Global distribution and climate sensitivity of the tropical montane forest nitrogen cycle

Temperature thresholds induce abrupt shifts in biodiversity and ecosystem services in montane ecosystems worldwide

Montane ecosystems are crucial for maintaining global biodiversity and function that sustain life on our planet. Yet, these ecosystems are highly vulnerable to changing temperatures and may undergo critical transitions under ongoing climate change. What we do not know is to what extent montane biodiversity and ecosystem services will respond to local temperature variations in a gradual versus abrupt manner across global environments. To fill this knowledge gap, we conducted a global synthesis, including 4,462 observations from 290 elevation gradients, to investigate how biodiversity (spanning animals and plants) and ecosystem services (including plant production, soil carbon, and fertility) respond to local temperature variations along elevation gradients. We found that nearly one-third of these gradients exhibited abrupt shifts in multiple biodiversity and ecosystem services in response to local variations in temperature along elevation gradients. More specifically, we showed that once a particular local temperature level (~10 °C for mean annual temperature) was reached, even small increases in temperature resulted in dramatic variations in biodiversity and ecosystem services. We further showed that those abrupt shifts in response to local temperature increases were commonly positive for plant and animal diversity, as well as plant production, while soil carbon and fertility more commonly exhibit negative abrupt trends. Our work, based on the most comprehensive empirical evidence available so far, reveals the pervasive abrupt responses of biodiversity and ecosystem services to local temperature variations in montane ecosystems worldwide, highlighting the highly sensitive nature of montane ecosystems in the context of climate change.

A probabilistic approach to estimating timber harvest location

Determining the harvest location of timber is crucial to enforcing international regulations designed to protect natural resources and to tackle illegal logging and associated trade in forest products. Stable isotope ratio analysis (SIRA) can be used to verify claims of timber harvest location by matching levels of naturally occurring stable isotopes within wood tissue to location-specific ratios predicted from reference data ("isoscapes"). However, overly simple models for predicting isoscapes have so far limited the confidence in derived predictions of timber provenance. In addition, most use cases have limited themselves to differentiating between a small number of predetermined location options. Here, we present a new analytic pipeline for SIRA data, designed to predict the harvest location of a wood sample in a continuous, arbitrarily large area. We use Gaussian processes to robustly estimate isoscapes from reference wood samples, and overlay with species distribution data to compute, for every pixel in the study area, the probability of it being the harvest location of the examined timber. This is the first time, to our knowledge, that this approach is applied to determining timber provenance, providing probabilistic results rather than a binary outcome. Additionally, we include an active learning tool to identify locations from which additional reference data would maximize the improvement to model performance, allowing for optimisation of subsequent field efforts. We demonstrate our approach on a set of SIRA data from seven oak species in the United States as a proof of concept. Our method can determine the harvest location up to within 520 km from the true origin of the sample and outperforms the state-of-the-art approach. Incorporating species distribution data improves accuracy by up to 36%. The future sampling locations proposed by our tool decrease the variance of resultant isoscapes by up to 86% more than sampling the same number of locations at random. Accurate prediction of harvest location has the potential to transform worldwide efforts to enforce anti-deforestation legislation and protect natural resources.

Unravelling spatial scale effects on elevational diversity gradients: insights from montane small mammals in Kenya

BACKGROUND

Montane ecosystems play crucial roles as global biodiversity hotspots. However, climatic changes and anthropogenic pressure increasingly threaten the stability of montane community dynamics, such as diversity-elevation interactions, creating a challenge in understanding species biogeography and community ecology dynamics in these crucial conservation areas. We examined how varying sampling spatial grains influence small mammal diversity patterns within Kenya's tallest montane ecosystems.

METHODS

Employing a combination of multidimensional alpha diversity metrics and multisite beta diversity characteristics (species richness, phylogenetic and functional diversity and divergence, and multisite beta diversity) alongside spatial generalized additive multivariate regression analyses, we tested how spatial scaling influences elevational diversity gradient patterns and their associations with environmental and human activity variables.

RESULTS

The diversity-elevation associations were generally homogeneous across spatial grains; however, idiosyncratic patterns emerged across mountains. The total (taxonomic, phylogenetic, and functional) beta diversity, nestedness, and turnover resultant components monotonically increased or decreased with varying spatial grains. The associations between the diversity patterns and the environmental and human footprint variables increased with spatial grain size but also presented variations across mountains and indices. Species richness and phylogenetic and functional richness indices were more strongly influenced by spatial scale variations than were the divergence and community structure indices in both the diversity distribution patterns and their associations with the environmental and human variables.

CONCLUSIONS

The diversity-elevation and diversity-environment (including human activity pressure) relationships across spatial grains suggest that montane small mammal diversity patterns portray subtle but systematic sensitivity to sampling spatial grain variation and underscore the importance of geographical context in shaping these elevational diversity gradients. For improved effectiveness, conservation efforts should consider these spatial effects and the unique geographical background of individual montane ecosystems.

Limiting Resources Define the Global Pattern of Soil Microbial Carbon Use Efficiency

Microbial carbon (C) use efficiency (CUE) delineates the proportion of organic C used by microorganisms for anabolism and ultimately influences the amount of C sequestered in soils. However, the key factors controlling CUE remain enigmatic, leading to considerable uncertainty in understanding soil C retention and predicting its responses to global change factors. Here, we investigate the global patterns of CUE estimate by stoichiometric modeling in surface soils of natural ecosystems, and examine its associations with temperature, precipitation, plant-derived C and soil nutrient availability. We found that CUE is determined by the most limiting resource among these four basic environmental resources within specific climate zones (i.e., tropical, temperate, arid, and cold zones). Higher CUE is common in arid and cold zones and corresponds to limitations in temperature, water, and plant-derived C input, while lower CUE is observed in tropical and temperate zones with widespread limitation of nutrients (e.g., nitrogen or phosphorus) in soil. The contrasting resource limitations among climate zones led to an apparent increase in CUE with increasing latitude. The resource-specific dependence of CUE implies that soils in high latitudes with arid and cold environments may retain less organic C in the future, as warming and increased precipitation can reduce CUE. In contrast, oligotrophic soils in low latitudes may increase organic C retention, as CUE could be increased with concurrent anthropogenic nutrient inputs. The findings underscore the importance of resource limitations for CUE and suggest asymmetric responses of organic C retention in soils across latitudes to global change factors.

Deforestation amplifies climate change effects on warming and cloud level rise in African montane forests

Tropical montane forest ecosystems are pivotal for sustaining biodiversity and essential terrestrial ecosystem services, including the provision of high-quality fresh water. Nonetheless, the impact of montane deforestation and climate change on the capacity of forests to deliver ecosystem services is yet to be fully understood. In this study, we offer observational evidence demonstrating the response of air temperature and cloud base height to deforestation in African montane forests over the last two decades. Our findings reveal that approximately 18% (7.4 ± 0.5 million hectares) of Africa's montane forests were lost between 2003 and 2022. This deforestation has led to a notable increase in maximum air temperature (1.37 ± 0.58 °C) and cloud base height (236 ± 87 metres), surpassing shifts attributed solely to climate change. Our results call for urgent attention to montane deforestation, as it poses serious threats to biodiversity, water supply, and ecosystem services in the tropics.

Revealing Molecular Structures of Nitrogen-Containing Compounds in Dissolved Black Carbon Using Ultrahigh-Resolution Mass Spectrometry Combined with Thermodynamic Calculations

Landscape wildfires generate a substantial amount of dissolved black carbon (DBC) annually, yet the molecular nitrogen (N) structures in DBC are poorly understood. Here, we systematically compared the chemodiversity of N-containing molecules among three different DBC samples from rice straw biochar pyrolyzed at 300, 400, and 500 °C, one leached dissolved organic carbon (LDOC) sample from composted rice straw, and one fire-affected soil dissolved organic matter (SDOMFire) sample using Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS). N-Containing molecules contributed 20.0%, 36.1%, and 43.7% of total compounds in Combined DBC (pooling together the three DBC), LDOC, and SDOMFire, respectively, and molecules with fewer N atoms had higher proportions (i.e., N1 > N2 > N3). The N-containing molecules in Combined DBC were dominated by polycyclic aromatic (62.2%) and aromatic (14.4%) components, while those in LDOC were dominated by lignin-like (50.4%) and aromatic (30.1%) components. The composition and structures of N-containing molecules in SDOMFire were more similar to those in DBC than in LDOC. As the temperature rose, the proportion of the nitrogenous polycyclic aromatic component in DBC significantly increased with concurrent enhanced oxidation and unsaturation of N. As indicated by density functional theory (DFT)-based thermodynamic calculations, the proportion of aliphatic amide N decreased from 23.2% to 7.9%, whereas that of nitroaromatic N increased from 10.0% to 39.5% as the temperature increased from 300 to 500 °C; alternatively, the proportion of aromatic N in the 5/6 membered ring remained relatively stable (∼31%) and that of aromatic amide N peaked at 400 °C (32.7%). Our work first provides a comprehensive and thorough description of molecular N structures of DBC, which helps to better understand and predict their fate and biogeochemical behavior.

A framework for tracing timber following the Ukraine invasion

Scientific testing including stable isotope ratio analysis (SIRA) and trace element analysis (TEA) is critical for establishing plant origin, tackling deforestation and enforcing economic sanctions. Yet methods combining SIRA and TEA into robust models for origin verification and determination are lacking. Here we report a (1) large Eastern European timber reference database (Betula, Fagus, Pinus, Quercus) tailored to sanctioned products following the Ukraine invasion; (2) statistical test to verify samples against a claimed origin; (3) probabilistic model of SIRA, TEA and genus distribution data, using Gaussian processes, to determine timber harvest location. Our verification method rejects 40-60% of simulated false claims, depending on the spatial scale of the claim, and maintains a low probability of rejecting correct origin claims. Our determination method predicts harvest location within 180 to 230 km of true location. Our results showcase the power of combining data types with probabilistic modelling to identify and scrutinize timber harvest location claims.