Biology subjects

Sparrow, B.

Publications and source records attributed to Sparrow, B..

3 recordsLinked to original sources

bioRxiv2024

Detecting ecosystem trends in response to climate and disturbance across continental plot networks: a power analysis

Plant communities are dynamic: cyclical, directional and stochastic changes in species composition make interpretation of responses complex. The multi-dimensionality of ecological change is reduced by ecosystem indicators that quantify functional responses to environmental drivers. However, thresholds of detectable change in indicators may vary by ecosystem or the spatial configuration of monitoring sites within regions. We quantified the power of a continental terrestrial ecosystem plot network (TERN Ausplots, Australia) to detect change in ecological indicators linked to disturbance and climate: community temperature index (CTI), proportional abundance of photosynthetic pathways (C4%), and species abundance distribution (SAD). We simulated trend and noise scenarios and calculated power and effect size across spatial clusters of plots using linear mixed effects models. We then assessed factors that influenced change detection capacity. Power varied substantially across the network. Trends with a minimum magnitude of 0.5-3.0{degrees}C in CTI, 0.01-0.3 in C4% and 0.1-0.5 in the SAD sigma were detectable, depending on cluster identity and noise. Increased plot replication within a spatial cluster increased power and lower variance among baseline replicates increased effect size for CTI and C4% but not SAD. Latitudinal patterns in change detection capacity emerged for CTI and C4%. The effect size of increasing CTI was greater in the tropics due to lower among-plot variance, while power to detect increasing C4% was higher in the temperate south, where C4 is rare. Indicators reveal trends in complex ecological data that can inform decision-making. Increasing spatial replication and minimising spatial variation by stratifying plots to the same ecological community enhance change detection, albeit with different effectiveness by indicator. The results suggest change detection differs by location, irrespective of sampling factors such as number of plots. Strategies and thresholds for change detection may therefore apply unequally across environments and should be considered by region and response parameter.

ecology↗

bioRxiv2020

Using generalised dissimilarity modelling and targeted field surveys to gap-fill an ecosystem surveillance network

O_LIWhen considering which sites or land parcels complement existing conservation or monitoring networks, there are many strategies for optimising ecological coverage in the absence of ground observations. However, such optimisation is often implemented theoretically in conservation prioritisation frameworks and real-world implementation is rarely assessed, particularly for networks of monitoring sites. C_LIO_LIWe assessed the performance of adding new survey sites informed by predictive modelling in gap-filling the ecological coverage of the Terrestrial Ecosystem Research Networks (TERN) continental network of ecosystem surveillance plots, Ausplots. Using plant cover observations from 531 sites, we constructed a generalised dissimilarity model (GDM) in which species composition was predicted by environmental parameters. We combined predicted nearest-neighbour ecological distances for locations across Australia with practical considerations to select regions for gap-filling surveys of 181 new plots across 18 trips. We tracked the drop in mean nearest-neighbour distances in GDM space, and increases in the actual sampling of ecological space through cumulative multivariate dispersion. C_LIO_LIGDM explained 34% of deviance in species compositional turnover and retained geographic distance, soil P, aridity, actual evapotranspiration and rainfall seasonality among 17 significant predictors. C_LIO_LIKey bioregions highlighted as gaps included Cape York Peninsula, Brigalow Belt South, South Eastern Queensland, Gascoyne and Dampierland. C_LIO_LIWe targeted identified gap regions for surveys in addition to opportunistic or project-based gap-filling over two years. Approximately 20% of the land area of Australia received increased servicing of biological representation, corresponding to a drop in mean nearest-neighbour ecological distances from 0.38 to 0.33 in units of compositional dissimilarity. The gain in sampled ecological space was 172% that from the previous 181 plots. Notable gaps were filled in northern and south-east Queensland, north-east New South Wales and northern Western Australia. C_LIO_LIBiological scaling of environmental variables through GDM supports practical sampling decisions for ecosystem monitoring networks. Optimising putative survey locations via ecological distance to a nearest neighbour rather than to all existing sites is useful when the aim is to increase representation of habitats rather than sampling evenness per se. Iterations between modelled gaps and field campaigns provide a pragmatic compromise between theoretical optima and real-world decision-making. C_LI

ecology↗

bioRxiv2020

Stocktaking the environmental coverage of a continental ecosystem observation network

Field-based sampling of terrestrial habitats at continental scales is required to build ecosystem observation networks. However, a key challenge for detecting change in ecosystem composition, structure and function is to obtain a representative sample of habitats. Representative sampling contributes to ecological validity when analysing large spatial surveys, but field resources are limited and representativeness may differ markedly from purely practical sampling strategies to statistically rigorous ones. Here, we report a post hoc assessment of the coverage of environmental gradients as a surrogate for ecological coverage by a continental-scale survey of the Australian Terrestrial Ecosystem Research Network (TERN). TERNs surveillance program maintains a network of ecosystem observation plots that were init ially established in the rangelands through a stratification method (clustering of bioregions by environment) and Ausplots methodology. Subsequent site selection comprised gap filling combined with opportunistic sampling. Firstly, we confirmed that environmental coverage has been a good surrogate for ecological coverage. The cumulative sampling of environments and plant species composition over time were strongly correlated (based on mean multivariate dispersion; r = 0.93). We then compared the environmental sampling of Ausplots to 100,000 background points and a set of retrospective (virtual) sampling schemes: systematic grid, simple random, stratified random, and generalised random-tessellation stratified (GRTS). Differences were assessed according to sampling densities along environmental gradients, and multivariate dispersion (environmental space represented via multi-dimensional scaling). Ausplots outperformed systematic grid, simple random and GRTS in coverage of environmental space (Tukey HSD of mean dispersion, p < .001). GRTS site selection obtained similar coverage to Ausplots when employing the same bioregional stratification. Stratification by climatic zones generated the highest environmental coverage (p < .001), but the resulting sampling densities over-represented mesic coastal habitats. The Ausplots stratification by bioregions implemented under practical constraints represented complex environments well compared to statistically oriented or spatially even samples. However, potential statistical inference and power also depend on spatial and temporal replication, unbiased site selection, and accurate field measurements relative to the magnitude of change. A key conclusion is that environmental, rather than spatial, stratification is required to maximise ecological coverage across continental ecosystem observation networks.

ecology↗