The alpine treeline is the highest elevation at which trees can grow, which is assumed to be controlled by temperature and globally predictable. However, many upper forest limits (UFLs) do not reach this elevation, due natural factors and human disturbance. A large elevational distance between UFLs and the climatic treeline can indicate that human activities have removed mountain forest or prevented it from tracking treeline under climate warming. We therefore aimed to map these distances globally as an indication of the conservation status of upper‐montane ecosystems. To obtain a reliable baseline and to evaluate global treeline predictability, we validated an existing treeline‐elevation model with 1241 globally‐distributed observations. Treeline was predicted well, but notably at too‐low elevations in some dry subtropical and very oceanic temperate regions, especially southern Patagonia. After correcting these non‐random geographical patterns, we determined the elevational distance between corrected modelled treelines and observed UFLs at a 30‐arc‐second resolution. Globally, UFLs were located 358 ± 313 m below treeline elevation, with 58% of UFL pixels located within 500 m, with smallest distances in the boreal zones and largest in tropical and subtropical America and Africa. Elevational distance was positively related to fire frequency in the last 20 years and to human footprint. We thus show that 1) the climatic predictability of treeline elevation is less globally universal than previously thought and requires more elaborate models or better climate data, and 2) the vast majority of current upper forest limits are located below treeline elevation, partly for natural reasons, but probably largely due to millennia of human use of high‐elevation ecosystems. These results provide a basis for further research into the drivers of global treeline position, for the conservation and management of montane and alpine areas, and for understanding under which conditions UFLs can move upwards in response to climate change.
The Arctic is warming at approximately four times the global rate, driving widespread decline in sea ice extent, thickness, and seasonal duration. The ecological consequences of these rapid changes remain poorly understood within the Last Ice Area, a region that includes northern Ellesmere Island and the northwest of Greenland, projected to retain summer sea ice the longest and to serve as a refuge for ice‐associated species. Our understanding of distributional shifts for ice‐associated species often comes from research at the southern extent of their ranges; however, knowledge of these shifts in the northern extents of their ranges remains limited. We conducted aerial surveys between 2018 and 2024 in both spring and summer to quantify spatio‐temporal variation in the density of the most abundant ice‐associated mesopredator in the Arctic, ringed seals Pusa hispida , and to model their habitat use relative to several habitat characteristics. Ringed seal densities varied spatially and temporally across the survey area, with the highest densities occurring in Archer Fiord in summer. Densities across the study area were still relatively low compared to more southern areas of the Arctic. Ringed seal abundance in summer was higher in deeper waters and closer to marine‐terminating glaciers, reflecting the species' ecological requirements such as prey availability during the intensive summer foraging period. Ringed seal abundance in both spring and summer was not influenced by sea ice concentration, likely because this variable remains relatively constant year‐round in the Last Ice Area. The density estimates and the habitat use model presented in this study offer novel insights into seal–habitat seasonal relationships in the Last Ice Area, providing a valuable index for monitoring potential future changes in ringed seal habitat use in this ecologically important region of the Arctic and across the broader extent of their range.
Understanding the macroecological drivers of biodiversity and ecosystem stability, i.e. the ability of an ecosystem to maintain its structure and function, is crucial to inform how ecosystems can be managed and conserved effectively. Here, we conducted a field survey of plant and soil biota (bacteria, fungi, and nematodes) and estimated the temporal stability of aboveground productivity (ecosystem stability, proxied by normalized difference vegetation index [NDVI]) in 378 plots along an aridity gradient at a 1200 × 1200 km area in the Qinghai‐Tibetan Plateau. We found that ecosystem stability first increased then decreased with thresholds at aridity 0.24 along the spatial gradient. Plant diversity was positively related to ecosystem stability across the aridity gradient, whereas soil biodiversity, particularly fungi, was positively associated with ecosystem stability only under more water‐limited conditions (aridity > 0.2). Mechanisms for maintaining ecosystem stability shifted from plant community to soil fungal community with thresholds at aridity 0.2 along the aridity gradient. Our results underscore that plant and soil biota should be particularly maintained for ecosystem stability under less and more water‐limited conditions, respectively.
Predicting and mapping connectivity are essential for ecology and conservation in the face of environmental change. Yet connectivity modeling is challenging because it requires understanding how organisms successfully move across complex landscapes. GPS tracking increasingly produces detailed data on movement trajectories and fine‐scale resource selection, yet it remains unclear how to reliably link such data to connectivity models without relying on individual‐based simulations. Currently, most analytical connectivity models are not built from movement parameters that are estimable from GPS‐tracking data, and none capture directional persistence in movement that is often observed in movement trajectories, such as directed movement during dispersal and migration. We address these gaps by advancing new models using the connectivity framework of spatial absorbing Markov chains (SAMC) to directly link step selection and directional persistence in movement to connectivity. We unite these concepts by altering transition matrices used in connectivity models and by extending the SAMC to accommodate correlated random walks through a re‐parameterization using spatial line graphs. We illustrate these advances with simulations and an example using the Florida panther Puma concolor coryi . We find that the SAMC captures expectations from individual‐based simulations of step selection and can be several orders of magnitude faster. Modeling connectivity of the Florida panther with directional persistence that captures correlated random walks predicts faster movement across the landscape, leading to greater expected connectivity. Our results demonstrate that common current practices do not correctly relate step‐selection functions to resistance used in connectivity models, and that incorporating directionality into connectivity efforts leads to greater realism for modeling but at a computational cost. We provide guidance on when this realism may be needed and how to efficiently link step selection with connectivity. Taken together, we offer a generalizable and powerful framework to reliably link movement trajectories to connectivity across landscapes.
Species with small ranges tend to occur with other range‐limited species in biodiversity hotspots, a pattern that has motivated both macroecological theory and practical conservation planning. However, not all range‐limited species follow this pattern. Some occur in otherwise species‐poor ‘coldspots' where maps of rarity‐weighted richness (RWR) may systematically undervalue their habitat. I used maps of suitable habitat for 1719 species of mammals, birds, amphibians, and reptiles in the contiguous United States at ~ 1 and ~ 50‐km resolution to investigate the relationship between range size of species and co‐occurring RWR. I compared the ability of RWR to efficiently represent species to a complementarity‐based algorithm (core area zonation, CAZ), and a new metric I introduce here based on maximum rarity of species (maxR), which ranks locations based on occurrence of the most range‐limited species. Range size was negatively correlated with co‐occurring RWR across all taxa and resolutions, confirming that species with small ranges tend to co‐occur with other range‐limited species. Despite high correlations among prioritization methods, they differed modestly in performance. CAZ most efficiently represented species across nearly all taxa, while maxR generally outperformed RWR when counting species based on presence or low thresholds of proportional habitat. When representation of a species was based on higher proportional thresholds of each species range, RWR tended to more efficiently represent more species than maxR. Comparisons between maxR and RWR methods revealed range‐limited species that occur in relative coldspots, including 53 species listed as ‘Threatened' or ‘Endangered' under the U.S. Endangered Species Act. Mapping maximum rarity should not replace complementarity‐based algorithms for spatial conservation planning but does offer an easily computed diagnostic for detecting range‐limited species in coldspots of biodiversity for use in biodiversity mapping.
