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Introducing a new or extirpated species to an ecosystem is risky, and managers need quantitative methods that can predict the consequences for the recipient ecosystem. Proponents of keystone predator reintroductions commonly argue that the presence of the predator will restore ecosystem function, but this has not always been the case, and mathematical modeling has an important role to play in predicting how reintroductions will likely play out. We devised an ensemble modeling method that integrates species interaction networks and dynamic community simulations and used it to describe the range of plausible consequences of 2 keystone-predator reintroductions: wolves (Cams lupus) to Yellowstone National Park and dingoes (Canis dingo) to a national park in Australia. Although previous methods for predicting ecosystem responses to such interventions focused on predicting changes around a given equilibrium, we used Lotka-Volterra equations to predict changing abundances through time. We applied our method to interaction networks for wolves in Yellowstone National Park and for dingoes in Australia. Our model replicated the observed dynamics in Yellowstone National Park and produced a larger range of potential outcomes for the dingo network. However, we also found that changes in small vertebrates or invertebrates gave a good indication about the potential future state of the system. Our method allowed us to predict when the systems were far from equilibrium. Our results showed that the method can also be used to predict which species may increase or decrease following a reintroduction and can identify species that are important to monitor (i.e., species whose changes in abundance give extra insight into broad changes in the system). Ensemble ecosystem modeling can also be applied to assess the ecosystem-wide implications of other types of interventions including assisted migration, biocontrol, and invasive species eradication. Introducir una especie nueva o extirpada a un ecosistema es un riesgo y los administradores necesitan métodos cuantitativos que puedan predecir las consecuencias para el ecosistema receptor. Quienes proponen reintroducciones de depredadores clave comúnmente argumentan que la presencia del depredador restaurará la función ambiental, pero esto no siempre ha sido el caso y el modelado matemático tiene un papel importante que desempeñar en la predicción de cómo es probable que terminen las reintroducciones. Concebimos un método de modelado en conjunto que integra las redes de interacción de las especies y las simulaciones de dinámicas de comunidad y lo utilizamos para describir la extensión de las consecuencias posibles de la reintroducción de dos depredadores clave: lobos (Canis lupus) al Parque Nacional Yellowstone y dingos (Canis dingo) a un parque nacional en Australia. Mientras que los métodos previos para predecir las respuestas de los ecosistemas a dichas intervenciones se enfocaron en predecir los cambios en torno a un equilibrio dado, nosotros utilizamos las ecuaciones Lotka-Volterra para predecir las abundancias cambiantes a través del tiempo. Aplicamos nuestro método a las redes de interacción de los lobos en el Parque Nacional Yellowstone y a los dingos en Australia. Nuestro modelo replicó las dinámicas observadas en el Parque Nacional Yellowstone y produjo una extensión más grande de resultados potenciales para la red de los dingos. Sin embargo, también encontramos que los cambios en los pequeños vertebrados e invertebrados dieron una buena indicación sobre el posible estado futuro del sistema. Nuestro método nos permitió predecir cuándo los sistemas estaban lejos del equilibrio. Nuestros resultados mostraron que el método también puede utilizarse para predecir cuáles especies pueden incrementar o disminuir después de una reintroducción y puede identificar a las especies que son importantes de monitorear (es decir, las especies cuyos cambios en la abundancia dan información extra de los cambios generales en el sistema). El modelado de ecosistemas en conjunto también puede aplicarse para valorar las implicaciones en todo el ecosistema de otras intervenciones, incluyendo la migración asistida, el bio-controly la erradicación de especies invasoras.

Protected areas are key to meeting biodiversity conservation goals, but direct measures of effectiveness have proven difficult to obtain. We address this challenge by using environmental DNA from leech-ingested bloodmeals to estimate spatially-resolved vertebrate occupancies across the 677 km 2 Ailaoshan reserve in Yunnan, China. From 30,468 leeches collected by 163 park rangers across 172 patrol areas, we identify 86 vertebrate species, including amphibians, mammals, birds and squamates. Multi-species occupancy modelling shows that species richness increases with elevation and distance to reserve edge. Most large mammals (e.g. sambar, black bear, serow, tufted deer) follow this pattern; the exceptions are the three domestic mammal species (cows, sheep, goats) and muntjak deer, which are more common at lower elevations. Vertebrate occupancies are a direct measure of conservation outcomes that can help guide protected-area management and improve the contributions that protected areas make towards global biodiversity goals. Here, we show the feasibility of using invertebrate-derived DNA to estimate spatially-resolved vertebrate occupancies across entire protected areas. Invertebrate-derived eDNA (iDNA) is an emerging tool for taxonomic and spatial biodiversity monitoring. Here, the authors use metabarcoding of leech-derived iDNA to estimate vertebrate occupancy over an entire protected area, the Ailaoshan Nature Reserve, China.

The eradication of invasive species from islands is an important part of managing these ecologically unique and at‐risk regions. Island eradications are complex projects and mathematical models play an important role in supporting efficient and transparent decision‐making. In this review, we cover the past applications of modeling to island eradications, which range from large‐scale prioritizations across groups of islands, to project‐level decision‐making tools. While quantitative models have been formulated and parameterized for a range of important problems, there are also critical research gaps. Many applications of quantitative modeling lack uncertainty analyses, and are therefore overconfident. Forecasting the ecosystem‐wide impacts of species eradications is still extremely challenging, despite recent progress in the field. Overall, the field of quantitative modeling is well‐developed for island eradication planning. Multiple practical modeling tools are available for, and are being applied to, a diverse suite of important decisions, and quantitative modeling is well placed to address pressing issues in the field.

Significance Specific protein–protein interactions are abundant in, and essential for, cellular life. In contrast to the well-studied docking of two already folded proteins, it has been recently established that many proteins are disordered and unfolded in the absence of their partner protein, but appear folded once bound. Must these initially disordered proteins transiently fold in isolation before binding their partners? We examine a small disordered protein and find that interactions with its (already structured) partner protein are what cause the relatively unstructured protein to fold. Thus, the requirement for one protein to fold is not an obstacle for reliable, fast association between two proteins. This result offers some explanation for the abundance of similar protein–protein interactions throughout biology. Protein–protein interactions are at the heart of regulatory and signaling processes in the cell. In many interactions, one or both proteins are disordered before association. However, this disorder in the unbound state does not prevent many of these proteins folding to a well-defined, ordered structure in the bound state. Here we examine a typical system, where a small disordered protein (PUMA, p53 upregulated modulator of apoptosis) folds to an α-helix when bound to a groove on the surface of a folded protein (MCL-1, induced myeloid leukemia cell differentiation protein). We follow the association of these proteins using rapid-mixing stopped flow, and examine how the kinetic behavior is perturbed by denaturant and carefully chosen mutations. We demonstrate the utility of methods developed for the study of monomeric protein folding, including β-Tanford values, Leffler α, Φ-value analysis, and coarse-grained simulations, and propose a self-consistent mechanism for binding. Folding of the disordered protein before binding does not appear to be required and few, if any, specific interactions are required to commit to association. The majority of PUMA folding occurs after the transition state, in the presence of MCL-1. We also examine the role of the side chains of folded MCL-1 that make up the binding groove and find that many favor equilibrium binding but, surprisingly, inhibit the association process.

Knowing what animals eat is fundamental to our ability to understand and manage biodiversity and ecosystems, but researchers often must rely on indirect methods to infer trophic position and food intake. Using an approach that combines evidence from stable isotope analysis and DNA metabarcoding, we assessed the diet and trophic position of Anthene usamba butterflies, for which there are no known direct observations of larval feeding. An earlier study that analyzed adults rather than caterpillars of A. usamba inferred that this butterfly was aphytophagous, but we found that the larval guts of A. usamba and two known herbivorous lycaenid species contain chloroplast 16S sequences. Moreover, chloroplast barcoding revealed high sequence similarity between chloroplasts found in A. usamba guts and the chloroplasts of the Vachellia drepanolobium trees on which the caterpillars live. Stable isotope analysis provided further evidence that A. usamba caterpillars feed on V. drepanolobium, and the possibilities of strict herbivory versus limited omnivory in this species are discussed. These results highlight the importance of combining multiple approaches and considering ontogeny when using stable isotopes to infer trophic ecology where direct observations are difficult or impossible.

1. Islands are global hotspots of both biodiversity and extinction. Invasive species are a primary threat, and the majority of islands have been invaded by more than one. Multispecies eradications are essential for conserving the biodiversity of these islands, but experience has shown that eradicating species at the wrong time can be disastrous for endemic species. 2. Managers not only have to decide how to eradicate each invasive species, they need to determine when to target each species, and how to control multiple species with a limited budget. We use dynamic control theory to show that, when resources are limited, species should be eradicated in a particular order (an eradication schedule). We focus on a common invaded island ecosystem motif, where one invasive predator consumes two prey species (one endemic, one invasive), and managers wish to eradicate both invasives while ensuring the persistence of the endemic species. We identify the optimal eradication schedule for this entire class of problem. To illustrate the application of our solution, we also analyse a particular case study from California's Channel Islands. 3. For any island ecosystem that shares this motif, managers should begin by allocating all of their resources towards invasive predator control. Only later should resources be shifted towards controlling the invasive prey. This shift should ideally be gradual, but an abrupt shift is very close to optimal. The Channel Islands case study confirms these findings. Targeting both species simultaneously is substantially suboptimal. 4. We reach the robust conclusion that the same eradication schedule should be applied to any island with this ecosystem motif, even if the ecosystem contains different species to the Channel Islands case study. 5. Synthesis and applications. Although very numerous, the world's invaded island ecosystems could be described by a limited range of invaded ecosystem motifs. By calculating robust optimal eradication schedules for each motif, the approach defined in this study could offer rapid decision-support for a large number of future conservation projects where specific data are scarce.

We tackle the problem of coupling a spatiotemporal model for simulating the spread and control of an invasive alien species with data coming from image processing and expert knowledge. In this study, we implement a spatially explicit optimal control model based on a reaction–diffusion equation which includes an Holling II type functional response term for modeling the density control rate. The model takes into account the budget constraint related to the control program and searches for the optimal effort allocation for the minimization of the invasive alien species density. Remote sensing and expert knowledge have been assimilated in the model to estimate the initial species distribution and its habitat suitability, empirically extracted by a land cover map of the study area. The approach has been applied to the plant species Ailanthus altissima (Mill.) Swingle within the Alta Murgia National Park. This area is one of the Natura 2000 sites under the study of the ongoing National Biodiversity Future Center (NBFC) funded by the Italian National Recovery and Resilience Plan (NRRP), and pilot site of the finished H2020 project ECOPOTENTIAL, which aimed at the integration of modeling tools and Earth Observations for a sustainable management of protected areas. Both the initial density map and the land cover map have been generated by using very high resolution satellite images and validated by means of ground truth data provided by the EU Life Alta Murgia Project (LIFE12 BIO/IT/000213), a project aimed at the eradication of A. altissima in the Alta Murgia National Park.

Reintroducing a species to an ecosystem can have significant impacts on the recipient ecological community. Although reintroductions can have striking and positive outcomes, they also carry risks; many well-intentioned conservation actions have had surprising and unsatisfactory outcomes. A range of network-based mathematical methods has been developed to make quantitative predictions of how communities will respond to management interventions. These methods are based on the limited knowledge of which species interact with each other and in what way. However, expert knowledge isn’t perfect and can only take models so far. Fortunately, other types of data, such as abundance time series, is often available, but, to date, no quantitative method exists to integrate these various data types into these models, allowing more precise ecosystem-wide predictions. In this paper, we develop mathematical methods that combine time-series data of multiple species with knowledge of species interactions and we apply it to proposed reintroductions at Booderee National Park in Australia. There have been large fluctuations in species abundances at Booderee National Park in recent history, following intense feral fox (Vulpes vulpes) control, including the local extinction of the greater glider (Petauroides volans). These fluctuations can provide information about the system isn’t readily obtained from a stable system, and we use them to inform models that we then use to predict potential outcomes of eastern quoll (Dasyurus viverrinus>) and long-nosed potoroo (Potorous tridactylus) reintroductions. One of the key species of conservation concern in the park is the Eastern Bristlebird (Dasyornis brachypterus), and we find that long-nosed potoroo introduction would have very little impact on the Eastern Bristlebird population, while the eastern quoll introduction increased the likelihood of Eastern Bristlebird decline, although that depends on the strength and form of any possible interaction.

Information about species’ locations can influence what happens to them—from supporting habitat protection to exposing poaching targets. Debate about releasing locations when new species are found highlights the trade‐off between the risk of loss and the benefits of funding and public support. No research so far has collected data on how such decisions are made, and no decision tools easily compare a range of decision‐making scenarios. Here, we present a method to compare the costs and benefits of decisions about the disclosure of information about newly discovered species and populations. We implement our method for seven species where information is completely or partially secret. We ask decision‐makers to estimate the costs and benefits associated with these case studies and apply our method. Results show a range of implications from choices that are always better, to others that depend on risk attitude, and demonstrate that the process of decision‐making can be transparent and easily communicated.