification (two); (c) evaluation (one); and (d) inference (five). Quantitative modeling can support effective conservation management provided that both managers and modelers understand and agree on the place for models in conservation. Our concise review and recommendations will assist conservation managers and modelers to collaborate in the development of quantitative models that are fit-for-purpose, and to trust and use these models appropriately while understanding key drivers of uncertainty.

Remoteness promotes biological invasions on islands worldwide.

One of the best-known general patterns in island biogeography is the species-isolation relationship (SIR), a decrease in the number of native species with increasing island isolation that is linked to lower rates of natural dispersal and colonization on remote oceanic islands. However, during recent centuries, the anthropogenic introduction of alien species has increasingly gained importance and altered the composition and richness of island species pools. We analyzed a large dataset for alien and native plants, ants, reptiles, mammals, and birds on 257 (sub) tropical islands, and showed that, except for birds, the number of naturalized alien species increases with isolation for all taxa, a pattern that is opposite to the negative SIR of native species. We argue that the reversal of the SIR for alien species is driven by an increase in island invasibility due to reduced diversity and increased ecological naiveté of native biota on the more remote islands.

Understanding the biological invasion risk posed by the global wildlife trade: propagule pressure drives the introduction and establishment of Nearctic turtles.

Biological invasions are a key component of human-induced global change. The continuing increase in global wildlife trade has raised concerns about the parallel increase in the number of new invasive species. However, the factors that link the wildlife trade to the biological invasion process are still poorly understood. Moreover, there are analytical challenges in researching the role of global wildlife trade in biological invasions, particularly issues related to the under-reporting of introduced and established populations in areas with reduced sampling effort. In this work, we use high-quality data on the international trade in Nearctic turtles (1999-2009) coupled with a statistical modelling framework, which explicitly accounts for detection, to investigate the factors that influence the introduction (release, or escape into the wild) of globally traded Nearctic turtles and the establishment success (self-sustaining exotic populations) of slider turtles (Trachemys scripta), the most frequently traded turtle species. We found that the introduction of a species was influenced by the total number of turtles exported to a jurisdiction and the age at maturity of the species, while the establishment success of slider turtles was best associated with the propagule number (number of release events), and the number of native turtles in the jurisdiction of introduction. These results indicate both a direct and indirect association between the wildlife trade and the introduction of turtles and establishment success of slider turtles, respectively. Our results highlight the existence of gaps in the number of globally recorded introduction events and established populations of slider turtles, although the expected bias is low. We emphasize the importance of researching independently the factors that affect the different stages of the invasion pathway. Critically, we observe that the number of traded individuals might not always be an adequate proxy for propagule pressure and establishment success.