A temperate Earth-sized planet with tidal heating transiting an M6 star.

Temperate Earth-sized exoplanets around late-M dwarfs offer a rare opportunity to explore under which conditions planets can develop hospitable climate conditions. The small stellar radius amplifies the atmospheric transit signature, making even compact secondary atmospheres dominated by N2 or CO2 amenable to characterization with existing instrumentation1. Yet, despite large planet search efforts2, detection of low-temperature Earth-sized planets around late-M dwarfs has remained rare and the TRAPPIST-1 system, a resonance chain of rocky planets with seemingly identical compositions, has not yet shown any evidence of volatiles in the system3. Here we report the discovery of a temperate Earth-sized planet orbiting the cool M6 dwarf LP 791-18. The newly discovered planet, LP 791-18d, has a radius of 1.03 ± 0.04 R⊕ and an equilibrium temperature of 300-400 K, with the permanent night side plausibly allowing for water condensation. LP 791-18d is part of a coplanar system4 and provides a so-far unique opportunity to investigate a temperate exo-Earth in a system with a sub-Neptune that retained its gas or volatile envelope. On the basis of observations of transit timing variations, we find a mass of 7.1 ± 0.7 M⊕ for the sub-Neptune LP 791-18c and a mass of [Formula: see text] for the exo-Earth LP 791-18d. The gravitational interaction with the sub-Neptune prevents the complete circularization of LP 791-18d's orbit, resulting in continued tidal heating of LP 791-18d's interior and probably strong volcanic activity at the surface5,6.

A machine learning approach to identify barriers in stream networks demonstrates high prevalence of unmapped riverine dams.

Restoring stream ecosystem integrity by removing unused or derelict dams has become a priority for watershed conservation globally. However, efforts to restore connectivity are constrained by the availability of accurate dam inventories which often overlook smaller unmapped riverine dams. Here we develop and test a machine learning approach to identify unmapped dams using a combination of publicly available topographic and geospatial habitat data. Specifically, we trained a random forest classification algorithm to identify unmapped dams using digitally engineered predictor variables and known dam sites for validation. We applied our algorithm to two subbasins in the Hudson River watershed, USA, and quantified connectivity impacts, as well as evaluated a range of predictor sets to examine tradeoffs between classification accuracy and model parameterization effort. The random forest classifier achieved high accuracy in predicting dam sites (true positive rate = 89%, false positive rate = 1.2%) using a subset of variables related to stream slope and presence of upstream lentic habitats. Unmapped dams were prevalent throughout the two test watersheds. In fact, existing dam inventories underestimated the true number of dams by ∼80-94%. Accounting for previously unmapped dams resulted in a 62-90% decrease in dendritic connectivity indices for migratory fishes. Unmapped dams may be pervasive and can dramatically bias stream connectivity information. However, we find that machine learning approaches can provide an accurate and scalable means of identifying unmapped dams that can guide efforts to develop accurate dam inventories, thereby informing and empowering efforts to better manage them.