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•A systematic review assessed ecosystem services from trees in grazed dairy enterprises.•Thirty key causal relationships (woody system ≫ mechanism ≫ outcome) were identified.•Linkages ...between on-farm woody systems and ecosystem services were largely positive.•Results provide strong support for the internal benefits of on-farm tree systems.
Intensification of the dairy industry globally, combined with a changing climate, has placed increased pressure on natural capital assets (and the flow of ecosystem services) on farms. Agroforestry is widely promoted as an intervention to address these issues. While some benefits of integrating trees on farms, such as carbon sequestration and biodiversity, are reasonably well known, less is known about other potential benefits, such as on-farm production. Understanding and quantifying these benefits would inform farm planning and decision-making. We used a systematic review approach to analyse the evidence base for biophysical ecosystem services from woody systems (including shelterbelts, riparian plantings, plantations, pasture trees, silvopasture and remnant native vegetation) provided to grazed dairy enterprises. We identified 83 publications containing 123 records that fit our review criteria of reporting on biophysical ecosystem services from woody systems on dairy farms relative to a grazed pasture comparison. For each relationship between a woody system and ecosystem service, we assessed the level of support, strength and predominant direction of evidence, and summarised the causal relationships (woody system ≫ mechanism ≫ outcome). Shelterbelts and riparian plantings were the most commonly reported woody systems. Linkages between woody systems and ecosystem services were largely positive, with the types of services provided and their importance differing among systems. Mean evaluation scores for the strength of the evidence were moderate to strong. However, the number of records for each relationship was often low. Consequently, only eight of the 30 causal pathways identified had high confidence; a further 14 had medium confidence indicating that these have good potential to deliver benefits but warrant further work. Although the evidence here was largely qualitative, our results provide strong support for the internal benefits that natural capital assets, such as on-farm woody systems, can provide to the productivity and resilience of grazed dairy enterprises.
Aim
Understanding biodiversity–ecosystem function (BEF) relationships in forest systems is crucial for effective forest management and restoration, yet testing these relationships is often limited by ...biased diversity patterns in forestry plantings (biased towards commercially valuable species) and uncontrollable diversity in mature natural forests. Multispecies reforestation plantings present a valuable opportunity to investigate BEF relationships in woody systems, especially across large environmental gradients.
Location
Reforestation plantings across the arable region of Australia.
Time period
1951–2012.
Major taxa studied
Three hundred and sixty‐four woody plant species.
Methods
We examined relationships between productivity and diversity using inventory data from 977 plots in 386 multispecies reforestation plantings. Diversity was estimated using observed species richness and three functional diversity indices calculated from four functional traits: specific leaf area, wood density, seed mass and maximum attainable height. We modelled how plot‐level biomass accumulation (a productivity proxy) correlated with these diversity indices, as well as age since planting, plant density and three environmental variables: solar radiation, moisture availability and soil sand content. These models were fitted across Australia and, separately, within eight groups of plantings with similar environmental conditions.
Results
We found no correlation between diversity and productivity, regardless of the diversity metric or spatial scale used (continent‐wide or within environment groups). Instead, productivity was best explained by local environmental conditions and plant density.
Main conclusions
A positive relationship between diversity and productivity was not evident in planted forests across a wide range of Australian woodland and forest systems, at least in the first few decades of growth. Our findings suggest that the positive relationship between diversity and productivity commonly reported in experimental settings should not be assumed for all systems and conditions.
Functional traits are proxies for a species' ecology and physiology and are often correlated with plant vital rates. As such they have the potential to guide species selection for restoration ...projects. However, predictive trait‐based models often only explain a small proportion of plant performance, suggesting that commonly measured traits do not capture all important ecological differences between species. Some residual variation in vital rates may be evolutionarily conserved and captured using taxonomic groupings alongside common functional traits. We tested this hypothesis using growth rate data for 17,299 trees and shrubs from 80 species of Eucalyptus and 43 species of Acacia, two hyper‐diverse and co‐occurring genera, collected from 497 neighborhood plots in 137 Australian mixed‐species revegetation plantings. We modeled relative growth rates of individual plants as a function of environmental conditions, species‐mean functional traits, and neighbor density and diversity, across a moisture availability gradient. We then assessed whether the strength and direction of these relationships differed between the two genera. We found that the inclusion of genus‐specific relationships offered a significant but modest improvement to model fit (1.6%–1.7% greater R2 than simpler models). More importantly, almost all correlates of growth rate differed between Eucalyptus and Acacia in strength, direction, or how they changed along the moisture gradient. These differences mapped onto physiological differences between the genera that were not captured solely by measured functional traits. Our findings suggest taxonomic groupings can capture or mediate variation in plant performance missed by common functional traits. The inclusion of taxonomy can provide a more nuanced understanding of how functional traits interact with abiotic and biotic conditions to drive plant performance, which may be important for constructing trait‐based frameworks to improve restoration outcomes.
The clearing of natural vegetation for agriculture has reduced the capacity of natural systems to provide ecosystem functions. Ecological restoration can restore desirable ecosystem functions, such ...as creating habitat for animal conservation and carbon sequestration as woody biomass. In order to maintain these beneficial ecosystem functions, restoration projects need to mature into self‐perpetuating communities. Here we compared the ecological attributes of two types of restoration, “active” tree plantings with “passive” natural forest regeneration (“natural regrowth”) to existing remnant vegetation in a cleared agricultural landscape. Specifically, we measured differences between forest categories in factors that may predict future restoration failure or ecosystem collapse: aboveground plant biomass and biomass accrual over time (for regrowing stands), plant density and size class distributions, and diversity of functional groups based on seed dispersal and growth strategy traits. We found that natural regrowth and planted forests were similar in many ecological characteristics, including biomass accrual. Despite this, planted stands contained fewer tree recruit and shrub individuals, which may be due to limited recruitment in plantings. If this continues, these forests may be at risk of collapsing into nonforest states after mature trees senesce. Lower shrub density and richness of mid‐story trees may lead to lower structural complexity in planting plots, and alongside lower richness of fleshy‐fruited plant species may reduce animal resources and animal use of the restored stand. In our study region, natural regrowth may result in restored woodland communities with greater conservation and carbon mitigation value.
•Specific and generalised allometrics were developed for mixed-species plantings.•To test predictions, biomass was directly measured at the site-level at 8 sites.•Bias was relatively low for ...generalised allometry-predictions of biomass.•Precision and accuracy increased with the level of specificity of allometry.
To quantify the impact that planting indigenous trees and shrubs in mixed communities (environmental plantings) have on net sequestration of carbon and other environmental or commercial benefits, precise and non-biased estimates of biomass are required. Because these plantings consist of several species, estimation of their biomass through allometric relationships is a challenging task. We explored methods to accurately estimate biomass through harvesting 3139 trees and shrubs from 22 plantings, and collating similar datasets from earlier studies, in non-arid (>300mm rainfallyear−1) regions of southern and eastern Australia. Site-and-species specific allometric equations were developed, as were three types of generalised, multi-site, allometric equations based on categories of species and growth-habits: (i) species-specific, (ii) genus and growth-habit, and (iii) universal growth-habit irrespective of genus. Biomass was measured at plot level at eight contrasting sites to test the accuracy of prediction of tonnes dry matter of above-ground biomass per hectare using different classes of allometric equations. A finer-scale analysis tested performance of these at an individual-tree level across a wider range of sites. Although the percentage error in prediction could be high at a given site (up to 45%), it was relatively low (<11%) when generalised allometry-predictions of biomass was used to make regional- or estate-level estimates across a range of sites. Precision, and thus accuracy, increased slightly with the level of specificity of allometry. Inclusion of site-specific factors in generic equations increased efficiency of prediction of above-ground biomass by as much as 8%. Site-and-species-specific equations are the most accurate for site-based predictions. Generic allometric equations developed here, particularly the generic species-specific equations, can be confidently applied to provide regional- or estate-level estimates of above-ground biomass and carbon.
Accurate ground‐based estimation of the carbon stored in terrestrial ecosystems is critical to quantifying the global carbon budget. Allometric models provide cost‐effective methods for biomass ...prediction. But do such models vary with ecoregion or plant functional type? We compiled 15 054 measurements of individual tree or shrub biomass from across Australia to examine the generality of allometric models for above‐ground biomass prediction. This provided a robust case study because Australia includes ecoregions ranging from arid shrublands to tropical rainforests, and has a rich history of biomass research, particularly in planted forests. Regardless of ecoregion, for five broad categories of plant functional type (shrubs; multistemmed trees; trees of the genus Eucalyptus and closely related genera; other trees of high wood density; and other trees of low wood density), relationships between biomass and stem diameter were generic. Simple power‐law models explained 84–95% of the variation in biomass, with little improvement in model performance when other plant variables (height, bole wood density), or site characteristics (climate, age, management) were included. Predictions of stand‐based biomass from allometric models of varying levels of generalization (species‐specific, plant functional type) were validated using whole‐plot harvest data from 17 contrasting stands (range: 9–356 Mg ha−1). Losses in efficiency of prediction were <1% if generalized models were used in place of species‐specific models. Furthermore, application of generalized multispecies models did not introduce significant bias in biomass prediction in 92% of the 53 species tested. Further, overall efficiency of stand‐level biomass prediction was 99%, with a mean absolute prediction error of only 13%. Hence, for cost‐effective prediction of biomass across a wide range of stands, we recommend use of generic allometric models based on plant functional types. Development of new species‐specific models is only warranted when gains in accuracy of stand‐based predictions are relatively high (e.g. high‐value monocultures).
To combat global warming and biodiversity loss, we require effective forest restoration that encourages recovery of species diversity and ecosystem function to deliver essential ecosystem services, ...such as biomass accumulation. Further, understanding how and where to undertake restoration to achieve carbon sequestration and biodiversity conservation would provide an opportunity to finance ecosystem restoration under carbon markets. We surveyed 30 native mixed‐species plantings in subtropical forests and woodlands in Australia and used structural equation modeling to determine vegetation, soil, and climate variables most likely driving aboveground biomass accrual and bird richness and investigate the relationships between plant diversity, aboveground biomass accrual, and bird diversity. We focussed on woodland and forest‐dependent birds, and functional groups at risk of decline (insectivorous, understorey‐nesting, and small‐bodied birds). We found that mean moisture availability strongly limits aboveground biomass accrual and bird richness in restoration plantings, indicating potential synergies in choosing sites for carbon and biodiversity purposes. Counter to theory, woody plant richness was a poor direct predictor of aboveground biomass accrual, but was indirectly related via significant, positive effects of stand density. We also found no direct relationship between aboveground biomass accrual and bird richness, likely because of the strong effects of moisture availability on both variables. Instead, moisture availability and patch size strongly and positively influenced the richness of woodland and forest‐dependent birds. For understorey‐nesting birds, however, shrub cover and patch size predicted richness. Stand age or area of native vegetation surrounding the patch did not influence bird richness. Our results suggest that in subtropical biomes, planting larger patches to higher densities, ideally using a diversity of trees and shrubs (characteristics of ecological plantings) in more mesic locations will enhance the provision of carbon and biodiversity cobenefits. Further, ecological plantings will aid the rapid recovery of woodland and forest bird richness, with comparable aboveground biomass accrual to less diverse forestry plantations.
We assessed native mixed‐species plantings in subtropical forests and woodlands in Australia to determine vegetation, soil, and climate variables most likely driving aboveground biomass (AGB) accrual and bird richness and investigate the relationships between plant diversity, AGB accrual, and bird diversity. Our results suggest that in subtropical biomes, planting larger patches to higher densities, ideally using a diversity of trees and shrubs, in locations with higher water availability will enhance the provision of carbon and biodiversity cobenefits.
•We review nine alternatives for correcting bias in log–log allometrics.•We use simulations to evaluate the ability of these to estimate average biomass.•We evaluate their ability to predict biomass ...of new trees.•Methods not commonly used in forest science performed best.
Allometric relationships are commonly used to estimate average biomass of trees of a particular size and to predict biomass of individual trees based on an easily measured covariate variable such as stem diameter. They are typically power relationships which, for the purpose of data fitting, are transformed using natural logarithms to convert the model to its linear equivalent. Implementation of these equations to estimate the relationships and to predict biomass of new trees on the natural (i.e., actual) scale requires back-transforming the logarithmic predictions. Because these transformations involve non-linearity, care must be taken during this step to avoid bias. Several correction factors have been proposed in the literature for removing the gross bias in estimates, but their performance as predictors of biomass has not yet been examined. This is a very important problem, and here we review nine such correction factors in terms of their abilities to estimate biomass and predict biomass for new trees. We compare their performance by examining their bias and variability based on large datasets of above-ground biomass and stem diameter for eight species of harvested trees and shrubs in the genera Eucalyptus and Acacia (n=102–365 individuals per species). We found that good estimates of average biomass turned out to be good predictors of biomass for new trees. The linear model fitted has log of the above-ground biomass as the response variable and log of the stem diameter as the covariate. The only exactly unbiased estimate among those considered was the uniform minimum variance unbiased (UMVU) estimate, which involves evaluating a confluent hypergeometric function to obtain its correction factor. Three alternative correction factors that are easy to compute also performed well. One of these minimises mean squared error and was found to result in low bias, low prediction bias, the lowest mean squared error, and the lowest mean squared prediction error among all correction factors examined.
Background: Agroforestry systems can improve the provision of ecosystem services at the farm scale whilst improving agricultural productivity, thereby playing an important role in the sustainable ...intensification of agriculture. Natural capital accounting offers a framework for demonstrating the capacity of agroforestry systems to deliver sustained private benefits to farming enterprises, but traditionally is applied at larger scales than those at which farmers make decisions. Methods: Here we review the current state of knowledge on natural capital accounting and analyse how such an approach may be effectively applied to demonstrate the farm-scale value of agroforestry assets. We also discuss the merits of applying a natural capital approach to agroforestry decision-making and present an example of a conceptual model for valuation of agroforestry assets at the farm scale. Results: Our findings suggest that with further development of conceptual models to support existing tools and frameworks, a natural capital approach could be usefully applied to improve decision-making in agroforestry at the farm scale. Using this approach to demonstrate the private benefits of agroforestry systems could also encourage adoption of agroforestry, increasing public benefits such as biodiversity conservation and climate change mitigation. However, to apply this approach, improvements must be made in our ability to predict the types and amounts of services that agroforestry assets of varying condition provide at the farm or paddock scale.
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