Search

Migratory species are protected under international legislation; their seasonal movements across international borders may therefore present opportunities for understanding how global conservation policies translate to local‐level actions across different socio‐ecological contexts. Moreover, local‐level management of migratory species can reveal how culture and governance affects progress towards achieving global targets. Here, we investigate potential misalignment in the two‐way relationship between global‐level conservation policies (i.e. hunting bans and quotas) and local‐level norms, values and actions (i.e. legal and illegal hunting) in the context of waterfowl hunting in northern Kazakhstan as a case‐study.Northern Kazakhstan is globally important for waterfowl and a key staging area for arctic‐breeding species. Hunting is managed through licences, quotas and seasonal bans under UN‐AEWA intergovernmental agreements. To better understand the local socio‐ecological context of waterfowl hunting, we take a mixed‐methods approach using socio‐ecological surveys, informal discussions and population modelling of a focal migratory goose species to: (a) investigate motivations for hunting in relation to socio‐economic factors; (b) assess knowledge of species' protection status; and (c) predict the population size of Lesser White‐fronted Geese (LWfG; Anser erythropus; IUCN Vulnerable) under different scenarios of survival rates and hunting offtake, to understand how goose population demographics interact with the local socio‐ecological context.Model results showed no evidence that waterfowl hunting is motivated by financial gain; social and cultural importance were stronger factors. The majority of hunters are knowledgeable about species' protection status; however, 11% did not know LWfG are protected, highlighting a key area for increased stakeholder engagement.Simulations of LWfG population growth over a 20‐year period showed LWfG are highly vulnerable to hunting pressure even when survival rates are high. This potential impact of hunting highlights the need for effective regulation along the entire flyway; our survey results show that hunters were generally compliant with newly introduced hunting regulations, showing that effective regulation is possible on a local level.Synthesis and applications. Here, we investigate how global conservation policy and local norms interact to affect the management of a threatened migratory species, which is particularly important for the protection and sustainable management of wildlife that crosses international borders where local contexts may differ. Our study highlights that to be effective and sustainable in the long‐term, global conservation policies must fully integrate local socio‐economic, cultural, governance and environmental contexts, to ensure interventions are equitable across entire species' ranges. This approach is relevant and adaptable for different contexts involving the conservation of wide‐ranging and migratory species, including the 255 migratory waterfowl covered by UN‐AEWA (United Nations Agreement on the Conservation of African‐Eurasian Migratory Waterbirds). [ABSTRACT FROM AUTHOR]

Knowledge concerning spatio‐temporal distributions of populations is a prerequisite for successful conservation and management of migratory animals. Achieving cost‐effective monitoring of large‐scale movements is often difficult due to lack of effective and inexpensive methods.Taiga bean goose Anser fabalis fabalis and tundra bean goose A. f. rossicus offer an excellent example of a challenging management situation with harvested migratory populations. The subspecies have different conservation statuses and population trends. However, their distribution overlaps during migration to an unknown extent, which, together with their similar appearance, has created a conservation–management dilemma.Gaussian process (GP) models are widely adopted in the field of statistics and machine learning, but have seldom been applied in ecology so far. We introduce the R package gplite for GP modelling and use it in our case study together with birdwatcher observation data to study spatio‐temporal differences between bean goose subspecies during migration in Finland in 2011–2019.We demonstrate that GP modelling offers a flexible and effective tool for analysing heterogeneous data collected by citizens. The analysis reveals spatial and temporal distribution differences between the two bean goose subspecies in Finland. Taiga bean goose migrates through the entire country, whereas tundra bean goose occurs only in a small area in south‐eastern Finland and migrates later than taiga bean goose.Synthesis and applications. Within the studied bean goose populations, harvest can be targeted at abundant tundra bean goose by restricting hunting to south‐eastern Finland and to the end of the migration period. In general, our approach combining citizen science data with GP modelling can be applied to study spatio‐temporal distributions of various populations and thus help in solving challenging management situations. The introduced R package gplite can be applied not only to ecological modelling, but to a wide range of analyses in other fields of science. [ABSTRACT FROM AUTHOR]

Reintroduction projects, which are an important tool in threatened species conservation, are becoming more complex, often involving the translocation of multiple species. Ecological theory predicts that the sequence and timing of reintroductions will play an important role in their success or failure. Following the removal of sheep, goats and feral cats, the Western Australian government is sequentially reintroducing 13 native fauna species to restore the globally important natural and cultural values of Dirk Hartog Island (DHI).We use ensembles of ecosystem models to compare 23 alternative reintroduction strategies on DHI, in Western Australia. The reintroduction strategies differ in the order, timing and location of releases on the island. Expert elicitation informed the model structure, allowing for use of different presumed species interaction networks which explicitly incorporated uncertainty in ecosystem dynamics.Our model ensembles predict that almost all of the species (~12.5 of 13, on average) will successfully establish in the ecosystem studied, regardless of which reintroduction strategy is undertaken. The project can therefore proceed with greater confidence and flexibility regarding the reintroduction strategy. However, the identity of the at‐risk species varies between strategies, and depends on the structure of the species interaction network, which is quite uncertain. The model ensembles also offer insights into why some species fail to establish on DHI, predicting that most unsuccessful reintroductions will be the result of competitive interactions with extant species.Synthesis and applications. Our model ensembles allow for the comparison of outcomes between reintroduction strategies and between different species interaction networks. This framework allows for inclusion of high uncertainty in dynamics. Finally, an ensemble modelling approach also creates a foundation for formal adaptive management as reintroduction projects proceed. [ABSTRACT FROM AUTHOR]

Ecosystems at high latitudes are exposed to some of the highest rates of climate warming on earth, and freshwater ecosystems in those regions are already experiencing extended ice‐free seasons and warmer waters. The dominant fish species in these ecosystems are cold‐water salmonids, which play a central ecological role in lake ecosystems, where they are often exposed to size‐selective fisheries that truncate their size and age distributions, making them potentially vulnerable to exploitation and environmental perturbations.Here, we address the combined effects of climate‐induced water temperature increase (using regionally downscaled climate models based on the RCP‐4.5 and RCP‐8.5 climate scenarios together with an air‐to‐water temperature model) and gillnet harvesting, over the period from 1950 to 2100, on the somatic growth, demography and vulnerability of Arctic charr Salvelinus alpinus (L.), using an eco‐genetic individual‐based model. The model captures successive annual life‐history events, including the key processes of size‐dependent mortality, age‐ and size‐dependent maturation described by a probabilistic reaction norm, temperature‐dependent growth, size‐dependent reproduction and density‐dependent recruitment.Our model predicts that higher water temperatures will increase the somatic growth of Arctic charr, leading to larger body size at age and increased stock biomass: for RCP‐8.5, we predict an 80% increase in stock biomass in the year 2100 relative to the year 2000 in the absence of fishing. Interestingly, this potential increase in biomass in future climate scenarios will be partially masked by harvesting: for a fishing mortality of 0.3 year−1, we predict a mere 40% increase in stock biomass in 2100 relative to 2000. Despite the predicted increase in stock biomass, yield will increase substantially only when fishing mortality is low. In addition, climate warming will accentuate the age‐truncation effect of harvesting, which will target younger individuals, including immatures, thus elevating the vulnerability of the population to environmental perturbations.Synthesis and applications. Our model‐based analyses highlight the combined effects of climate change and size‐selective fishing, emphasizing the emerging vulnerability of fish populations to multiple stressors. We recommend carefully climate‐adapted management strategies permitting only a narrow range of gillnet mesh sizes for inland fisheries at high latitudes. [ABSTRACT FROM AUTHOR]

Dynamic management (DM) is a novel approach to spatial management that aligns scales of environmental variability, animal movement and human uses. While static approaches to spatial management rely on one‐time assessments of biological, environmental, economic, and/or social conditions, dynamic approaches repeatedly assess conditions to produce regularly updated management recommendations. Owing to this complexity, particularly regarding operational challenges, examples of applied DM are rare. To implement DM, scientific methodologies are operationalized into tools, i.e., self‐contained workflows that run automatically at a prescribed temporal frequency (e.g., daily, weekly, monthly).Here we present a start‐to‐finish framework for operationalizing DM tools, consisting of four stages: Acquisition, Prediction, Dissemination, and Automation. We illustrate this operationalization framework using an applied DM tool as a case study.Our DM tool operates in near real‐time and was designed to maximize target catch and minimize bycatch of non‐target and protected species in a US‐based commercial fishery. It is important to quantify the sensitivity of DM tools to missing data, because dissemination streams for observed (i.e., remotely sensed or directly sampled) data can experience delays or gaps. To address this issue, we perform a detailed example sensitivity analysis using our case study tool.Synthesis and applications. Dynamic management (DM) tools are emerging as viable management solutions to accommodate the biological, environmental, economic, and social variability in our fundamentally dynamic world. Our four‐stage operationalization framework and case study can facilitate the implementation of DM tools for a wide array of resource and disturbance management objectives. Dynamic management (DM) tools are emerging as viable management solutions to accommodate the biological, environmental, economic, and social variability in our fundamentally dynamic world. Our four‐stage operationalization framework and case study can facilitate the implementation of DM tools for a wide array of resource and disturbance management objectives. [ABSTRACT FROM AUTHOR]

Passively dispersed species, such as many plant species, rely on abiotic forces to achieve movement. Despite the fact that such species form the base of many terrestrial and oceanic food webs, these species are often overlooked by conservation management plans because it is assumed that they are dispersed easily and widely, and therefore are relatively unthreatened by anthropogenic influences in comparison with poorly dispersing species., To quantify the threat presented by habitat degradation and destruction to passively dispersed species, we present a theoretical population model of passively dispersed organisms that incorporates the effects of increased dispersal-associated mortality. We examine how the effects of habitat degradation and destruction depend on species' dispersal tactics, specifically: highly dispersive, highly retentive or spatially biased., Contrary to prevailing intuition, highly dispersive species may suffer the strongest absolute negative demographic effects in the face of changes in human-modified landscapes. These results suggest that passively dispersed organisms may be more threatened by landscape modification than previously thought, and should be more carefully considered in plans for conservation and management., Synthesis and applications. Since passively dispersed species cannot choose their ultimate settling location, they are therefore less likely to arrive at suitable habitat in landscapes that are degraded or destroyed. If we do not understand the implications for population dynamics of demographic costs of dispersal in fragmented landscapes, we may fail to predict declines of apparently widespread species, including many wind-dispersed plants and insects, benthic invertebrates and airborne microbes. Conservation and management plans should consider loss of propagules to unsuitable habitat for highly dispersive as well as poorly dispersive species, to avoid potential errors. [ABSTRACT FROM AUTHOR]

1. Large differences exist in the predictions of plant responses to climate change among models that consider population variation and those that do not. Models that treat species as homogeneous entities typically predict net positive impacts of climate change on temperate forest productivity, while most studies that consider adaptive genetic variation within species conclude that the impacts of climate change on forest productivity will be negative. 2. We present a modelling approach that predicts plant responses to climate change using both ecological and genetic variables. The model uses growth data from multi-site provenance trials together with climate data for provenance source locations and test sites to predict distribution and productivity of tree populations under climate change. We used an extensive lodgepole pine Pinus contorta provenance data set to illustrate the model. 3. Spatially explicit predictions of the impacts of climate change on production were developed and suggested that different populations of lodgepole pine will respond very differently to climate change. Large production losses will be seen in many areas, although modest production increases may occur in some areas by 2085. The model further projects a significant redistribution of the species’ potential habitat northwards and upwards in altitude over the next several decades. 4. Synthesis and applications. This study points to the need to consider population differences when modelling biotic responses to climate change, and suggests that climate change will render populations maladapted in many areas. The model also provides a key tool potentially to mitigate climate change impacts by identifying populations expected to be best adapted throughout the next generation of forests. Finally, the study highlights the value of wide-ranging long-term provenance tests in addressing key issues in ecology and climate change. [ABSTRACT FROM AUTHOR]

1. A large proportion of the world's land surface is extensively managed for livestock production. In areas where livestock systems are becoming more intensive, a major challenge is to predict those plant species likely to decline, persist or increase as a result of agricultural intensification. 2. Most analyses develop inferences for frequent or abundant species, or rely on intensive studies of single species. A promising approach is to identify plant traits related to disturbance to enable inference to be made about changes in plant community composition. We used a Bayesian hierarchical model to analyse the response to agricultural intensification of 494 plant species of pastures and woodlands in southern Australia, and to identify how simple species’ traits (life form, growth form and species origin) influence those responses. 3. The probability of occurrence of most species declined along the two intensification gradients, grazing intensity and soil phosphorous concentration, although the occurrence of a greater proportion of species was negatively correlated with soil phosphorous. Responses could be broadly predicted from both plant origin and plant traits, in particular growth form. 4. Native perennial geophytes, ferns and shrubs were most negatively affected by both gradients, while exotic annual grasses and forbs were more tolerant. Along the phosphorous gradient, 24 of the 30 most negatively affected plant species were native geophytes. Mean within-group responses masked considerable within- and between-species variation, particularly for the exotic species group which included species that responded both negatively and positively to intensification. 5. Synthesis and applications. The hierarchical model described here provides a powerful method for estimating individual plant responses and identifying how species’ traits influence those responses. Plant species native to southern Australia are sensitive to grazing and phosphorous apparently due to a shared evolutionary history of low grazing intensity and low phosphorous soils. Invading exotic plants have faced strongly contrasting ecological filters, leading to a greater diversity of responses. Where grazing systems have been most intense, a small suite of exotics dominate. Maintaining native and functional plant diversity will necessitate limits being placed on intensive livestock management systems. [ABSTRACT FROM AUTHOR]

1. The ecological niche is a fundamental biological concept. Modelling species’ niches is central to numerous ecological applications, including predicting species invasions, identifying reservoirs for disease, nature reserve design and forecasting the effects of anthropogenic and natural climate change on species’ ranges. 2. A computational analogue of Hutchinson's ecological niche concept (the multidimensional hyperspace of species’ environmental requirements) is the support of the distribution of environments in which the species persist. Recently developed machine-learning algorithms can estimate the support of such high-dimensional distributions. We show how support vector machines can be used to map ecological niches using only observations of species presence to train distribution models for 106 species of woody plants and trees in a montane environment using up to nine environmental covariates. 3. We compared the accuracy of three methods that differ in their approaches to reducing model complexity. We tested models with independent observations of both species presence and species absence. We found that the simplest procedure, which uses all available variables and no pre-processing to reduce correlation, was best overall. Ecological niche models based on support vector machines are theoretically superior to models that rely on simulating pseudo-absence data and are comparable in empirical tests. 4. Synthesis and applications. Accurate species distribution models are crucial for effective environmental planning, management and conservation, and for unravelling the role of the environment in human health and welfare. Models based on distribution estimation rather than classification overcome theoretical and practical obstacles that pervade species distribution modelling. In particular, ecological niche models based on machine-learning algorithms for estimating the support of a statistical distribution provide a promising new approach to identifying species’ potential distributions and to project changes in these distributions as a result of climate change, land use and landscape alteration. [ABSTRACT FROM AUTHOR]