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Abstract Studies of annual patterns of ecosystem metabolism in rivers have primarily been conducted in temperate ecosystems, and little is known about metabolic regimes of tropical rivers. We estimated ecosystem metabolism in four nonwadeable rivers in southern México that varied in size and the extent of human disturbance. The smaller rivers with limited human disturbance showed reduced gross primary production (GPP; 1.0 and 1.7 g O₂m⁻² d⁻¹), ecosystem respiration (ER; − 1.9 g O₂m⁻²d⁻¹), and net ecosystem production (NEP) approaching autotrophy (− 0. 8 and − 0.3 g O₂m⁻²d⁻¹) relative to rivers draining larger, more disturbed catchments (GPP, 1.2 and 2.7 g O₂m⁻²d⁻¹; ER, − 5.7 and − 6.9 g O₂m⁻²d⁻¹; NEP, − 3.8 and − 3.7 g O₂m⁻²d⁻¹). In all rivers, GPP and ER varied seasonally with discharge. The smaller rivers exhibited a distinct pattern of greater and sustained GPP during periods of low discharge, a seasonal metabolic regime we describe as “flow decline.” In general, process–discharge relationships exhibited thresholds, with an initial decline in GPP and ER, with increasing discharge and an increase in ER at higher flows. Relative to larger and more disturbed watersheds, smaller rivers showed a more constrained metabolic fingerprint. Annual NEP (− 1033 and − 641 g C m⁻² yr⁻¹) in the larger rivers was more negative than the global average, supporting evidence from other studies that tropical rivers are greater contributors to CO²emissions than temperate ecosystems. Our study indicates that hydrological seasonality is a major driver of metabolism in tropical rivers. 

Abstract Anthropogenic climate change threatens the structure and function of ecosystems throughout the globe, but many people are still skeptical of its existence. Traditional “knowledge deficit model” thinking has suggested that providing the public with more facts about climate change will assuage skepticism. However, presenting evidence contrary to prior beliefs can have the opposite effect and result in a strengthening of previously held beliefs, a phenomenon known as biased assimilation or a backfire effect. Given this, strategies for effectively communicating about socioscientific issues that are politically controversial need to be thoroughly investigated. We randomly assigned 184 undergraduates from an environmental science class to one of three experimental conditions in which we exposed them to short videos that employed different messaging strategies: (a) an engaging science lecture, (b) consensus messaging, and (c) elite cues. We measured changes in student perceptions of climate change across five constructs (content knowledge, acceptance of scientific consensus, perceived risk, support for action, and climate identity) before and after viewing videos. Consensus messaging outperformed the other two conditions in increasing student acceptance of the scientific consensus, perceived risk of climate change, and climate identity, suggesting this may be an effective strategy for communicating the gravity of anthropogenic climate change. Elite cues outperformed the engaging science lecture condition in increasing student support for action on climate, with politically conservative students driving this relationship, suggesting that the messenger is more important than the message if changing opinions about the necessity of action on climate change is the desired outcome. Relative to the other conditions, the engaging science lecture did not support change in students' perceptions on climate, but appealing to student respect for authority produced positive results. Notably, we observed no decline in students' acceptance of climate science, indicating that none of the conditions induced a backfire effect. 

Onsite wastewater treatment systems (OWTSs), such as septic systems, are widely used in the United States, with 16.4% of households relying on them. OWTSs process approximately 4 billion gallons of wastewater per day, yet only about half is safely treated. Identifying factors contributing to impaired functionality is crucial for developing effective management and monitoring strategies and protecting environmental and human health. This study uses a machine learning approach and a unique data set from Athens-Clarke County, Georgia, to predict OWTS failures based on environmental and system-specific variables. The three main predictors of impaired OWTS function were the number of bedrooms (25.4%), height above stream (18.6%), and system age (16.2%), with both older and younger systems prone to failure. Our findings suggest there is a need to reevaluate construction guidelines for effective tank and drainfield sizing, placement, and construction, and our findings indicate that additional training for permitters, installers, and homeowners may be beneficial. Our work demonstrates the power of using machine learning to assess OWTS function, which can better enable local governments and environment managers to identify areas in need of infrastructure and educational investment with limited data and highlights the data types that local jurisdictions should prioritize for collection. 

Rivers and streams contribute to global carbon cycling by decomposing immense quantities of terrestrial plant matter. However, decomposition rates are highly variable and large-scale patterns and drivers of this process remain poorly understood. Using a cellulose-based assay to reflect the primary constituent of plant detritus, we generated a predictive model (81% variance explained) for cellulose decomposition rates across 514 globally distributed streams. A large number of variables were important for predicting decomposition, highlighting the complexity of this process at the global scale. Predicted cellulose decomposition rates, when combined with genus-level litter quality attributes, explain published leaf litter decomposition rates with high accuracy (70% variance explained). Our global map provides estimates of rates across vast understudied areas of Earth and reveals rapid decomposition across continental-scale areas dominated by human activities. 

Novel in their scope and intensity, human‐mediated changes in genetic diversity through directed gene transfer technologies and longer standing human‐driven selective pressures, such as land‐use change, species introductions, mass extinctions, and broad application of antibiotics, are combining to reorganize mechanisms of evolution. The evolutionary consequences of anthropogenic change can be observed across levels of biological organization and are influencing the rate of micro‐ and macroevolutionary changes, as well as feedback among them. This may have large‐scale effects on the provisioning of ecosystem services, food security, and human health. Here, we summarize several of the ecological implications of human modification of evolutionary dynamics, focusing specifically on emerging molecular technologies, to highlight some of the challenges in predicting subsequent changes in the world’s ecosystems.