Thermodynamic sensitivity of ammonia oxidizers-driven N2O fluxes under oxic-suboxic realms

In terrestrial ecosystems, the nitrogen dynamics, including N₂O production, are majorly regulated by a complex consortium of microbes favored by different substrates and environmental conditions. To better predict the daily, seasonal and annual variation in N₂O fluxes, it is critical to estimate the temperature sensitivity of different ammonia-oxidizing groups under oxic and suboxic conditions prevalent in soils and wetlands. Here, we studied the thermodynamics of N₂O fluxes, via nitrification and nitrifier-denitrification, for two ammonia-oxidizers, archaea (AOA) and bacteria (AOB), across a wide temperature gradient (10-55 °C). Using square root theory (SQRT) and macromolecular rate theory (MMRT) models, we estimated thermodynamic parameters, cardinal temperatures, and maximum temperature sensitivity (T_Smax). The distinction between N₂O pathways was facilitated by microbial-specific inhibitors (PTIO and C₂H₂) and controlled oxygen supply (oxic: ambient; suboxic: ∼4%) environments. We found that nitrification supported by AOA (Nt_A) and AOB (Nt_B) dominated N₂O production in an oxic climate, while only AOB-supported nitrifier-denitrification (ND_B) majorly contributed (>90%) to suboxic N₂O budget. The models predicted significantly higher temperature optima (T_opt) and T_Smax for Nt_A and ND_B compared to Nt_B. Intriguingly, both Nt_B and ND_B exhibited significantly wider temperature ranges than Nt_A. Altogether, our results suggest that temperature and oxygen supply control the dominance of specific AOA- and AOB-supported N₂O pathways in soil and sediments. This emergent understanding can potentially contribute toward novel targeted N₂O inhibitors for GHG mitigation under global warming.

Soil microbiome feedback to climate change and options for mitigation

Microbes are a critical component of soil ecosystems, performing crucial functions in biogeochemical cycling, carbon sequestration, and plant health. However, it remains uncertain how their community structure, functioning, and resultant nutrient cycling, including net GHG fluxes, would respond to climate change at different scales. Here, we review global and regional climate change effects on soil microbial community structure and functioning, as well as the climate-microbe feedback and plant-microbe interactions. We also synthesize recent studies on climate change impacts on terrestrial nutrient cycles and GHG fluxes across different climate-sensitive ecosystems. It is generally assumed that climate change factors (e.g., elevated CO₂ and temperature) will have varying impacts on the microbial community structure (e.g., fungi-to-bacteria ratio) and their contribution toward nutrient turnover, with potential interactions that may either enhance or mitigate each other's effects. Such climate change responses, however, are difficult to generalize, even within an ecosystem, since they are subjected to not only a strong regional influence of current ambient environmental and edaphic conditions, historical exposure to fluctuations, and time horizon but also to methodological choices (e.g., network construction). Finally, the potential of chemical intrusions and emerging tools, such as genetically engineered plants and microbes, as mitigation strategies against global change impacts, particularly for agroecosystems, is presented. In a rapidly evolving field, this review identifies the knowledge gaps complicating assessments and predictions of microbial climate responses and hindering the development of effective mitigation strategies.

Assessing the influence of environmental niche segregation in ammonia oxidizers on N2O fluxes from soil and sediments

Understanding the environmental niche segregation of ammonia-oxidizing archaea (AOA) and bacteria (AOB) and its impact on their relative contributions to nitrification and nitrous oxide (N₂O) production is essential for predicting N₂O dynamics within an ecosystem. Here, we used ammonia oxidizer-specific inhibitors to measure the differential contributions of AOA and AOB to potential ammonia oxidization (PAO) and N₂O fluxes over pH (4.0-9.0) and temperature (10-45 °C) gradients in five soils and three wetland sediments. AOA and AOB activities were differentiated using PTIO (2-phenyl-4,4,5,5-tetramethylimidazoline-1-oxyl 3-oxide), 1-octyne, and acetylene. We used square root growth (SQRT) and macromolecular rate theory (MMRT) models to estimate cardinal temperatures and thermodynamic characteristics for AOA- and AOB-dominated PAO and N₂O fluxes. We found that AOA and AOB occupied different niches for PAO, and soil temperature was the major determinant of niche specialization. SQRT and MMRT models predicted a higher optimum temperature for AOA-dominated PAO and N₂O fluxes compared with those of AOB. Additionally, PAO was dominated by AOA in acidic conditions, whereas both AOA- and AOB-dominated N₂O fluxes decreased with increasing pH. Consequently, net N₂O fluxes (AOA and AOB) under acidic conditions were approximately one to three-fold higher than those observed in alkaline conditions. Moreover, structural equation and linear regression modeling confirmed a significant positive correlation (R² = 0.45, p < 0.01) between PAO and N₂O fluxes. Collectively, these results show the influence of ammonia oxidizer responses to temperature and pH on nitrification-driven N₂O fluxes, highlighting the potential for mitigating N₂O emissions via pH manipulation.

Exploring hybrid consensus models to assess roadkill

Accurate information provided by reliable models is essential for identifying hotspots and mitigating roadkill. However, existing methods, such as kernel density estimation (KDE) and maximum entropy modeling (ME) may individually identify only a subset of the suitable locations for mitigation, because KDE cannot detect hotspots once local abundances are depressed, and ME may only partially identify current hotspots due to imperfect discrimination skill. Here, we propose a hybrid consensus modeling (HCM) approach that leverages the strengths of both KDE and ME by using their consensus to identify the core subset of hotspots. We collected herpetofauna (amphibians and reptiles) roadkill data (N = 839) along four roads in Taiwan (R.O.C.) to evaluate the statistical performance and theoretical mitigation efficiency of HCM, KDE and ME, and to compare the allocation among roads, spatial clustering, and environmental conditions in the identified hotspots. HCM was applied on the herpetofauna dataset as well as separately on amphibians and reptiles. Although the discrimination skill of KDE and ME models for both target clades together was good to excellent (AUC_KDE = 0.944, AUC_ME = 0.822), the highest theoretical mitigation efficiency, was displayed by HCM Consensus (2.89), followed by KDE (2.58), and ME (1.91). Furthermore, we show that theoretical mitigation efficiency increases with decreasing spatial clustering (Moran's I). Given pervasive budget constraints, we recommend to limit permanent mitigation measures such as fenced culverts to HCM Consensus hotspots, temporary measures to KDE hotspots, and to target additional monitoring at ME hotspots.

Topsoil microbial community structure responds to land cover type and environmental zone in the Western Pacific region

Soil encompasses diverse microbial communities that are essential for fundamental ecosystem functions such as biogeochemical cycling. To better understand underlying biogeochemical processes, it is necessary to know the structure of soil archaeal and bacterial communities and their responses to edaphic and climate variables within and across various land cover types (LCTs) and environmental zones (ENZs). Here we sampled eighty-nine sites across five ENZs and four LCTs within the Western Pacific region. Through leveraging the second-generation sequencing of topsoil samples, we showed that α-diversity (taxonomic diversity) of archaea strongly varied within LCTs, whereas bacterial α-diversity was significantly controlled by both LCT and ENZ. Soil archaea and bacteria showed global niche differentiation associated with contrasting diversity responses to latitude and differential responses of microbial diversity patterns to edaphic and climate variables within LCTs and ENZs. In contrast to α-diversity, microbial β-diversity (the compositional dissimilarity between sites) was majorly governed by ENZs, particularly for archaea (P < 0.01). Our results highlight the importance of LCTs and ENZs for understanding soil microbial contributions to nutrient dynamics and ecosystem resilience under land-use intensification and climate change.

Climate and land cover shape the fungal community structure in topsoil

Globally, soils are subject to radical changes in their biogeochemistry as rampant deforestation and other forms of land use and climate change continue to transform planet Earth. To better understand soil ecosystem functioning, it is necessary to understand the responses of soil microbial diversity and community structure to changing climate, land cover, and associated environmental variables. With next-generation sequencing, we investigated changes in topsoil fungi community structure among different land cover types (from Forest to Cropland) and climate zones (from Hot to Cold zones) in the Western Pacific Region. We demonstrated that climate zones substantially (P = 0.001) altered the soil fungal beta-diversity (change in community composition), but not alpha-diversity (taxonomical diversity). In particular, precipitation, temperature, and also latitude were the best predictors of beta-diversity. Individual fungal classes displayed divergent but strong responses to climate variables and latitude, suggesting niche differentiation at lower taxonomic levels. We also demonstrated that fungal taxonomic diversity differentially responded to latitude across land covers: fungal diversity increased towards lower latitudes in the Forest and Cropland (R² = 0.19) but increased towards both lower and higher latitudes in Fallow land (R² = 0.45). Further, alpha-diversity was significantly influenced by soil pH in Forest (P = 0.02), and by diurnal temperature range in Fallow land and mean annual precipitation in Cropland. Collectively, various land cover types had differential influence on the latitude diversity gradient, while climate, and to some extent, edaphic variables, were crucial in shaping soil fungal community structure. Our results can also serve as a baseline for estimating global change impacts on fungal community structure in the Western Pacific Region.

Two alternative evaluation metrics to replace the true skill statistic in the assessment of species distribution models

Model evaluation metrics play a critical role in the selection of adequate species distribution models for conservation and for any application of species distribution modelling (SDM) in general. The responses of these metrics to modelling conditions, however, are rarely taken into account. This leads to inadequate model selection, downstream analyses and uniformed decisions. To aid modellers in critically assessing modelling conditions when choosing and interpreting model evaluation metrics, we analysed the responses of the True Skill Statistic (TSS) under a variety of presence-background modelling conditions using purely theoretical scenarios. We then compared these responses with those of two evaluation metrics commonly applied in the field of meteorology which have potential for use in SDM: the Odds Ratio Skill Score (ORSS) and the Symmetric Extremal Dependence Index (SEDI). We demonstrate that (1) large cell number totals in the confusion matrix, which is strongly biased towards ‘true’ absences in presence-background SDM and (2) low prevalence both compromise model evaluation with TSS. This is since (1) TSS fails to differentiate useful from random models at extreme prevalence levels if the confusion matrix cell number total exceeds ~30,000 cells and (2) TSS converges to hit rate (sensitivity) when prevalence is lower than ~2.5%. We conclude that SEDI is optimal for most presence-background SDM initiatives. Further, ORSS may provide a better alternative if absence data are available or if equal error weighting is strictly required.