The significant portion of global terrestrial biodiversity harboured in the mountains is under increasing threat from various anthropogenic impacts. Protecting fragile mountain ecosystems requires understanding how these human disturbances affect biodiversity. As roads and railways are extended further into mountain ecosystems, understanding the long-term impacts of this infrastructure on community composition and diversity gains urgency. We used railway corridors constructed across the mountainous landscapes of the Kashmir Himalaya from 1994 to 2013 to study the effects of anthropogenic disturbance on species distributions and community dynamics. In 2014 and 2017, we collected vegetation data along 31 T-shaped transects laid perpendicular to the railway line, adopting the MIREN (Mountain Invasion Research Network) road survey methodology. Plant communities shifted significantly from 2014 to 2017, potentially because of an ongoing species redistribution after railway construction, driven mainly by declines in both native and non-native species richness, and an increasing abundance of a few non-native species, especially in areas away from the railway track. These patterns indicate an advancing succession, where initially-rare-pioneer species are replaced by increasingly dominant and often non-native competitors, and potentially suggest a trend towards delayed local extinctions after the disturbance event. Native and non-native species richness was negatively correlated with elevation, but that relationship diminished over time, with the abundance of non-natives significantly increasing at higher elevations. Synthesis and applications. Transport corridors seem to facilitate the spread of non-native species to higher elevations, which has serious implications considering the warming mountain tops. Our results indicate that the plant communities next to railways do not reach equilibrium quickly after a disturbance. More than 10 years after railway establishment within Kashmir Himalaya, succession continued, and signs pointed towards a landscape increasingly dominated by non-native species. Our study indicates that the single disturbance event associated with constructing railway in this Himalayan region had large and long-lasting effects on plant communities at and around this transport corridor and suggests the need for a long-term region-wide coordinated monitoring and management program.

Crops worldwide are simultaneously affected by weeds, which reduce yield, and by climate change, which can negatively or positively affect both crop and weed species. While the individual effects of environmental change and of weeds on crop yield have been assessed, the combined effects have not been broadly characterized. To explore the simultaneous impacts of weeds with changes in climate-related environmental conditions on future food production, we conducted a meta-analysis of 171 observations measuring the individual and combined effects of weeds and elevated CO2, drought or warming on 23 crop species. The combined effect of weeds and environmental change tended to be additive. On average, weeds reduced crop yield by 28%, a value that was not significantly different from the simultaneous effect of weeds and environmental change (27%), due to increased variability when acting together. The negative effect of weeds on crop yield was mitigated by elevated CO2 and warming, but added to the negative effect of drought. The impact of weeds with environmental change was also dependent on the photosynthetic pathway of the weed/crop pair and on crop identity. Native and non-native weeds had similarly negative effects on yield, with or without environmental change. Weed impact with environmental change was also independent of whether the crop was infested with a single or multiple weed species. Since weed impacts remain negative under environmental change, our results highlight the need to evaluate the efficacy of different weed management practices under climate change. Understanding that the effects of environmental change and weeds are, on average, additive brings us closer to developing useful forecasts of future crop performance.

Terrestrial ecosystems regulate Earth's climate through water, energy, and biogeochemical transformations. Despite a key role in regulating the Earth system, terrestrial ecology has historically been underrepresented in the Earth system models (ESMs) that are used to understand and project global environmental change. Ecology and Earth system modeling must be integrated for scientists to fully comprehend the role of ecological systems in driving and responding to global change. Ecological insights can improve ESM realism and reduce process uncertainty, while ESMs offer ecologists an opportunity to broadly test ecological theory and increase the impact of their work by scaling concepts through time and space. Despite this mutualism, meaningfully integrating the two remains a persistent challenge, in part because of logistical obstacles in translating processes into mathematical formulas and identifying ways to integrate new theories and code into large, complex model structures. To help overcome this interdisciplinary challenge, we present a framework consisting of a series of interconnected stages for integrating a new ecological process or insight into an ESM. First, we highlight the multiple ways that ecological observations and modeling iteratively strengthen one another, dispelling the illusion that the ecologist's role ends with initial provision of data. Second, we show that many valuable insights, products, and theoretical developments are produced through sustained interdisciplinary collaborations between empiricists and modelers, regardless of eventual inclusion of a process in an ESM. Finally, we provide concrete actions and resources to facilitate learning and collaboration at every stage of data-model integration. This framework will create synergies that will transform our understanding of ecology within the Earth system, ultimately improving our understanding of global environmental change, and broadening the impact of ecological research.

Researchers use both experiments and observations to study the impacts of climate change on ecosystems, but results from these contrasting approaches have not been systematically compared for droughts. Using a meta-analysis and accounting for potential confounding factors, we demonstrate that aboveground biomass responded only about half as much to experimentally imposed drought events as to natural droughts. Our findings indicate that experimental results may underestimate climate change impacts and highlight the need to integrate results across approaches.

In democracies around the world, societies have demonstrated that elections can have major consequences for the environment. In Colombia, the 2022 presidential elections will take place at a time when progress towards peace has stalled and socioeconomic, security, and environmental conditions have deteriorated. The recent declines in these conditions largely coincide with the change of government after the 2018 elections, and the associated rise to power of a party that boycotted the peace negotiations from the beginning. These indicators suggest that 2018 marked the end of a decade of improvements in safety, wealth, and equality-societal factors that can interact with the environment in multiple ways. A spike in assassinations of land and environmental defenders in 2019 and 2020 made Colombia one of the most dangerous places in the world for environmentalists. With the 2022 presidential election, Colombians will once again decide who will govern the country and what new social, economic, and environmental policies will be implemented. In preparation for elections like this, we believe that it is important for scientists with relevant backgrounds to highlight relationships between political events and the environment, to enrich the political debate, help prioritize public resources, and inform policymaking. Here, we provide a multidisciplinary analysis of different socioeconomic and environmental trends that can help inform the public and decision-makers. We intend for this analysis to be useful not only in Colombia, but also to other societies under similar situations, managing biodiversity-rich ecosystems in sociopolitical environments of increasing violence, poverty, and inequality.

Tropical ecosystems strongly influence Earth's climate and weather patterns. Most tropical ecosystems remain warm year-round; nonetheless, their plants undergo seasonal cycles of carbon and water exchange. Previous research has shown the importance of precipitation and radiation as drivers of the seasonality of photosynthetic activity in the tropics. Although data are scarce, field-based studies have found that seasonal cycles at a handful of tropical sites do not match those in the land surface model (LSM) simulations. A comprehensive understanding and model comparison of how seasonal variations in tropical photosynthetic activity relate to climate is lacking. Here, we identify the relationships of precipitation and radiation with satellite-based proxies for photosynthetic activity (e.g., GOME-2 SIF, MAIAC EVI) for the pantropical region. Three dominant and spatially distinct seasonal relationships emerge: photosynthetic activity that is positively correlated with both drivers (36% of tropical pixels), activity that increases following rain but decreases with radiation (28%), and activity that increases following bright seasons but decreases with rain (14%). We compare distributions of these observed relationships with those from LSMs. In general, compared to satellite-based proxies of photosynthetic activity, model simulations of gross primary productivity (GPP) overestimate the extent of positive correlations of photosynthetic activity with water and underestimate positive correlations with radiation. The largest discrepancies between simulations and observations are in the representation of regions where photosynthetic activity increases with radiation and decreases with rain. Our clear scheme for representing the relationship between climate and photosynthetic activity can be used to benchmark tropical seasonality of GPP in LSMs.

Magnetic fields can drastically change predictions of evolutionary models of massive stars via mass-loss quenching, magnetic braking, and efficient angular momentum transport, which we aim to quantify in this work. We use the mesa software instrument to compute an extensive main-sequence grid of stellar structure and evolution models, as well as isochrones, accounting for the effects attributed to a surface fossil magnetic field. The grid is densely populated in initial mass (3-60 M-circle dot), surface equatorial magnetic field strength (0-50 kG), and metallicity (representative of the Solar neighbourhood and the Magellanic Clouds). We use two magnetic braking and two chemical mixing schemes and compare the model predictions for slowly rotating, nitrogen-enriched ('Group 2') stars with observations in the Large Magellanic Cloud. We quantify a range of initial field strengths that allow for producing Group 2 stars and find that typical values (up to a few kG) lead to solutions. Between the subgrids, we find notable departures in surface abundances and evolutionary paths. In our magnetic models, chemical mixing is always less efficient compared to non-magnetic models due to the rapid spin-down. We identify that quasi-chemically homogeneous main sequence evolution by efficient mixing could be prevented by fossil magnetic fields. We recommend comparing this grid of evolutionary models with spectropolarimetric and spectroscopic observations with the goals of (i) revisiting the derived stellar parameters of known magnetic stars, and (ii) observationally constraining the uncertain magnetic braking and chemical mixing schemes.

The Carnegie-Chicago Hubble Program (CCHP) is building a direct path to the Hubble constant (H-0) using Population II stars as the calibrator of the Type Ia supernova (SN;Ia)-based distance scale. This path to calibrate the SNe;Ia is independent of the systematics in the traditional Cepheid-based technique. In this paper, we present the distance to M101, the host to SN 2011fe, using the I-band tip of the red giant branch (TRGB) based on observations from the ACS/WFC instrument on the Hubble Space Telescope. The CCHP targets the halo of M101, where there is little to no host galaxy dust, the red giant branch is isolated from nearly all other stellar populations, and there is virtually no source confusion or crowding at the magnitude of the tip. Applying the standard procedure for the TRGB method from the other works in the CCHP series, we find a foreground-extinction-corrected M101 distance modulus of ?(0);=;29.07;;0.04(stat);;0.05(sys) mag, which corresponds to a distance of D;=;6.52;;0.12(stat);;0.15(sys) Mpc. This result is consistent with several recent Cepheid-based determinations, suggesting agreement between Population I and II distance scales for this nearby SN;Ia host galaxy. We further analyze four archival data sets for M101 that have targeted its outer disk to argue that targeting in the stellar halo provides much more reliable distance measurements from the TRGB method owing to the combination of multiple structural components and heavy population contamination. Application of the TRGB in complex regions will have sources of uncertainty not accounted for in commonly used uncertainty measurement techniques.