Mosses are an early lineage of the plant kingdom, with around 13,000 species. Although an important part of biodiversity, providing crucial ecosystem services, many species are threatened with extinction. However, only circa 300 species have so far had their extinction risk evaluated globally for the IUCN Red List. Functional traits are known to help predict the extinction risk of species in other plant groups. In this study, a matrix of 15 functional traits was produced for 723 moss species from around the world to evaluate the potential of such predictability. Binary generalized linear models showed that monoicous species were more likely to be threatened than dioicous species, and the presence of a sporophyte (sexual reproduction), vegetative reproduction and an erect (straight) capsule instead of a pendent (immersed) one lowers the risk of species extinction. A longer capsule, seta and stem length, as well as broader substrate breadth, are indicative of species with a lower risk of extinction. The best-performing models fitted with few traits were able to predict extinction risks of species with good accuracy. These models applied to Data Deficient (DD) species proved how useful they may be to speed up the IUCN Red List assessment process while reducing the number of listed DD species, by selecting species most in need of a full, detailed assessment. Some traits tested in this study are a novelty in conservation research on mosses, opening new possibilities for future studies. The traits studied and the models presented here are a significant contribution to the knowledge of mosses at risk of extinction and will help to improve conservation efforts.

Seasonally tropical dry forests (SDTFs) in the American tropics are a highly diverse yet poorly understood and endangered ecosystem scattered from Northern Mexico to Southern Argentina. One floristic element of the STDFs is the genus Magoniella (Polygonaceae), which includes two liana species, M. laurifolia and M. obidensis, which have winged fruits and are distributed from Costa Rica to Southern Brazil. In a field expedition to the SDTFs of the Colombian Caribbean in 2015, morphologically distinctive individuals of Magoniella were found. In this study, we investigated the species boundaries within Magoniella and determined the phylogenetic position of these morphologically distinctive individuals in the tribe Triplaridae. We compiled morphological trait data across 19 specimens of both species and produced newly sequenced nuclear-plastid DNA data for M. obidensis. Morphometric analyses revealed significant differences in fruit length and perianth size among individuals from the Colombian Caribbean compared to M. obidensis and bract length when compared to M. laurifolia. Maximum likelihood analysis of non-conflicting nuclear and plastid datasets placed the Colombian Caribbean individuals as sister to M. obidensis with maximum statistical support. Additionally, pairwise sequence comparisons of the nuclear ribosomal ITS and the lfy2i loci consistently showed 15-point mutations (10 transitions, five transversions) and six 2 bp-long substitutions that differ between M. obidensis and the Colombian Caribbean individuals. Our morphological and molecular evidence thus suggests that the Colombian Caribbean individuals of Magoniella represent a divergent population from M. laurifolia and M. obidensis, which we describe and illustrate as a new species, M. chersina. Additionally, we provide nomenclatural updates for M. laurifolia and M. obidensis. This study highlights the power of combining morphological and molecular evidence in documenting and naming plant diversity.

Climate change is anticipated to cause species to shift their ranges upward and poleward, yet space for tracking suitable habitat conditions may be limited for range-restricted species at the highest elevations and latitudes of the globe. Consequently, range-restricted species inhabiting Arctic freshwater ecosystems, where global warming is most pronounced, face the challenge of coping with changing abiotic and biotic conditions or risk extinction. Here, we use an extensive fish community and environmental dataset for 1762 lakes sampled across Scandinavia (mid-1990s) to evaluate the climate vulnerability of Arctic char (Salvelinus alpinus), the world\'s most cold-adapted and northernly distributed freshwater fish. Machine learning models show that abiotic and biotic factors strongly predict the occurrence of Arctic char across the region with an overall accuracy of 89 percent. Arctic char is less likely to occur in lakes with warm summer temperatures, high dissolved organic carbon levels (i.e., browning), and presence of northern pike (Esox lucius). Importantly, climate warming impacts are moderated by habitat (i.e., lake area) and amplified by the presence of competitors and/or predators (i.e., northern pike). Climate warming projections under the RCP8.5 emission scenario indicate that 81% of extant populations are at high risk of extirpation by 2080. Highly vulnerable populations occur across their range, particularly near the southern range limit and at lower elevations, with potential refugia found in some mountainous and coastal regions. Our findings highlight that range shifts may give way to range contractions for this cold-water specialist, indicating the need for pro-active conservation and mitigation efforts to avoid the loss of Arctic freshwater biodiversity.

Assessing the extinction risk of species based on the International Union for Conservation of Nature (IUCN) Red List (RL) is key to guiding conservation policies and reducing biodiversity loss. This process is resource demanding, however, and requires continuous updating, which becomes increasingly difficult as new species are added to the RL. Automatic methods, such as comparative analyses used to predict species RL category, can be an efficient alternative to keep assessments up to date. Using amphibians as a study group, we predicted which species are more likely to change their RL category and thus should be prioritized for reassessment. We used species biological traits, environmental variables, and proxies of climate and land-use change as predictors of RL category. We produced an ensemble prediction of IUCN RL category for each species by combining 4 different model algorithms: cumulative link models, phylogenetic generalized least squares, random forests, and neural networks. By comparing RL categories with the ensemble prediction and accounting for uncertainty among model algorithms, we identified species that should be prioritized for future reassessment based on the mismatch between predicted and observed values. The most important predicting variables across models were species\' range size and spatial configuration of the range, biological traits, climate change, and land-use change. We compared our proposed prioritization index and the predicted RL changes with independent IUCN RL reassessments and found high performance of both the prioritization and the predicted directionality of changes in RL categories. Ensemble modeling of RL category is a promising tool for prioritizing species for reassessment while accounting for models\' uncertainty. This approach is broadly applicable to all taxa on the IUCN RL and to regional and national assessments and may improve allocation of the limited human and economic resources available to maintain an up-to-date IUCN RL.
Uso del análisis comparativo del riesgo de extinción para priorizar la reevaluación de los anfibios en la Lista Roja de la UICN Resumen El análisis del riesgo de extinción de una especie con base en la Lista Roja (LR) de la Unión Internacional para la Conservación de la Naturaleza (UICN) es clave para guiar las políticas de conservación y reducir la pérdida de la biodiversidad. Sin embargo, este proceso demanda recursos y requiere de actualizaciones continuas, lo que se complica conforme se añaden especies nuevas a la LR. Los métodos automáticos, como los análisis comparativos usados para predecir la categoría de la especie en la LR, pueden ser una alternativa eficiente para mantener actualizados los análisis. Usamos a los anfibios como grupo de estudio para predecir cuáles especies tienen mayor probabilidad de cambiar de categoría en la LR y que, por lo tanto, se debería priorizar su reevaluación. Usamos las características biológicas de la especie, las variables ambientales e indicadores climáticos y del cambio de uso de suelo como predictores de la categoría en la LR. Elaboramos una predicción de ensamble de la categoría en la LR de la UICN para cada especie mediante la combinación de cuatro algoritmos diferentes: modelos de vínculo acumulativo, menor número de cuadros filogenéticos generalizados, bosques aleatorios y redes neurales. Con la comparación entre las categorías de la LR y la predicción de ensamble y con considerar la incertidumbre entre los algoritmos identificamos especies que deberían ser prioridad para futuras reevaluaciones con base en el desfase entre los valores predichos y los observados. Las variables de predicción más importantes entre los modelos fueron el tamaño de la distribución de la especie y su configuración espacial, las características biológicas, el cambio climático y el cambio de uso de suelo. Comparamos nuestra propuesta de índice de priorización y los cambios predichos en la LR con las reevaluaciones independientes de la LR de la UICN y descubrimos un buen desempeño tanto para la priorización como para la direccionalidad predicha de los cambios en las categorías de la LR. El modelo de ensamble de la categoría de la LR esa una herramienta prometedora para priorizar la reevaluación de las especies a la vez que considera la incertidumbre del modelo. Esta estrategia puede generalizarse para aplicarse a todos los taxones de la LR de la UICN y a los análisis regionales y nacionales. También podría mejorar la asignación de los recursos humanos y económicos limitados disponibles para mantener actualizada la LR de la UICN.

Von Schmalensee et al. present two concerns about our study. While the first stems from a general disagreement about our simulation methodology, the second is a useful observation of a modelling choice we made that affected simulation outcomes, but in ways that do not invalidate our original conclusions.

In their simulation study, Garcia-Costoya et al. (2023) conclude that evolutionary constraints might aid populations facing climate change. However, we are concerned that this conclusion is largely a consequence of the simulated temperature variation being too small, and, most importantly, that uneven limitations to standing variation disadvantage unconstrained populations.

Endemic island species face heightened extinction risk from climate-driven shifts, yet standard models often underestimate threat levels for those like Quercus alnifolia, an iconic Cypriot oak with pre-adaptations to aridity. Through species distribution modelling, we investigated the potential shifts in its distribution under future climate and land-use change scenarios. Our approach uniquely combines dispersal constraints, detailed soil characteristics, hydrological factors, and anticipated soil erosion data, offering a comprehensive assessment of environmental suitability. We quantified the species\' sensitivity, exposure, and vulnerability to projected changes, conducting a preliminary IUCN extinction risk assessment according to Criteria A and B. Our projections uniformly predict range reductions, with a median decrease of 67.8% by the 2070s under the most extreme scenarios. Additionally, our research indicates Quercus alnifolia\'s resilience to diverse erosion conditions and preference for relatively dry climates within a specific annual temperature range. The preliminary IUCN risk assessment designates Quercus alnifolia as Critically Endangered in the future, highlighting the need for focused conservation efforts. Climate and land-use changes are critical threats to the species\' survival, emphasising the importance of comprehensive modelling techniques and the urgent requirement for dedicated conservation measures to safeguard this iconic species.

Reptiles are an important, yet often understudied, taxon in nature conservation. They play a significant role in ecosystems1 and can serve as indicators of environmental health, often responding more rapidly to human pressures than other vertebrate groups.2 At least 21% of reptiles are currently assessed as threatened with extinction by the IUCN.3 However, due to the lack of comprehensive global assessments until recently, they have been omitted from spatial studies addressing conservation or spatial prioritization (e.g., Rosauer et al.,4,5,6,7,8 Fritz and Rahbek,4,5,6,7,8 Farooq et al.,4,5,6,7,8 Meyer et al., 4,5,6,7,8 and Farooq et al.4,5,6,7,8). One important knowledge gap in conservation is the lack of spatially explicit information on the main threats to biodiversity,9 which significantly hampers our ability to respond effectively to the current biodiversity crisis.10,11 In this study, we calculate the probability of a reptile species in a specific location being affected by one of seven biodiversity threats-agriculture, climate change, hunting, invasive species, logging, pollution, and urbanization. We conducted the analysis at a global scale, using a 50 km × 50 km grid, and evaluated the impact of these threats by studying their relationship with the risk of extinction. We find that climate change, logging, pollution, and invasive species are most linked to extinction risk. However, we also show that there is considerable geographical variation in these results. Our study highlights the importance of going beyond measuring the intensity of threats to measuring the impact of these separately for various biogeographical regions of the world, with different historical contingencies, as opposed to a single global analysis treating all regions the same.

We investigated a system of ordinary differential equations that describes the dynamics of prey and predator populations, taking into account the Allee effect affecting the reproduction of the predator population, and mutual interference amongst predators, which is modeled with the Bazykin-Crowley-Martin (BCM) trophic function. Bifurcation analysis revealed a rich spectrum of bifurcations occurring in the system. In particular, analytical conditions for the saddle-node, Hopf, cusp, and Bogdanov-Takens bifurcations were derived for the model parameters, quantifying the strength of the predator interference, the Allee effect, and the predation efficiency. Numerical simulations verify and illustrate the analytical findings. The main purpose of the study was to test whether the mutual interference in the model with BCM trophic function provides a stabilizing or destabilizing effect on the system dynamics. The obtained results suggest that the model demonstrates qualitatively the same pattern concerning varying the interference strength as other predator-dependent models: both low and very high interference levels increase the risk of predator extinction, while moderate interference has a favorable effect on the stability and resilience of the prey-predator system.

Substantial global restoration commitments are occurring alongside a rapid expansion in land-hungry tropical commodities, including to supply increasing demand for wood products. Future commercial tree plantations may deliver high timber yields, shrinking the footprint of production forestry, but there is an as-yet unquantified risk that plantations may expand into priority restoration areas, with marked environmental costs. Focusing on Brazil-a country of exceptional restoration importance and one of the largest tropical timber producers-we use random forest models and information on the economic, social, and spatial drivers of historic commercial tree plantation expansion to estimate and map the probability of future monoculture tree plantation expansion between 2020 and 2030. We then evaluate potential plantation-restoration conflicts and opportunities at national and biome-scales and under different future production and restoration pathways. Our simulations show that of 2.8 Mha of future plantation expansion (equivalent to plantation expansion 2010-2020), ~78,000 ha (3%) is forecast to occur in the top 1% of restoration priority areas for terrestrial vertebrates, with ~547,500 ha (20%) and ~1,300,000 ha (46%) in the top 10% and 30% of priority areas, respectively. Just ~459,000 ha (16%) of expansion is forecast within low-restoration areas (bottom 30% restoration priorities), and the first 1 Mha of plantation expansion is likely to have disproportionate impacts, with potential restoration-plantation overlap starkest in the Atlantic Forest but prominent in the Pampas and Cerrado as well. Our findings suggest that robust, coherent land-use policies must be deployed to ensure that significant trade-offs between restoration and production objectives are navigated, and that commodity expansion does not undermine the most tractable conservation gains under emerging global restoration agendas. They also highlight the potentially significant role an engaged forestry sector could play in improving biodiversity outcomes in restoration projects in Brazil, and presumably elsewhere.