Fire is an integral component of ecosystems globally and a tool that humans have harnessed for millennia. Altered fire regimes are a fundamental cause and consequence of global change, impacting people and the biophysical systems on which they depend. As part of the newly emerging Anthropocene, marked by human-caused climate change and radical changes to ecosystems, fire danger is increasing, and fires are having increasingly devastating impacts on human health, infrastructure, and ecosystem services. Increasing fire danger is a vexing problem that requires deep transdisciplinary, trans-sector, and inclusive partnerships to address. Here, we outline barriers and opportunities in the next generation of fire science and provide guidance for investment in future research. We synthesize insights needed to better address the long-standing challenges of innovation across disciplines to (i) promote coordinated research efforts; (ii) embrace different ways of knowing and knowledge generation; (iii) promote exploration of fundamental science; (iv) capitalize on the “firehose” of data for societal benefit; and (v) integrate human and natural systems into models across multiple scales. Fire science is thus at a critical transitional moment. We need to shift from observation and modeled representations of varying components of climate, people, vegetation, and fire to more integrative and predictive approaches that support pathways toward mitigating and adapting to our increasingly flammable world, including the utilization of fire for human safety and benefit. Only through overcoming institutional silos and accessing knowledge across diverse communities can we effectively undertake research that improves outcomes in our more fiery future.

Pathogens and parasites (henceforth “pathogens”) can make up a large percentage of the biomass found in ecosystems, and therefore, their impacts on ecosystem processes should be prominent. Pathogens influence ecosystem processes by affecting the abundance or phenotype of hosts and through direct contributions to ecosystem production. However, there has been little quantitative synthesis of the relative effect sizes of these impacts on ecosystem processes. This study presents a systematic review and meta‐analysis of pathogen effects on primary production, secondary production, and biogeochemical cycles. We find that the effects of pathogens on ecosystem processes were greater where pathogens influenced host or community abundance or biomass than when they influenced phenotypes. Pathogen impacts on primary production were larger than on secondary production or biogeochemical cycles. By contrast, we detected no general differences in effect sizes across host or pathogen taxon or ecosystem type (terrestrial vs. aquatic). While we have found potential evidence of publication bias against negative results, a well‐known issue in meta‐analyses, our work nonetheless shows that the available literature under‐represents some taxa and geographic regions. To better understand the extent and magnitude of pathogen impacts on ecosystem processes, future research is needed in four areas. First, research is needed on the most understudied systems, including bacteria and viruses, as well as tropical ecosystems. A second priority is research seeking to understand how key components of ecosystem variation, including age (time of ecological continuity), productivity, and species diversity and composition, may interact to mediate pathogen impacts. Third, we suggest expanding on work examining how pathogen effects are influenced by climate change, species introductions, deforestation, and other human impacts. Fourth, we expect that host coinfection influences ecosystem processes in ways that cannot always be predicted based on studies of single infections. To enable others to build on this work, we make available the data we extracted from the literature, with the code for computing effect sizes.

Metabolic syndrome is becoming more common worldwide. Studies suggest environmental pollution, including traffic noise, might be linked with metabolic syndrome. This study sought to evaluate how noise exposure is linked to the development of metabolic syndrome and its components in Taiwan. Using data from a cohort of 42,509 participants and Cox proportional hazards regression models, the effects of noise exposure on metabolic syndrome and its components were quantified. After adjustment for covariates (age, gender, body mass index, and physical activity), the hazard ratio for metabolic syndrome was 1.13 (95% CI: 1.04–1.22) for medium noise exposure and 1.24 (95% CI: 1.13–1.36) for high noise exposure. Noise exposure was also positively associated with all of metabolic syndrome’s components. This finding suggests noise exposure might contribute to metabolic syndrome and its components. Policies aiming to reduce noise pollution might reduce the risks of metabolic syndrome and its components.

Context: Unoccupied aerial systems/vehicles (UAS/UAV, a.k.a. drones) have become an increasingly popular tool for ecological research. But much of the recent research is concerned with developing mapping and detection approaches, with few studies attempting to link UAS data to ecosystem processes and function. Landscape ecologists have long used high resolution imagery and spatial analyses to address ecological questions and are therefore uniquely positioned to advance UAS research for ecological applications. Objectives: The review objectives are to: (1) provide background on how UAS are used in landscape ecological studies, (2) identify major advancements and research gaps, and (3) discuss ways to better facilitate the use of UAS in landscape ecology research. Methods: We conducted a systematic review based on PRISMA guidelines using key search terms that are unique to landscape ecology research. We reviewed only papers that applied UAS data to investigate questions about ecological patterns, processes, or function. Results: We summarize metadata from 161 papers that fit our review criteria. We highlight and discuss major research themes and applications, sensors and data collection techniques, image processing, feature extraction and spatial analysis, image fusion and satellite scaling, and open data and software. Conclusion: We observed a diversity of UAS methods, applications, and creative spatial modeling and analysis approaches. Key aspects of UAS research in landscape ecology include modeling wildlife micro-habitats, scaling of ecosystem functions, landscape and geomorphic change detection, integrating UAS with historical aerial and satellite imagery, and novel applications of spatial statistics.