Reassessment of the invasion history of two species of Cakile (Brassicaceae) in Australia

In this paper we revisit the invasion history of two species of Cakile in Australia. Cakile edentula subsp. edentula arrived in the mid 19th Century and spread into coastal strandline habitat from the southeast towards the west and to the north; Cakile maritima arrived in the late 19th Century and has replaced Cakile edentula over much of the range. While Cakile edentula is morphologically quite uniform, the great variation within Cakile maritima has confused field ecologists. Using herbarium records we update previous accounts of the spread of the species and report on field surveys that determined their current geographic overlap in Tasmania and in northern New South Wales/southern Queensland. We examine regional morphological variation within Cakile maritima using the national herbaria collections and variation within new population samples. We support previous interpretations that Cakile maritima has been introduced on more than one occasion from morphologically distinct races, resulting in regional variation within Australia and high variability within populations in the south-east. Western Australian populations appear distinct and probably did not initiate those in the east; we consider that eastern populations are likely to be a mix of Cakile maritima subsp. maritima from the Mediterranean and Cakile maritima subsp. integrifolia from Atlantic Europe. Although introgression from Cakile edentula into Cakile maritima cannot be discounted from our results, it is not required to explain the levels of variation in the latter species observed in Australia. Cakile maritima continues to spread southwards in Tasmania and northwards in NSW; in Queenland, a recent occurrence has proliferated in Moreton Bay, spreading slowly to the north but not appreciably southwards

High aqueous salinity does not preclude germination of invasive Iris pseudacorus from estuarine populations

Estuarine ecosystems are threatened by climate change and biological invasions. Among global changes, sea-level rise is broadly impacting tidal wetlands, through increases in salinity and alteration of inundation regimes. Extant freshwater plant species are often presumed to be limited to reaches of estuaries with low salinity and narrow tidal ranges. However, the potential for invasive freshwater species (e.g., Iris pseudacorus) to persist and spread with increased salinity and flooding is poorly understood and can jeopardize native biodiversity and other wetland ecosystem services. The successful establishment of invasive plants will be dependent on their tolerance to salinity and inundation, starting with the germination life stage. Changes to abiotic estuarine gradients may alter the germination process of tidal wetland plant species that underlies significant patterns of plant community composition and biodiversity. We explored germination responses of seeds from two invasive I. pseudacorus populations from freshwater and brackish tidal sites in California’s San Francisco Bay–Delta Estuary. We tested germination dynamics under salinity levels ranging from freshwater to seawater (0, 12.5, 25, and 45 dS/m) and two hydrological conditions (moist and flooded). Salinity levels >12.5 dS/m inhibited germination of seeds from both populations, consistent with viviparism and seedling emergence recorded at field sites. However, seeds exposed to seawater for 55 d germinated once exposed to freshwater. Germination velocity and seed buoyancy differed between populations, likely due to differences in seed coat thickness. Our results demonstrate that after 55 d in seawater, buoyant seeds of I. pseudacorus retain their ability to germinate, and germinate quickly with freshwater exposure. This suggests that invasive populations of I. pseudacorus can colonize new sites following potentially long-distance dispersal of buoyant seeds with tidal currents. These findings inform risk assessments and highlight the need to prioritize the management of invasive I. pseudacorus in estuarine ecosystems impacted by rising sea level

Sex dimorphism in dioecious Palmer amaranth (Amaranthus palmeri) in response to water stress.

Main conclusionPhenological isolation can potentially reduce seed output and may be exploited as a novel tool for ecological management of dioecious weeds. Dioecious plants may benefit from a maximized outcrossing and optimal sex-specific resource allocation; however, this breeding system may also be exploited for weed management. Seed production in dioecious species is contingent upon the co-occurrence and co-flowering of the two genders and can be further disturbed by flowering asynchrony. We explored dimorphism in secondary sex characters in Amaranthus palmeri, and tested if reproductive synchrony can be affected by water stress. We have used seeds of A. palmeri from California, Kansas and Texas, and studied secondary sex characters under natural conditions and in response to water stress. Seeds of A. palmeri from California (CA) and Kansas (KS) were cordially provided by Dr. Anil Shrestha (California State University, Fresno, California) and Dr. Dallas E. Peterson (Kansas State University, Manhattan, Kansas), respectively. Seeds of a third population were collected from mature plants (about 30 plants) from a set-aside field in College Station, Texas. A. palmeri showed no sexual dimorphism with regard to the timing of emergence, plant height, and relative growth rate. While the initiation of flowering occurred earlier in males than females, females preceded males in timing of anthesis. Water stress delayed anthesis in males to a greater extent than females increasing the anthesis mismatch between the two sexes by seven days. Our data provide the first evidence of environment-controlled flowering asynchrony in A. palmeri. From a practical point of view, phenological isolation can potentially reduce seed output and may be exploited as a novel tool for ecological management of dioecious weeds

Hydrothermal-time-to-event models for seed germination

Time-to-event methods have been proposed in the agricultural sciences, as one of the most suitable options for the analysis of seed germination data. In contrast to traditional linear/nonlinear regression, time-to-event methods can easily account for all statistical peculiarities inherited in germination assays, such as censoring, and they can produce unbiased estimates of model parameters and their standard errors. So far, these methods have only been used in combination with empirical models of germination, which are lacking biological underpinnings. We bridge the gap between statistical requirements and biological understanding by developing a general method that formulates biologically-oriented hydro time (HT), thermal time (TT) and hydrothermal time (HTT) models into a time-to-event framework. HT, TT, and HTT models are widely used for describing seed germination and emergence of plants as affected by the environmental temperature and/or water potential. Owing to their simplicity and the direct biological interpretation of model parameters, these models have become one of the most common tools for both predicting germination as well as understanding the physiology of germination responses to environmental factors. However, these models are usually fitted by using nonlinear regression and, therefore, they fall short of statistical rigor when inference about model parameters is of interest. In this study, we develop HT-to-event, TT-to-event and HTT-to-event models and provide a readily available implementation relying on the package “drc” in the R statistical environment. Examples of usage are also provided and we highlight the possible advantages of this procedure, such as efficiency and flexibility

Data from: Why we do not expect dispersal probability density functions based on a single mechanism to fit real seed shadows

Bullock et al. (Journal of Ecology 105:6-19, 2017) have suggested that the theory behind the Wald Analytical Long Distance (WALD) model for wind dispersal from a point source needs to be re-examined. This is on the basis that an inverse Gaussian probability density function (pdf) does not provide the best fit to seed shadows around individual source plants known to be dispersed by wind. We present two reasons why we would not necessarily expect any of the standard mechanistically derived pdfs to fit real seed shadows any better than empirical functions. Firstly, the derivation of “off-the-shelf” pdfs such as the Gaussian, exponential and inverse Gaussian involves only one of the processes and factors that together generate a real seed shadow. It is implausible to expect that a single-process model, no matter how sophisticated in detail, will capture the behaviour of an entire, complex system, which may involve a number of sequential random processes, or a superposition of parallel random processes, or both. Secondly, even if there is only one process involved and we have a perfect model for that process, the basic parameters of the model would be difficult to pin down precisely. Moreover, these parameters are unlikely to remain constant over a dispersal season, so that effectively we observe the outcome of a linear combination of dispersal events with different parameter values, constituting a form of averaging over the parameters of the distribution. Simple examples show that averaging a pdf over its parameters can lead to a pdf from an entirely different class. Synthesis. The failure of the inverse Gaussian model to fit seed shadow data is not in itself a reason to doubt the validity of the Wald Analytical Long Distance model for movement of particles through the air under specified environmental conditions. A greater awareness is needed of the differences between the Wald Analytical Long Distance and the inverse Gaussian (or Wald) and the purposes for which they are used. The complexity of dispersing populations of seeds means that any of the standard mechanistically derived pdfs will actually be merely empirical in this context. Shape and flexibility of a pdf is far more important for adequately describing data than some perceived higher status

Experimental data manipulations to assess performance of hyperspectral classification models of crop seeds and other objects.

BackgroundOptical sensing solutions are being developed and adopted to classify a wide range of biological objects, including crop seeds. Performance assessment of optical classification models remains both a priority and a challenge.MethodsAs training data, we acquired hyperspectral imaging data from 3646 individual tomato seeds (germination yes/no) from two tomato varieties. We performed three experimental data manipulations: (1) Object assignment error: effect of individual object in the training data being assigned to the wrong class. (2) Spectral repeatability: effect of introducing known ranges (0-10%) of stochastic noise to individual reflectance values. (3) Size of training data set: effect of reducing numbers of observations in training data. Effects of each of these experimental data manipulations were characterized and quantified based on classifications with two functions [linear discriminant analysis (LDA) and support vector machine (SVM)].ResultsFor both classification functions, accuracy decreased linearly in response to introduction of object assignment error and to experimental reduction of spectral repeatability. We also demonstrated that experimental reduction of training data by 20% had negligible effect on classification accuracy. LDA and SVM classification algorithms were applied to independent validation seed samples. LDA-based classifications predicted seed germination with RMSE = 10.56 (variety 1) and 26.15 (variety 2), and SVM-based classifications predicted seed germination with RMSE = 10.44 (variety 1) and 12.58 (variety 2).ConclusionWe believe this study represents the first, in which optical seed classification included both a thorough performance evaluation of two separate classification functions based on experimental data manipulations, and application of classification models to validation seed samples not included in training data. Proposed experimental data manipulations are discussed in broader contexts and general relevance, and they are suggested as methods for in-depth performance assessments of optical classification models