Global dataset of soil organic carbon in tidal marshes.

Tidal marshes store large amounts of organic carbon in their soils. Field data quantifying soil organic carbon (SOC) stocks provide an important resource for researchers, natural resource managers, and policy-makers working towards the protection, restoration, and valuation of these ecosystems. We collated a global dataset of tidal marsh soil organic carbon (MarSOC) from 99 studies that includes location, soil depth, site name, dry bulk density, SOC, and/or soil organic matter (SOM). The MarSOC dataset includes 17,454 data points from 2,329 unique locations, and 29 countries. We generated a general transfer function for the conversion of SOM to SOC. Using this data we estimated a median (± median absolute deviation) value of 79.2 ± 38.1 Mg SOC ha-1 in the top 30 cm and 231 ± 134 Mg SOC ha-1 in the top 1 m of tidal marsh soils globally. This data can serve as a basis for future work, and may contribute to incorporation of tidal marsh ecosystems into climate change mitigation and adaptation strategies and policies.

A global and regional view of the opportunity for climate-smart mariculture.

Food systems and the communities they support are increasingly challenged by climate change and the need to arrest escalating threats through mitigation and adaptation. To ensure climate change mitigation strategies can be implemented effectively and to support substantial gains in greenhouse gas emissions reduction, it is, therefore, valuable to understand where climate-smart strategies might be used for best effect. We assessed mariculture in 171 coastal countries for vulnerabilities to climate change (12 indicators) and opportunities to deliver climate mitigation outcomes (nine indicators). We identified Northern America and Europe as having comparatively lower regional vulnerability and higher opportunity for impact on climate mitigation. Australia, Canada, France, Italy, Japan, Republic of Korea, New Zealand, Norway and the United States of America were identified as well-positioned to advance strategies linked to mariculture. However, the nature of vulnerabilities and opportunities within and between all regions and countries varied, due to the formation of existing mariculture, human development factors and governance capacity. Our analysis demonstrates that global discussion will be valuable to motivating climate-smart approaches associated with mariculture, but to ensure these solutions contribute to a resilient future, for industry, ecosystems and communities, local adaptation will be needed to address constraints and to leverage local prospects. This article is part of the theme issue 'Nurturing resilient marine ecosystems'.

A targeted capture approach to generating reference sequence databases for chloroplast gene regions.

Metabarcoding has improved the way we understand plants within our environment, from their ecology and conservation to invasive species management. The notion of identifying plant taxa within environmental samples relies on the ability to match unknown sequences to known reference libraries. Without comprehensive reference databases, species can go undetected or be incorrectly assigned, leading to false-positive and false-negative detections. To improve our ability to generate reference sequence databases, we developed a targeted capture approach using the OZBaits_CP V1.0 set, designed to capture chloroplast gene regions across the entirety of flowering plant diversity. We focused on generating a reference database for coastal temperate plant species given the lack of reference sequences for these taxa. Our approach was successful across all specimens with a target gene recovery rate of 92%, which was achieved in a single assay (i.e., samples were pooled), thus making this approach much faster and more efficient than standard barcoding. Further testing of this database highlighted 80% of all samples could be discriminated to family level across all gene regions with some genes achieving greater resolution than others-which was also dependent on the taxon of interest. Thus, we demonstrate the importance of generating reference sequences across multiple chloroplast gene regions as no single loci are sufficient to discriminate across all plant groups. The targeted capture approach outlined in this study provides a way forward to achieve this.

Climate-Friendly Seafood: The Potential for Emissions Reduction and Carbon Capture in Marine Aquaculture.

Aquaculture is a critical food source for the world's growing population, producing 52% of the aquatic animal products consumed. Marine aquaculture (mariculture) generates 37.5% of this production and 97% of the world's seaweed harvest. Mariculture products may offer a climate-friendly, high-protein food source, because they often have lower greenhouse gas (GHG) emission footprints than do the equivalent products farmed on land. However, sustainable intensification of low-emissions mariculture is key to maintaining a low GHG footprint as production scales up to meet future demand. We examine the major GHG sources and carbon sinks associated with fed finfish, macroalgae and bivalve mariculture, and the factors influencing variability across sectors. We highlight knowledge gaps and provide recommendations for GHG emissions reductions and carbon storage, including accounting for interactions between mariculture operations and surrounding marine ecosystems. By linking the provision of maricultured products to GHG abatement opportunities, we can advance climate-friendly practices that generate sustainable environmental, social, and economic outcomes.

Evaluation of a popular spatial cumulative impact assessment method for marine systems: A seagrass case study.

Human activities put stress on our oceans and with a growing global population, the impact is increasing. Stressors rarely act in isolation, with the majority of marine areas being impacted by multiple, concurrent stressors. Marine spatial cumulative impact assessments attempt to estimate the collective impact of multiple stressors on marine environments. However, this is difficult given how stressors interact with one another, and the variable response of ecosystems. As a result, assumptions and generalisations are required when attempting to model cumulative impacts. One fundamental assumption of the most commonly applied, semi-quantitative cumulative impact assessment method is that a change in modelled cumulative impact is correlated with a change in ecosystem condition. However, this assumption has rarely been validated with empirical data. We tested this assumption using a case study of seagrass in a large, inverse estuary in South Australia (Spencer Gulf). We compared three different seagrass condition indices, based on survey data collected in the field, to scores from a spatial cumulative impact model for the study area. One condition index showed no relationship with cumulative impact, whilst the other two indices had very small, negative relationships with cumulative impact. These results suggest that one of the most commonly used methods for assessing cumulative impacts on marine systems is not robust enough to accurately reflect the effect of multiple stressors on seagrasses; possibly due to the number and generality of assumptions involved in the approach. Future methods should acknowledge the complex relationships between stressors, and the impact these relationships can have on ecosystems. This outcome highlights the need for greater evaluation of cumulative impact assessment outputs and the need for data-driven approaches. Our results are a caution for marine scientists and resource managers who may rely on spatial cumulative impact assessment outputs for informing policy and decision-making.

Identifying island safe havens to prevent the extinction of the World's largest lizard from global warming.

The Komodo dragon (Varanus komodoensis) is an endangered, island-endemic species with a naturally restricted distribution. Despite this, no previous studies have attempted to predict the effects of climate change on this iconic species. We used extensive Komodo dragon monitoring data, climate, and sea-level change projections to build spatially explicit demographic models for the Komodo dragon. These models project the species' future range and abundance under multiple climate change scenarios. We ran over one million model simulations with varying model parameters, enabling us to incorporate uncertainty introduced from three main sources: (a) structure of global climate models, (b) choice of greenhouse gas emission trajectories, and (c) estimates of Komodo dragon demographic parameters. Our models predict a reduction in range-wide Komodo dragon habitat of 8%-87% by 2050, leading to a decrease in habitat patch occupancy of 25%-97% and declines of 27%-99% in abundance across the species' range. We show that the risk of extirpation on the two largest protected islands in Komodo National Park (Rinca and Komodo) was lower than other island populations, providing important safe havens for Komodo dragons under global warming. Given the severity and rate of the predicted changes to Komodo dragon habitat patch occupancy (a proxy for area of occupancy) and abundance, urgent conservation actions are required to avoid risk of extinction. These should, as a priority, be focused on managing habitat on the islands of Komodo and Rinca, reflecting these islands' status as important refuges for the species in a warming world. Variability in our model projections highlights the importance of accounting for uncertainties in demographic and environmental parameters, structural assumptions of global climate models, and greenhouse gas emission scenarios when simulating species metapopulation dynamics under climate change.

A meta-analysis of multiple stressors on seagrasses in the context of marine spatial cumulative impacts assessment.

Humans are placing more strain on the world's oceans than ever before. Furthermore, marine ecosystems are seldom subjected to single stressors, rather they are frequently exposed to multiple, concurrent stressors. When the combined effect of these stressors is calculated and mapped through cumulative impact assessments, it is often assumed that the effects are additive. However, there is increasing evidence that different combinations of stressors can have non-additive impacts, potentially leading to synergistic and unpredictable impacts on ecosystems. Accurately predicting how stressors interact is important in conservation, as removal of certain stressors could provide a greater benefit, or be more detrimental than would be predicted by an additive model. Here, we conduct a meta-analysis to assess the prevalence of additive, synergistic, and antagonistic stressor interaction effects using seagrasses as case study ecosystems. We found that additive interactions were the most commonly reported in seagrass studies. Synergistic and antagonistic interactions were also common, but there was no clear way of predicting where these non-additive interactions occurred. More studies which synthesise the results of stressor interactions are needed to be able to generalise interactions across ecosystem types, which can then be used to improve models for assessing cumulative impacts.

Outstanding Challenges in the Transferability of Ecological Models.

Predictive models are central to many scientific disciplines and vital for informing management in a rapidly changing world. However, limited understanding of the accuracy and precision of models transferred to novel conditions (their 'transferability') undermines confidence in their predictions. Here, 50 experts identified priority knowledge gaps which, if filled, will most improve model transfers. These are summarized into six technical and six fundamental challenges, which underlie the combined need to intensify research on the determinants of ecological predictability, including species traits and data quality, and develop best practices for transferring models. Of high importance is the identification of a widely applicable set of transferability metrics, with appropriate tools to quantify the sources and impacts of prediction uncertainty under novel conditions.

Functional acclimation across microgeographic scales in Dodonaea viscosa.

Intraspecific plant functional trait variation provides mechanistic insight into persistence and can infer population adaptive capacity. However, most studies explore intraspecific trait variation in systems where geographic and environmental distances co-vary. Such a design reduces the certainty of trait-environment associations, and it is imperative for studies that make trait-environment associations be conducted in systems where environmental distance varies independently of geographic distance. Here we explored trait variation in such a system, and aimed to: (i) quantify trait variation of parent and offspring generations, and associate this variation to parental environments; (ii) determine the traits which best explain population differences; (iii) compare parent and offspring trait-trait relationships. We characterized 15 plant functional traits in eight populations of a shrub with a maximum separation ca. 100 km. Populations differed markedly in aridity and elevation, and environmental distance varied independently of geographic distance. We measured traits in parent populations collected in the field, as well as their offspring reared in greenhouse conditions. Parent traits regularly associated with their environment. These associations were largely lost in the offspring generation, indicating considerable phenotypic plasticity. An ordination of parent traits showed clear structure with strong influence of leaf area, specific leaf area, stomatal traits, isotope δ13C and δ15N ratios, and Narea, whereas the offspring ordination was less structured. Parent trait-trait correlations were in line with expectations from the leaf economic spectrum. We show considerable trait plasticity in the woody shrub over microgeographic scales (<100 km), indicating it has the adaptive potential within a generation to functionally acclimate to a range of abiotic conditions. Since our study shrub is commonly used for restoration in southern Australia and local populations do not show strong genetic differentiation in functional traits, the potential risks of transferring seed across the broad environmental conditions are not likely to be a significant issue.

Capturing expert uncertainty in spatial cumulative impact assessments.

Understanding the spatial distribution of human impacts on marine environments is necessary for maintaining healthy ecosystems and supporting 'blue economies'. Realistic assessments of impact must consider the cumulative impacts of multiple, coincident threats and the differing vulnerabilities of ecosystems to these threats. Expert knowledge is often used to assess impact in marine ecosystems because empirical data are lacking; however, this introduces uncertainty into the results. As part of a spatial cumulative impact assessment for Spencer Gulf, South Australia, we asked experts to estimate score ranges (best-case, most-likely and worst-case), which accounted for their uncertainty about the effect of 32 threats on eight ecosystems. Expert scores were combined with data on the spatial pattern and intensity of threats to generate cumulative impact maps based on each of the three scoring scenarios, as well as simulations and maps of uncertainty. We compared our method, which explicitly accounts for the experts' knowledge-based uncertainty, with other approaches and found that it provides smaller uncertainty bounds, leading to more constrained assessment results. Collecting these additional data on experts' knowledge-based uncertainty provides transparency and simplifies interpretation of the outputs from spatial cumulative impact assessments, facilitating their application for sustainable resource management and conservation.

Eight habitats, 38 threats and 55 experts: Assessing ecological risk in a multi-use marine region.

Identifying the relative risk human activities pose to a habitat, and the ecosystem services they provide, can guide management prioritisation and resource allocation. Using a combination of expert elicitation to assess the probable effect of a threat and existing data to assess the level of threat exposure, we conducted a risk assessment for 38 human-mediated threats to eight marine habitats (totalling 304 threat-habitat combinations) in Spencer Gulf, Australia. We developed a score-based survey to collate expert opinion and assess the relative effect of each threat to each habitat, as well as a novel and independent measure of knowledge-based uncertainty. Fifty-five experts representing multiple sectors and institutions participated in the study, with 6 to 15 survey responses per habitat (n = 81 surveys). We identified key threats specific to each habitat; overall, climate change threats received the highest risk rankings, with nutrient discharge identified as a key local-scale stressor. Invasive species and most fishing-related threats, which are commonly identified as major threats to the marine environment, were ranked as low-tier threats to Spencer Gulf, emphasising the importance of regionally-relevant assessments. Further, we identified critical knowledge gaps and quantified uncertainty scores for each risk. Our approach will facilitate prioritisation of resource allocation in a region of increasing social, economic and environmental importance, and can be applied to marine regions where empirical data are lacking.

Tick exposure and extreme climate events impact survival and threaten the persistence of a long-lived lizard.

Assessing the impacts of multiple, often synergistic, stressors on the population dynamics of long-lived species is becoming increasingly important due to recent and future global change. Tiliqua rugosa (sleepy lizard) is a long-lived skink (>30 years) that is adapted to survive in semi-arid environments with varying levels of parasite exposure and highly seasonal food availability. We used an exhaustive database of 30 years of capture-mark-recapture records to quantify the impacts of both parasite exposure and environmental conditions on the lizard's survival rates and long-term population dynamics. Lizard abundance was relatively stable throughout the study period; however, there were changing patterns in adult and juvenile apparent survival rates, driven by spatial and temporal variation in levels of tick exposure and temporal variation in environmental conditions. Extreme weather events during the winter and spring seasons were identified as important environmental drivers of survival. Climate models predict a dramatic increase in the frequency of extreme hot and dry winter and spring seasons in our South Australian study region; from a contemporary probability of 0.17 up to 0.47-0.83 in 2080 depending on the emissions scenario. Our stochastic population model projections showed that these future climatic conditions will induce a decline in the abundance of this long-lived reptile of up to 67% within 30 years from 2080, under worst case scenario modelling. The results have broad implications for future work investigating the drivers of population dynamics and persistence. We highlight the importance of long-term data sets and accounting for synergistic impacts between multiple stressors. We show that predicted increases in the frequency of extreme climate events have the potential to considerably and negatively influence a long-lived species, which might previously have been assumed to be resilient to environmental perturbations.