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Monitoring vegetation restoration is challenging because monitoring is costly, requires long-term funding, and involves monitoring multiple vegetation variables that are often not linked back to learning about progress toward objectives. There is a clear need for the development of targeted monitoring programs that focus on a reduced set of variables that are tied to specific restoration objectives. In this paper, we present a method to progress the development of a targeted monitoring program, using a pre-existing state-and-transition model. We (1) use field data to validate an expert-derived classification of woodland vegetation states; (2) use these data to identify which variable(s) help differentiate woodland states; and (3) identify the target threshold (for the variable) that signifies if the desired transition has been achieved. The measured vegetation variables from each site in this study were good predictors of the different states. We show that by measuring only a few of these variables, it is possible to assign the vegetation state for a collection of sites, and monitor if and when a transition to another state has occurred. For this ecosystem and state-and-transition models, out of nine vegetation variables considered, the density of immature trees and percentage of exotic understory vegetation cover were the variables most frequently specified as effective to define a threshold or transition. We synthesize findings by presenting a decision tree that provides practical guidance for the development of targeted monitoring strategies for woodland vegetation.

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The contribution of urban greenspaces to support biodiversity and provide benefits for people is increasingly recognized. However, ongoing management practices favor vegetation oversimplification, often limiting greenspaces to lawns and tree canopy rather than multi-layered vegetation that includes under- and midstorey, and the use of nonnative species. These practices hinder the potential of greenspaces to sustain indigenous biodiversity, particularly for taxa like insects that rely on plants for food and habitat. Yet, little is known about which plant species may maximize positive outcomes for taxonomically and functionally diverse insect communities in greenspaces. Additionally, while cities are expected to experience high rates of introductions, quantitative assessments of the relative occupancy of indigenous vs. introduced insect species in greenspace are rare, hindering understanding of how management may promote indigenous biodiversity while limiting the establishment of introduced insects. Using a hierarchically replicated study design across 15 public parks, we recorded occurrence data from 552 insect species on 133 plant species, differing in planting design element (lawn, midstorey, and tree canopy), midstorey growth form (forbs, lilioids, graminoids, and shrubs) and origin (nonnative, native, and indigenous), to assess (1) the relative contributions of indigenous and introduced insect species and (2) which plant species sustained the highest number of indigenous insects. We found that the insect community was overwhelmingly composed of indigenous rather than introduced species. Our findings further highlight the core role of multi-layered vegetation in sustaining high insect biodiversity in urban areas, with indigenous midstorey and canopy representing key elements to maintain rich and functionally diverse indigenous insect communities. Intriguingly, graminoids supported the highest indigenous insect richness across all studied growth forms by plant origin groups. Our work highlights the opportunity presented by indigenous understory and midstorey plants, particularly indigenous graminoids, in our study area to promote indigenous insect biodiversity in urban greenspaces. Our study provides a blueprint and stimulus for architects, engineers, developers, designers, and planners to incorporate into their practice plant species palettes that foster a larger presence of indigenous over regionally native or nonnative plant species, while incorporating a broader mixture of midstorey growth forms.

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Modeling plant growth using functional traits is important for understanding the mechanisms that underpin growth and for predicting new situations. We use three data sets on plant height over time and two validation methods-in-sample model fit and leave-one-species-out cross-validation-to evaluate non-linear growth model predictive performance based on functional traits. In-sample measures of model fit differed substantially from out-of-sample model predictive performance; the best fitting models were rarely the best predictive models. Careful selection of predictor variables reduced the bias in parameter estimates, and there was no single best model across our three data sets. Testing and comparing multiple model forms is important. We developed an R package with a formula interface for straightforward fitting and validation of hierarchical, non-linear growth models. Our intent is to encourage thorough testing of multiple growth model forms and an increased emphasis on assessing model fit relative to a model's purpose.

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Field data collection can be expensive, time consuming, and difficult; insightful research requires statistical analyses supported by sufficient data. Pilot studies and power analysis provide guidance on sampling design but can be challenging to perform, as ecologists increasingly collect multiple types of data over different scales. Despite a growing simulation literature, it remains unclear how to appropriately design data collection for many complex projects. Approaches that seek to achieve realism in decision-making contexts, such as management strategy evaluation and virtual ecologist simulations, can help. For a relatively complex analysis, we develop and demonstrate a flexible simulation approach that informs what data are needed and how long those data will take to collect, under realistic fieldwork constraints. We simulated data collection and analysis under different constraint scenarios that varied in deterministic (field trip length, travel, and measurement times) and stochastic (species detection and occupancy rates and inclement weather) features. In our case study, we fit plant height data to a multispecies, three-parameter, nonlinear growth model. We tested how the simulated data sets, based on the varying constraint scenarios, affected the model fit (parameter bias, uncertainty, and capture rate). Species prevalence in the field exerted a stronger influence on the data sets and downstream model performance than deterministic aspects such as travel times. When species detection and occupancy were not considered, the field time needed to collect an adequate data set was underestimated by 40%. Simulations can assist in refining fieldwork design, estimating field costs, and incorporating uncertainties into project planning. We argue that combining data collection, analysis, and decision-making processes in a flexible virtual setting can help address many of the decisions that field ecologists face when designing field-based research.

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Plant functional traits are increasingly used to generalize across species, however few examples exist of predictions from trait-based models being evaluated in new species or new places. Can we use functional traits to predict growth of unknown species in different areas? We used three independently collected datasets, each containing data on heights of individuals from non-resprouting species over a chronosquence of time-since-fire sites from three ecosystems in south-eastern Australia. We examined the influence of specific leaf area, woody density, seed size and leaf nitrogen content on three aspects of plant growth; maximum relative growth rate, age at maximum growth and asymptotic height. We tested our capacity to perform out-of-sample prediction of growth trajectories between ecosystems using species functional traits. We found strong trait-growth relationships in one of the datasets; whereby species with low SLA achieved the greatest asymptotic heights, species with high leaf-nitrogen content achieved relatively fast growth rates, and species with low seed mass reached their time of maximum growth early. However these same growth-trait relationships did not hold across the two other datasets, making accurate prediction from one dataset to another unachievable. We believe there is evidence to suggest that growth trajectories themselves may be fundamentally different between ecosystems and that trait-height-growth relationships may change over environmental gradients.

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Optical couplers are very important devices in optical communication systems and optical sensor systems. Several types of optical couplers with different materials or different transmission characteristics have been reported. Here we propose a semi-reciprocal polarization maintaining fibre coupler with unique transmission characteristics, which is distinct from conventional polarization maintaining fibre couplers and polarization beam splitters, and investigate the characteristics of the coupler theoretically and experimentally. The experimental results show that for circularly and elliptically polarized input light, the proposed coupler will act both as an in-line polariser and a conventional polarization maintaining fibre coupler. The output polarization extinction ratio of the transmission arm is 31.79 dB at a centre wavelength of 841 nm. For linearly polarized input light, the coupler will merely act as a conventional 3 dB polarization maintaining fibre coupler. The unique features of the proposed coupler enables the removal of polarisers from optical sensor systems and coherent optical communication systems, and reduces the insertion loss and production cost of the optical path. Therefore there is wide application for this device in optical sensor systems and optical communication systems.