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Clustering is indispensable in the quest for robust vegetation classification schemes that aim to partition, summarise and communicate patterns. However, clustering solutions are sensitive to methods and data and are therefore unstable, a feature that is usually attributed to noise. Viewed through a central‐tendency lens, noise is defined as the degree of departure from type, which is problematic since vegetation types are abstractions of continua, and so noise can only be quantified relative to the particular solution at hand. Graph theory models the structure of vegetation data based on the interconnectivity of samples. Through a graph‐theoretic lens, the causes of instability can be quantified in absolute terms via the degree of connectivity among objects. We simulated incremental increases in sampling intensity in a dataset over five iterations and assessed classification stability across successive solutions derived using algorithms implementing, respectively, models of central‐tendency and interconnectivity. We used logistic regression to model the likelihood of a sample changing groups between iterations as a function of distance to the centroid and degree of interconnectivity. Our results show that the degree to which samples are interconnected is a more powerful predictor of instability than the degree to which they deviate from their nearest centroid. The removal of weakly interconnected samples resulted in more stable classifications, although solutions with many clusters were apparently inherently less stable than those with few clusters, and improvements in stability flowing from the removal of outliers declined as the number of clusters increased. Our results reinforce the fact that clusters abstracted from continuous data are inherently unstable and that the quest for stable, fine‐scale classifications from large regional datasets is illusory. Nevertheless, our results show that using models better suited to the analysis of continuous data may yield more stable classifications of the available data. [ABSTRACT FROM AUTHOR]

Questions: Most clustering methods assume data are structured as discrete hyperspheroidal clusters to be evaluated by measures of central tendency. If vegetation data do not conform to this model, then vegetation data may be clustered incorrectly. What are the implications for cluster stability and evaluation if clusters are of irregular shape or density? Location: Southeast Australia. Methods: We define misplacement as the placement of a sample in a cluster other than (distinct from) its nearest neighbor and hypothesize that optimizing homogeneity incurs the cost of higher rates of misplacement. Chameleon is a graph‐theoretic algorithm that emphasizes interconnectivity and thus is sensitive to the shape and distribution of clusters. We contrasted its solutions with those of traditional nonhierarchical and hierarchical (agglomerative and divisive) approaches. Results: Chameleon‐derived solutions had lower rates of misplacement and only marginally higher heterogeneity than those of k‐means in the range of 15–60 clusters, but their metrics converged with larger numbers of clusters. Solutions derived by agglomerative clustering had the best metrics (and divisive clustering the worst) but both produced inferior high‐level solutions to those of Chameleon by merging distantly‐related clusters. Conclusions: Graph‐theoretic algorithms, such as Chameleon, have an advantage over traditional algorithms when data exhibit discontinuities and variable structure, typically producing more stable solutions (due to lower rates of misplacement) but scoring lower on traditional metrics of central tendency. Advantages are less obvious in the partitioning of data from continuous gradients; however, graph‐based partitioning protocols facilitate the hierarchical integration of solutions. [ABSTRACT FROM AUTHOR]

Ecological community and ecosystem "red lists" have been developed in several jurisdictions to improve ecosystem‐level biodiversity protection. However, a challenge for the conservation and management of listed ecosystems is consistent identification in the field or from plot records. Ecosystem descriptions must have enough detail for positive identification but be broad enough that most instances are included. In many jurisdictions, descriptions are not supported by dichotomous keys or thresholds of ecosystem collapse and identification relies on the interpretation of trained individuals, with potential for opposing opinions. Using a structured process, we assessed the ability of experts to identify a critically endangered ecological community from vegetation plot samples. We compared the allocations made by experts with a numeric classification that underpinned the legal definition of the community. Overall, experts correctly identified the presence or absence of the community in 81% of samples although individual classification rates ranged from 63% to 94%. False positive rates varied among experts (7–50%) and experienced botanists did not necessarily perform better. Disturbance increased uncertainty and experts differed in their opinion about when the community had collapsed and was no longer recoverable. Inconsistent interpretation, in the absence of diagnostic keys and consensus models of collapse, will have implications for recovery and conservation of listed communities and ecosystems, and could impact the effectiveness of laws and policies designed to protect them. [ABSTRACT FROM AUTHOR]

Global change is threatening ecosystems and biodiversity world-wide, creating a pressing need to understand how climate and disturbance regimes interact and influence the persistence of species. We quantify how three ecosystem drivers - rainfall, fire and herbivory - influence vital rates in the perennial resprouting graminoid, Triodia scariosa, a foundation species of semi-arid Australia., We used an 11-year dataset from a fire and herbivore exclosure experiment, to model flowering, post-fire recruitment and the post-fire survival of seedlings and resprouting plants. Regression modelling quantified the effect of rainfall, inter-fire interval, fire type (wildfire or prescribed fire), grazing by herbivores (native and feral) and an interaction between fire type and herbivory on T. scariosa populations., Rainfall, fire and herbivory had significant effects on post-fire recruitment and the survival of seedlings and resprouting plants, including strong interactions between these drivers. Herbivory following wildfire had a minor effect, but in years of below-average rainfall herbivory following prescribed fire had a large effect, reducing the survival of seedlings and resprouting plants by 20% and over 50% respectively, relative to post-fire survival under average rainfall conditions., Variation in rainfall underpinned significant variation in post-fire resprouting and seedling survival, thus we postulate rainfall primarily drives the dynamics of T. scariosa populations., Synthesis. This study highlights the importance of modelling interactions between key ecosystem drivers when predicting how changes in global climate and disturbance regimes influence plant vital rates. Relatively small changes to disturbance regimes can substantially alter population processes, even in perennial resprouting species. This work suggests that conservation of foundation species, such as T. scariosa, will benefit if fire management decisions are better integrated with inter-annual weather forecasts and herbivore management. [ABSTRACT FROM AUTHOR]

Theory suggests spatial heterogeneity can facilitate species co-occurrence at fine scales, but environmental data are rarely collected at sufficiently high resolution to test this empirically. Whilst there is emerging evidence that subtle variation in soil hydrology represents a fundamental fine-scale niche axis within plant communities, this is largely derived from studies of soil hydrology in isolation from other environmental factors., We assessed the comparative importance of fine-scale hydrological niche differentiation for species co-occurrence using a high-resolution study of soil hydrology and other edaphic variables, coupled with a long-term (24 years) data set of herbaceous plant plots in a heathland community in south-east Australia., For the analysis, we employed novel latent variable models (LVMs), which offer an explicit, model-based approach to partitioning out the different drivers of species co-occurrence patterns. Whilst the regression component of an LVM models the species-specific environmental responses, the latent variable component can be used to identify residual patterns of co-occurrence, which may be attributable to unmeasured factors and/or biotic interactions., Relative to a host of plant resources, non-resource factors and 'unmeasured' latent variables, soil hydrology emerged as the best predictor of negative co-occurrences within the community, with the dominant species exhibiting strongly differentiated responses across a comparatively narrow moisture gradient. Nevertheless, strong species-specific responses to environmental variability only emerged at scales greater than those at which plants may be expected to compete for resources, throwing doubt on the direct role of spatial heterogeneity as a mechanism for local-scale coexistence., Synthesis. This study confirms the vital role of hydrological niches for the maintenance of within-community plant diversity, but also highlights the need for more rigorous analysis of scale dependencies to better understand the underlying coexistence mechanisms at play. In addition, it illustrates the inferential gains made possible with model-based approaches to the analysis of species co-occurrence. r code illustrating model fitting and inference is provided as a supplement. [ABSTRACT FROM AUTHOR]

The International Union for the Conservation of Nature ( IUCN) has proposed a standard global assessment protocol for the evaluation of conservation risks to ecosystems. The assessment criteria mirror the IUCN protocol for the assessment of species, however there are relatively few case studies available to demonstrate their utility across a broad range of ecosystems. We applied the IUCN protocol to assess the conservation status of the Cumberland Plain Woodland ( CPW) located in the western suburbs of Sydney, Australia. The ecosystem has been the focus of extensive survey and research and has been determined to be critically endangered under NSW and Australian Government legislation, primarily as a consequence of its history of extensive clearing. The outcome of the risk assessment for CPW using the IUCN protocol was identical to those under the NSW and Australian legislation. Data for risk assessment fell into two categories, each with different limitations. Phytosocialogical data and distribution models provided a strong basis for quantifying past and future changes in the distribution of CPW, but offered only indirect measures of functional symptoms of decline. Conversely, local case studies documenting declines in ecosystem function due to weed invasion, soil disturbance and alteration of fire regimes could not easily be extrapolated in order to evaluate the assessment criteria. The critically endangered rating was based solely on the historic reduction in geographic distribution (92-94%), however clearing poses an ongoing threat to the ecosystem. The contemporary clearing rate of CPW is approximately half the historical average but there is evidence that the rate will double in the next decade as a consequence of ongoing urbanisation and Government policy of biodiversity offsets. A systematic approach to documenting the extent of environmental degradation and disruption to biotic processes would assist the assessment of CPW and other ecosystems against the IUCN criteria. [ABSTRACT FROM AUTHOR]

We evaluated the restoration of native plant assemblages by topsoil translocation in the Hunter Valley, south-east Australia. Species' responses were characterized by defining nine plant functional types (PFTs) based on combinations of four response mechanisms (seed bank persistence, germination cues, resprouting mechanisms, and longevity) through which species were predicted to persist or decline following translocation. The effects of community type and delay in topsoil restoration on restoration outcomes were tested in an orthogonal experiment. Changes in species' frequency were detected using Bayesian statistics with prior probabilities derived from pre-clearing data. Few species failed to reestablish following translocation; these were offset by recruitment of other native species not detected prior to clearing. Compositional changes were more pronounced when topsoil was stockpiled (cf direct reinstatement), although there was no trend related to the period of stockpiling. The PFT response model correctly predicted the rank probability of decline in three of the nine PFTs, while a further three were correctly placed in the top ranks but in the incorrect order. Three PFTs were incorrectly ranked because the response model was incorrect. Resprouters declined more frequently than seeders; however, species with physical seed dormancy declined less frequently than those with either transient seed banks or physiological, morphological, or morpho-physiological dormancy, irrespective of resprouting ability. Species with short juvenile periods were more likely to increase. We conclude that PFTs based on fire-response traits represent a practical means of predicting species' responses to translocation and a basis for prioritizing species for supplementary planting. [ABSTRACT FROM AUTHOR]

Models that couple habitat suitability with demographic processes offer a potentially improved approach for estimating spatial distributional shifts and extinction risk under climate change. Applying such an approach to five species of Australian plants with contrasting demographic traits, we show that: (i) predicted climate-driven changes in range area are sensitive to the underlying habitat model, regardless of whether demographic traits and their interaction with habitat patch configuration are modeled explicitly; and (ii) caution should be exercised when using predicted changes in total habitat suitability or geographic extent to infer extinction risk, because the relationship between these metrics is often weak. Measures of extinction risk, which quantify threats to population persistence, are particularly sensitive to life-history traits, such as recruitment response to fire, which explained approximately 60% of the deviance in expected minimum abundance. Dispersal dynamics and habitat patch structure have the strongest influence on the amount of movement of the trailing and leading edge of the range margin, explaining roughly 40% of modeled structural deviance. These results underscore the need to consider direct measures of extinction risk (population declines and other measures of stochastic viability), as well as measures of change in habitat area, when assessing climate change impacts on biodiversity. Furthermore, direct estimation of extinction risk incorporates important demographic and ecosystem processes, which potentially influence species' vulnerability to extinction due to climate change. [ABSTRACT FROM AUTHOR]