With limited monetary resources available for nature conservation, policy‐makers need to be able to prioritize conservation objectives. This has traditionally been done using qualitative ecological criteria. However, since declines in species and habitats are largely the result of socio‐economic and political forces, human preferences and values should also be taken into account. An environmental economics technique, contingent valuation, provides one way of doing this by quantifying public willingness‐to‐pay towards specific conservation objectives. In this paper, the use of this approach for quantifying public preferences towards the UK Biodiversity Action Plans for four different British mammal species is considered. The species included are the Red Squirrel Sciurus vulgaris, the Brown Hare Lepus europaeus, the Otter Lutra lutra and the Water Vole Arvicola terrestris. Willingness‐to‐pay for conservation was increased by the inclusion of the Otter among the species, membership of an environmental organization and awareness of the general and species‐specific threats facing British mammals. It was reduced by the presence of the Brown Hare among the species being considered. These findings for British mammals are compared with other willingness‐to‐pay studies for mammal conservation worldwide. Willingness‐to‐pay tends to be greater for marine mammals than terrestrial ones, and recreational users of species (tourists or hunters) are generally more willing than residents to pay towards species conservation. The choice of technique for eliciting willingness‐to‐pay from respondents is also shown to be highly significant. Willingness‐to‐pay values for British mammals derived from contingent valuation are sensitive to the species included rather than merely symbolic. This indicates that, with care, such measures can be used as a reliable means of quantifying public preferences for conservation, and therefore contributing to the decision‐making process. However, irrespective of the internal consistency of contingent valuation, the validity of the approach, especially for use in nature conservation, is disputed. Willingness‐to‐pay is likely to reflect many interrelated factors such as ethical and moral values, knowledge and tradition, and monetary values may not be an adequate representation of these broader considerations. Willingness‐to‐pay approaches should therefore be used in addition to, rather than in place of, expert judgements and more deliberative approaches towards policy decision‐making for conservation.

Gross primary productivity (GPP) of wooded ecosystems (forests and savannas) is central to the global carbon cycle, comprising 67%–75% of total global terrestrial GPP. Climate change may alter this flux by increasing the frequency of temperatures beyond the thermal optimum of GPP (Topt). We examined the relationship between GPP and air temperature (Ta) in 17 wooded ecosystems dominated by a single plant functional type (broadleaf evergreen trees) occurring over a broad climatic gradient encompassing five ecoregions across Australia ranging from tropical in the north to Mediterranean and temperate in the south. We applied a novel boundary‐line analysis to eddy covariance flux observations to (a) derive ecosystem GPP–Ta relationships and Topt (including seasonal analyses for five tropical savannas); (b) quantitatively and qualitatively assess GPP–Ta relationships within and among ecoregions; (c) examine the relationship between Topt and mean daytime air temperature (MDTa) across all ecosystems; and (d) examine how down‐welling short‐wave radiation (Fsd) and vapour pressure deficit (VPD) influence the GPP–Ta relationship. GPP–Ta relationships were convex parabolas with narrow curves in tropical forests, tropical savannas (wet season), and temperate forests, and wider curves in temperate woodlands, Mediterranean woodlands, and tropical savannas (dry season). Ecosystem Topt ranged from 15℃ (temperate forest) to 32℃ (tropical savanna—wet and dry seasons). The shape of GPP–Ta curves was largely determined by daytime Ta range, MDTa, and maximum GPP with the upslope influenced by Fsd and the downslope influenced by VPD. Across all ecosystems, there was a strong positive linear relationship between Topt and MDTa (Adjusted R2: 0.81; Slope: 1.08) with Topt exceeding MDTa by >1℃ at all but two sites. We conclude that ecosystem GPP has adjusted to local MDTa within Australian broadleaf evergreen forests and that GPP is buffered against small Ta increases in the majority of these ecosystems.

Above‐ground biomass in forests is critical to the global carbon cycle as it stores and sequesters carbon from the atmosphere. Climate change will disrupt the carbon cycle hence understanding how climate and other abiotic variables determine forest biomass at broad spatial scales is important for validating and constraining Earth System models and predicting the impacts of climate change on forest carbon stores. We examined the importance of climate and soil variables to explaining above‐ground biomass distribution across the Australian continent using publicly available biomass data from 3130 mature forest sites, in 6 broad ecoregions, encompassing tropical, subtropical and temperate biomes. We used the Random Forest algorithm to test the explanatory power of 14 abiotic variables (8 climate, 6 soil) and to identify the best‐performing models based on climate‐only, soil‐only and climate plus soil. The best performing models explained ~50% of the variation (climate‐only: R2 = 0.47 ± 0.04, and climate plus soils: R2 = 0.49 ± 0.04). Mean temperature of the driest quarter was the most important climate variable, and bulk density was the most important soil variable. Climate variables were consistently more important than soil variables in combined models, and model predictive performance was not substantively improved by the inclusion of soil variables. This result was also achieved when the analysis was repeated at the ecoregion scale. Predicted forest above‐ground biomass ranged from 18 to 1066 Mg ha−1, often under‐predicting measured above‐ground biomass, which ranged from 7 to 1500 Mg ha−1. This suggested that other non‐climate, non‐edaphic variables impose a substantial influence on forest above‐ground biomass, particularly in the high biomass range. We conclude that climate is a strong predictor of above‐ground biomass at broad spatial scales and across large environmental gradients, yet to predict forest above‐ground biomass distribution under future climates, other non‐climatic factors must also be identified.

In 2020, the Australian and New Zealand flux research and monitoring network, OzFlux, celebrated its 20th anniversary by reflecting on the lessons learned through two decades of ecosystem studies on global change biology. OzFlux is a network not only for ecosystem researchers, but also for those ‘next users’ of the knowledge, information and data that such networks provide. Here, we focus on eight lessons across topics of climate change and variability, disturbance and resilience, drought and heat stress and synergies with remote sensing and modelling. In distilling the key lessons learned, we also identify where further research is needed to fill knowledge gaps and improve the utility and relevance of the outputs from OzFlux. Extreme climate variability across Australia and New Zealand (droughts and flooding rains) provides a natural laboratory for a global understanding of ecosystems in this time of accelerating climate change. As evidence of worsening global fire risk emerges, the natural ability of these ecosystems to recover from disturbances, such as fire and cyclones, provides lessons on adaptation and resilience to disturbance. Drought and heatwaves are common occurrences across large parts of the region and can tip an ecosystem's carbon budget from a net CO2 sink to a net CO2 source. Despite such responses to stress, ecosystems at OzFlux sites show their resilience to climate variability by rapidly pivoting back to a strong carbon sink upon the return of favourable conditions. Located in under‐represented areas, OzFlux data have the potential for reducing uncertainties in global remote sensing products, and these data provide several opportunities to develop new theories and improve our ecosystem models. The accumulated impacts of these lessons over the last 20 years highlights the value of long‐term flux observations for natural and managed systems. A future vision for OzFlux includes ongoing and newly developed synergies with ecophysiologists, ecologists, geologists, remote sensors and modellers.

<p>How to formulate and run an element cycling model within a day?<br>How to compare many models with respect to many different diagnostics reliably? <br>How to allow models to be formulated in different ways?<br>How to make runnable models transparent without implementation details obscuring the scientific content?</p><p>bgc_md2 is an open source python library, available on github and binder. <br>It provides an extendable set of datatypes that capture the essential properties compartmental models have in common and enables the formulation of a model with a few lines of regular python code. The structure of the model is captured in symbolic math (using sympy) and can be checked during the creation of the model e.g. by drawing a carbon flow diagram or printing the flux equations using the same mathematical symbols used in the publication describing the model. <br>This can be done long before a complete parameter set for the model is added and the model can be run e.g. for a benchmark.<br>The computation of diagnostic variables both symbolic or numeric is based on the common building blocks which avoids the effort, obscurity and possible inconsistency resulting from a model specific implementation. The difference in available data for different models is addressed by computational graphs.<br>Instead of an inflexible schema for a relational database records can have different entries reflecting the available data.<br>Using the computability graphs the comparable is extended to the computable data. This allows for instance comparing a model described by a collection of fluxes with one described by matrices.<br>bgc_md2 is an extendable library that provides complex and well tested tools for model comparison but does not force the user into a rigid framework.<br>Rather than full automation it aims at flexibility of use within the python universe and can be used interactively in a jupyter notebook as well as in parallel computations on a supercomputer for global data assimilation as we do in a current model inter comparison.<br>Example jupyter notebooks can be explored interactively on binder without installation. </p>