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Animals interact with and impact ecosystem biogeochemical cycling—processes known as zoogeochemistry. While the deposition of various animal materials (e.g. carcasses and faeces) has been shown to create nutrient hotspots and alter nutrient cycling and storage, the inputs from parturition (i.e. calving) have yet to be explored. We examine the effects of ungulate parturition, which often occurs synchronously during spring green‐up and therefore aligns with increased plant nitrogen demand in temperate biomes.

Impacts of zoogeochemical inputs are likely context‐dependent, where differences in material quality, quantity and the system of deposition modulate their impacts. Plant mycorrhizal associations, especially, create different nutrient‐availability contexts, which can modify the effects of nutrient inputs. We, therefore, hypothesize that mycorrhizal associations modulate the consequences of parturition on soil nutrient dynamics and nitrogen pools.

We established experimental plots that explore the potential of two kinds of zoogeochemical inputs deposited at ungulate parturition (placenta and natal fluid) in forest microsites dominated by either ericoid mycorrhizal (ErM) or ectomycorrhizal (EcM) plants. We assess how these inputs affect rates of nutrient cycling and nitrogen content in various ecosystem pools, using isotope tracers to track the fate of nitrogen inputs into plant and soil pools.

Parturition treatments accelerate nutrient cycling processes and increase nitrogen contents in the plant leaf, stem and fine root pools. The ecosystem context strongly modulates these effects. Microsites dominated by ErM plants mute parturition treatment impacts on most nutrient cycling processes and plant pools. Both plant–fungal associations are, however, equally efficient at retaining nitrogen, although retention of nitrogen in the parturition treatment plots was more than two times lower than in control plots.

Our results highlight the potential importance of previously unexamined nitrogen inputs from animal inputs, such as those from parturition, in contributing to fine‐scale heterogeneity in nutrient cycling and availability. Animal inputs should therefore be considered, along with their interactions with plant mycorrhizal associations, in terms of how zoogeochemical dynamics collectively affect nutrient heterogeneity in ecosystems.

 

Soil organic matter (SOM) stocks, decom- position and persistence are largely the product of controls that act locally. Yet the controls are shaped and interact at multiple spatiotemporal scales, from which macrosystem patterns in SOM emerge. Theory on SOM turnover recognizes the resulting spatial and temporal conditionality in the effect sizes of controls that play out across macrosystems, and couples them through evolutionary and community assembly pro- cesses. For example, climate history shapes plant functional traits, which in turn interact with contem- porary climate to influence SOM dynamics. Selection and assembly also shape the functional traits of soil decomposer communities, but it is less clear how in turn these traits influence temporal macrosystem patterns in SOM turnover. Here, we review evidence that establishes the expectation that selection and assembly should generate decomposer communities across macrosystems that have distinct functional effects on SOM dynamics. Representation of this knowledge in soil biogeochemical models affects the magnitude and direction of projected SOM responses under global change. Yet there is high uncertainty and low confidence in these projections. To address these issues, we make the case that a coordinated set of empirical practices are required which necessitate (1) greater use of statistical approaches in biogeochem- istry that are suited to causative inference; (2) long- term, macrosystem-scale, observational and experi- mental networks to reveal conditionality in effect sizes, and embedded correlation, in controls on SOM turnover; and (3) use of multiple measurement grains to capture local- and macroscale variation in controls and outcomes, to avoid obscuring causative understanding through data aggregation. When employed together, along with process-based models to synthesize knowledge and guide further empirical work, we believe these practices will rapidly advance understanding of microbial controls on SOM and improve carbon cycle projections that guide policies on climate adaptation and mitigation. 

Society increasingly demands accurate predictions of complex ecosystem processes under novel conditions to address environmental challenges. However, obtaining the process‐level knowledge required to do so does not necessarily align with the burgeoning use in ecology of correlative model selection criteria, such as Akaike information criterion. These criteria select models based on their ability to reproduce outcomes, not on their ability to accurately represent causal effects. Causal understanding does not require matching outcomes, but rather involves identifying model forms and parameter values that accurately describe processes. We contend that researchers can arrive at incorrect conclusions about cause‐and‐effect relationships by relying on information criteria. We illustrate via a specific example that inference extending beyond prediction into causality can be seriously misled by information‐theoretic evidence. Finally, we identify a solution space to bridge the gap between the correlative inference provided by model selection criteria and a process‐based understanding of ecological systems.

 

As the primary decomposers of organic material in terrestrial ecosystems, fungi are critical agents of the global carbon cycle. Yet our ability to link fungal community composition to ecosystem functioning is constrained by a limited understanding of the factors accounting for different wood decomposition rates among fungi. Here we examine which traits best explain fungal decomposition ability by combining detailed trait-based assays on 34 saprotrophic fungi from across North America in the laboratory with a 5-y field study comprising 1,582 fungi isolated from 74 decomposing logs. Fungal growth rate (hyphal extension rate) was the strongest single predictor of fungal-mediated wood decomposition rate under laboratory conditions, and accounted for up to 27% of the in situ variation in decomposition in the field. At the individual level, decomposition rate was negatively correlated with moisture niche width (an indicator of drought stress tolerance) and with the production of nutrient-mineralizing extracellular enzymes. Together, these results suggest that decomposition rates strongly align with a dominance-tolerance life-history trade-off that was previously identified in these isolates, forming a spectrum from slow-growing, stress-tolerant fungi that are poor decomposers to fast-growing, highly competitive fungi with fast decomposition rates. Our study illustrates how an understanding of fungal trait variation could improve our predictive ability of the early and midstages of wood decay, to which our findings are most applicable. By mapping our results onto the biogeographic distribution of the dominance-tolerance trade-off across North America, we approximate broad-scale patterns in intrinsic fungal-mediated wood decomposition rates.