Veronica F. Frans

Ecological communities, and especially metacommunities, are complex and dynamic entities. Resolving the processes and mechanisms that shape these systems remains a central challenge in ecology. This challenge is compounded by the increasing entanglement of mechanisms, processes, and emergent patterns of biodiversity as scales of space, time, and biological organization expand. Here, we define and contextualize key issues, describe recent progress, and identify remaining challenges in interpreting basic metacommunity data and using predictive models to link processes to patterns. We identify two contrasting modeling strategies for complex metacommunities – top-down and bottom-up – and consider how they guide different approaches to pattern-to-process inference. We find substantial progress in connecting pattern and process through improved data repeatability and scaling, enhanced analytical tools to quantify patterns, and increasingly sophisticated theoretical models that address ecological complexity. However, accurately matching observable patterns with process-oriented theory remains a persistent challenge. Finally, we identify potential pipelines connecting process and pattern and highlight areas for future progress.

Ecological communities, and especially metacommunities, are complex and dynamic entities. Resolving the processes and mechanisms that shape these systems remains a central challenge in ecology. This challenge is compounded by the increasing entanglement of mechanisms, processes, and emergent patterns of biodiversity as scales of space, time, and biological organization expand. Here, we define and contextualize key issues, recent progress, and remaining challenges in interpreting basic metacommunity data and using predictive models to link processes to patterns. We find substantial progress in connecting pattern and process through improved data repeatability and scaling, enhanced analytical tools to quantify patterns, and increasingly sophisticated theoretical models that address ecological complexity. However, accurately matching observable patterns with process-oriented theory remains a persistent challenge. We identify potential pipelines connecting process and pattern and highlight areas for future progress.

Ecological communities, and especially metacommunities, are complex and dynamic entities. Resolving the processes and mechanisms that shape these systems remains a central challenge in ecology. This challenge is compounded by the increasing entanglement of mechanisms, processes, and emergent patterns of biodiversity as scales of space, time, and biological organization expand. Here, we define and contextualize key issues, recent progress, and remaining challenges in interpreting basic metacommunity data and using predictive models to link processes to patterns. We find substantial progress in connecting pattern and process through improved data repeatability and scaling, enhanced analytical tools to quantify patterns, and increasingly sophisticated theoretical models that address ecological complexity. However, accurately matching observable patterns with process-oriented theory remains a persistent challenge. We identify potential pipelines connecting process and pattern and highlight areas for future progress.

The complex relationship between temperature and schistosomiasis, an environmentally mediated neglected tropical disease affecting 250 million people globally, with hyperendemicity mostly in Africa, is poorly characterized. Here, we explored how seasonal temperature fluctuation affects the persistence, dynamics, and geographic distribution of schistosomiasis in Africa. We used a temperature-sensitive, mechanistic model of schistosomiasis dynamics that accounts for the adaptive behaviors of intermediate snail hosts and derived the disease’s thermal response curve for different patterns of seasonal temperature fluctuations. Changing the amplitude of seasonal temperature fluctuations can influence both the thermal optimum and critical thermal thresholds which imply accurately drawing the thermal response curves requires accounting for seasonality in addition to mean annual temperature. Moreover, our simulations can reproduce the documented persistence of schistosomiasis at locations with strong seasonal temperature fluctuations and mean annual temperatures near or above the critical thermal maxima for snail hosts only when snail adaptive behavior (e.g., aestivation, movement into cooler depths or shade) is included in the model. These results suggest that future climate change impacting the amplitude and timing of these fluctuations will likely alter the future geographic distribution of schistosomiasis in African regions. Our work demonstrates that a comprehensive understanding of schistosomiasis and, potentially, other environmentally mediated diseases in Africa, necessitates the inclusion of seasonal temperature fluctuations and host behavioral adaptations in process-based mechanistic models.

Understanding species distributions is a global priority for mitigating environmental pressures from human activities. Ample studies have identified key environmental (climate and habitat) predictors and the spatial scales at which they influence species distributions. However, regarding human influence, such understandings are largely lacking. Here, to advance knowledge concerning human influence on species distributions, we systematically reviewed species distribution modelling (SDM) articles and assessed current modelling efforts. We searched 12,854 articles and found only 1,429 articles using human predictors within SDMs. Collectively, these studies of >58,000 species used 2,307 unique human predictors, suggesting that in contrast to environmental predictors, there is no ‘rule of thumb’ for human predictor selection in SDMs. The number of human predictors used across studies also varied (usually one to four per study). Moreover, nearly half the articles projecting to future climates held human predictors constant over time, risking false optimism about the effects of human activities compared with climate change. Advances in using human predictors in SDMs are paramount for accurately informing and advancing policy, conservation, management and ecology. We show considerable gaps in including human predictors to understand current and future species distributions in the Anthropocene, opening opportunities for new inquiries. We pose 15 questions to advance ecological theory, methods and real-world applications.

Seesus is an open-source Python package that evaluates whether a textual expression aligns with the concept of sustainability as defined by the United Nations Sustainable Development Goals (SDGs). It labels a statement with the 17 SDGs as well as 169 specific targets and categorizes the statement into social, environmental, or economic sustainability. Compared to existing SDG classification software in Python, seesus is the only one that is tuned to a finer scale-the SDG target level-and offers transparency and controllability, allowing users to examine and customize the syntax of text classification according to their needs.

The relationship between biodiversity and ecosystem function (BEF) captivates ecologists, but the factors responsible for the direction of this relationship remain unclear. While higher ecosystem functioning at higher biodiversity levels (‘positive BEF’) is not universal in nature, negative BEF relationships seem puzzlingly rare. Here, we develop a dynamical consumer‐resource model inspired by microbial decomposer communities in pitcher plant leaves to investigate BEF. We manipulate microbial diversity via controlled colonization and measure their function as total ammonia production. We test how niche partitioning among bacteria and other ecological processes influence BEF in the leaves. We find that a negative BEF can emerge from reciprocal interspecific inhibition in ammonia production causing a negative complementarity effect, or from competitive hierarchies causing a negative selection effect. Absent these factors, a positive BEF was the typical outcome. Our findings provide a potential explanation for the rarity of negative BEF in empirical data.

The relationship between biodiversity and ecosystem function (BEF) captivates ecologists, but the factors responsible for the direction of this relationship remain unclear. While higher ecosystem functioning at higher biodiversity levels (‘positive BEF’) is not universal in nature, negative BEF relationships seem puzzlingly rare. Here, we develop a dynamical consumer-resource model inspired by microbial decomposer communities in pitcher plant leaves to investigate BEF. We manipulate microbial diversity via controlled colonization and measure their function as total ammonia production. We test how niche partitioning among bacteria and other ecological processes influence BEF in the leaves. We find that a negative BEF can emerge from reciprocal interspecific inhibition in ammonia production causing a negative complementarity effect, or from competitive hierarchies causing a negative selection effect. Absent these factors, a positive BEF was the typical outcome. Our findings provide a potential explanation for the rarity of negative BEF in empirical data.

The global interest in moving towards a sustainable future has grown exponentially at all levels. The United Nations' Sustainable Development Goals (SDGs), adopted by world leaders in 2015, provide an integrated framework to track progress toward sustainability (UN, 2019). Textual data, such as public statements posted on websites, organization reports, and scientific publications, is a rich source for evaluating the planned and ongoing efforts, as well as achievements towards sustainability. However, no computational tool exists to date that can accurately and efficiently identify SDG-related statements from these large amounts of text data. To fill this gap, we developed the SDGdetector package in R (R Core Team, 2021) to map textual data to specific goals and targets under the UN SDG framework for quantitative analysis. This is the first open-source, high-resolution, and high-accuracy analytical package that can identify which and how many SDG goals and targets are declared in any type of text-based data frame or corpus. This package thus enables a unique way to monitor individuals' and organizations' commitments and efforts towards advancing the 17 SDGs and 169 associated targets.