The Impact of Herbivory and Competition on Flowering and Survival during Saltmarsh Succession

Abstract: For succession, the establishment and turnover of plants, in terms of generative spreading and renewal, is of crucial importance. Especially the success of seedling establishment into the existing vegetation might be of prime importance for the further development of plant species composition. Flowering, on the other hand, is the necessary first step towards generative output and later seed set. In a chronosequential gradient of a saltmarsh succession, we investigated the performance of transplants of three common species under different conditions: Artemisia maritima, Atriplex portulacoides and Plantago maritima were transplanted as seedlings and mature plants into plots in early-, mid-, and late-successional stages (15, 30 and 40 years) on the Dutch island of Schiermonnikoog. Natural herbivory (by hares and geese) and neighbourhood competition were excluded in a factorial design. Flowering and survival rates of the three species were generally greatly reduced under natural conditions, compared to competition- and herbivory-free plots. The survival rates of Artemisia maritima and Plantago maritima reflect their position as early-successional species: early in succession, both show their highest survival and abundance. For late-dominant Atriplex portulacoides a different explanation has to be invoked for the medium-high survival and low abundance: probably winter grazing by hares and the importance of a high nitrogen supply rate, rather than flowering and survival, determine its low abundance in the early and mid-phase of this saltmarsh succession. Some long-term impacts of herbivores affecting flowering and survival are discussed.     

Growth of Variants of Myxococcus virescens

Some observations on variant strains of Myxococcus virescens B2 with special emphasis on characteristics associated with the ability to grow in dispersion are reported. The isolated strains were divided into two major classes according to their mode of growth in shaken and static liquid cultures based on casitone and casamino acids media. Strains growing in dispersion were designated D⁺-strains and those growing in aggregates or as films, D^?-strains. Colony morphology, cell morphology, growth in liquid and on solid medium and morphogenesis were compared. The ability to grow in dispersion shown by D⁺-strains seemed to be associated with a smooth colony on casitone agar, inability to form typical fruiting bodies and a low linear growth rate of colonies on solid medium as compared with the D^?-strains. In contrast D^?-strains produced rough colonies on casitone agar, were able to fruit and evidently formed an adhesive slime in the form of fibrils extending from the cell surface. It is suggested that the observed differences depend on different envelopes of the cells in the two classes.     

The role of biotic interactions in shaping distributions and realised assemblages of species: implications for species distribution modelling

Predicting which species will occur together in the future, and where, remains one of the greatest challenges in ecology, and requires a sound understanding of how the abiotic and biotic environments interact with dispersal processes and history across scales. Biotic interactions and their dynamics influence species' relationships to climate, and this also has important implications for predicting future distributions of species. It is already well accepted that biotic interactions shape species' spatial distributions at local spatial extents, but the role of these interactions beyond local extents (e.g. 10 km² to global extents) are usually dismissed as unimportant. In this review we consolidate evidence for how biotic interactions shape species distributions beyond local extents and review methods for integrating biotic interactions into species distribution modelling tools. Drawing upon evidence from contemporary and palaeoecological studies of individual species ranges, functional groups, and species richness patterns, we show that biotic interactions have clearly left their mark on species distributions and realised assemblages of species across all spatial extents. We demonstrate this with examples from within and across trophic groups. A range of species distribution modelling tools is available to quantify species environmental relationships and predict species occurrence, such as: (i) integrating pairwise dependencies, (ii) using integrative predictors, and (iii) hybridising species distribution models (SDMs) with dynamic models. These methods have typically only been applied to interacting pairs of species at a single time, require a priori ecological knowledge about which species interact, and due to data paucity must assume that biotic interactions are constant in space and time. To better inform the future development of these models across spatial scales, we call for accelerated collection of spatially and temporally explicit species data. Ideally, these data should be sampled to reflect variation in the underlying environment across large spatial extents, and at fine spatial resolution. Simplified ecosystems where there are relatively few interacting species and sometimes a wealth of existing ecosystem monitoring data (e.g. arctic, alpine or island habitats) offer settings where the development of modelling tools that account for biotic interactions may be less difficult than elsewhere.     

The cost of virulence: retarded growth of Salmonella Typhimurium cells expressing type III secretion system 1

Virulence factors generally enhance a pathogen's fitness and thereby foster transmission. However, most studies of pathogen fitness have been performed by averaging the phenotypes over large populations. Here, we have analyzed the fitness costs of virulence factor expression by Salmonella enterica subspecies I serovar Typhimurium in simple culture experiments. The type III secretion system ttss-1, a cardinal virulence factor for eliciting Salmonella diarrhea, is expressed by just a fraction of the S. Typhimurium population, yielding a mixture of cells that either express ttss-1 (TTSS-1(+) phenotype) or not (TTSS-1(-) phenotype). Here, we studied in vitro the TTSS-1(+) phenotype at the single cell level using fluorescent protein reporters. The regulator hilA controlled the fraction of TTSS-1+ individuals and their ttss-1 expression level. Strikingly, cells of the TTSS-1(+) phenotype grew slower than cells of the TTSS-1(-) phenotype. The growth retardation was at least partially attributable to the expression of TTSS-1 effector and/or translocon proteins. In spite of this growth penalty, the TTSS-1(+) subpopulation increased from <10% to approx. 60% during the late logarithmic growth phase of an LB batch culture. This was attributable to an increasing initiation rate of ttss-1 expression, in response to environmental cues accumulating during this growth phase, as shown by experimental data and mathematical modeling. Finally, hilA and hilD mutants, which form only fast-growing TTSS-1(-) cells, outcompeted wild type S. Typhimurium in mixed cultures. Our data demonstrated that virulence factor expression imposes a growth penalty in a non-host environment. This raises important questions about compensating mechanisms during host infection which ensure successful propagation of the genotype.     

A standard protocol for reporting species distribution models

Species distribution models (SDMs) constitute the most common class of models across ecology, evolution and conservation. The advent of ready-to-use software packages and increasing availability of digital geoinformation have considerably assisted the application of SDMs in the past decade, greatly enabling their broader use for informing conservation and management, and for quantifying impacts from global change. However, models must be fit for purpose, with all important aspects of their development and applications properly considered. Despite the widespread use of SDMs, standardisation and documentation of modelling protocols remain limited, which makes it hard to assess whether development steps are appropriate for end use. To address these issues, we propose a standard protocol for reporting SDMs, with an emphasis on describing how a study's objective is achieved through a series of modeling decisions. We call this the ODMAP (Overview, Data, Model, Assessment and Prediction) protocol, as its components reflect the main steps involved in building SDMs and other empirically-based biodiversity models. The ODMAP protocol serves two main purposes. First, it provides a checklist for authors, detailing key steps for model building and analyses, and thus represents a quick guide and generic workflow for modern SDMs. Second, it introduces a structured format for documenting and communicating the models, ensuring transparency and reproducibility, facilitating peer review and expert evaluation of model quality, as well as meta-analyses. We detail all elements of ODMAP, and explain how it can be used for different model objectives and applications, and how it complements efforts to store associated metadata and define modelling standards. We illustrate its utility by revisiting nine previously published case studies, and provide an interactive web-based application to facilitate its use. We plan to advance ODMAP by encouraging its further refinement and adoption by the scientific community.     

Crop–noncrop spillover: arable fields affect trophic interactions on wild plants in surrounding habitats

Ecosystem processes in agricultural landscapes are often triggered by resource availability in crop and noncrop habitats. We investigated how oilseed rape (OSR; Brassica napus, Brassicaceae) affects noncrop plants in managed systems and semi-natural habitat, using trophic interactions among wild mustard (Sinapis arvensis, Brassicaceae), rape pollen beetles (Meligethes aeneus, Nitidulidae) and their parasitoids (Tersilochus heterocerus, Ichneumonidae). We exposed wild mustard as phytometer plants in two cropland habitat types (wheat field, field margin) and three noncrop habitat types (fallow, grassland, wood margin) across eight landscapes along a gradient from simple to complex (quantified as % arable land). Both landscape and local factors affected the abundance of rape pollen beetles and parasitoids. Rape pollen beetle infestation and parasitism rates on these plants were lower in noncrop habitats and higher in wheat fields and field margins, whereas beetles and parasitoids responded differently to landscape scale parameters. We found the hypothesized spillover from OSR crop onto wild plants in surrounding habitats only for parasitoids, but not for pollen beetles. Parasitism rates were not related to landscape simplification, but benefited from increasing proportions of OSR. In contrast, rape pollen beetles benefited from simple landscape structures, presumably due to multi-annual population build-ups resulting from long-term OSR planting (as part of the crop rotation). In conclusion, we showed that spillover from cropland affects parasitism rates on related wild plants outside cropland, which has not been shown so far, but can be expected to be a widespread effect shaping noncrop food webs.     

Refuges from fire maintain pollinator–plant interaction networks

Fire is a major disturbance factor in many terrestrial ecosystems, leading to landscape transformation in fire‐prone areas. Species in mutualistic interactions are often highly sensitive to disturbances like fire events, but the degree and complexity of their responses are unclear. We use bipartite insect–flower interaction networks across a recently burned landscape to explore how plant–pollinator interaction networks respond to a recent major fire event at the landscape level, and where fire refuges were present. We also investigate the effectiveness of these refuges at different elevations (valley to hilltop) for the conservation of displaced flower‐visiting insects during fire events. Then, we explore how the degree of specialization of flower‐visiting insects changes across habitats with different levels of fire impact. We did this in natural areas in the Greater Cape Floristic Region (GCFR) biodiversity hotspot, which is species rich in plants and pollinators. Bees and beetles were the most frequent pollinators in interactions, followed by wasps and flies. Highest interaction activity was in the fire refuges and least in burned areas. Interactions also tracked flower abundance, which was highest in fire refuges in the valley and lowest in burned areas. Interactions consisted mostly of specialized flower visitors, especially in refuge areas. The interaction network and species specialization were lowest in burned areas. However, species common to at least two fire classes showed no significant difference in species specialization. We conclude that flower‐rich fire refuges sustain plant–pollinator interactions, especially those involving specialized species, in fire‐disturbed landscape. This may be an important shelter for specialized pollinator species at the time that the burned landscape goes through regrowth and succession as part of ecosystem recovery process after a major fire event.     

Chemotactic cell movement during development

Chemotaxis is an important mechanism controlling cell migration over either short or long distances during different developmental processes. Small rapid diffusing chemo-attractants are detected through serpentine, G protein coupled receptors through graded activation of receptors along the length of the cell. Internal amplification results in polarisation of the actin and myosin cytoskeletal dynamics along the gradient and directed movement. The dynamics of these processes can now be studied in individual cells in developing organisms. Slow diffusing chemo-attractants such as growth factors, providing short-range guidance information, often signal through tyrosine kinase receptors. Detection of these signals may involve the active extension of very long cellular process up growth factor gradients, followed by translocation of the cell in the direction of the gradient.     

Cross‐validation strategies for data with temporal,spatial, hierarchical,or phylogenetic structure

Ecological data often show temporal, spatial, hierarchical (random effects), or phylogenetic structure. Modern statistical approaches are increasingly accounting for such dependencies. However, when performing cross‐validation, these structures are regularly ignored, resulting in serious underestimation of predictive error. One cause for the poor performance of uncorrected (random) cross‐validation, noted often by modellers, are dependence structures in the data that persist as dependence structures in model residuals, violating the assumption of independence. Even more concerning, because often overlooked, is that structured data also provides ample opportunity for overfitting with non‐causal predictors. This problem can persist even if remedies such as autoregressive models, generalized least squares, or mixed models are used. Block cross‐validation, where data are split strategically rather than randomly, can address these issues. However, the blocking strategy must be carefully considered. Blocking in space, time, random effects or phylogenetic distance, while accounting for dependencies in the data, may also unwittingly induce extrapolations by restricting the ranges or combinations of predictor variables available for model training, thus overestimating interpolation errors. On the other hand, deliberate blocking in predictor space may also improve error estimates when extrapolation is the modelling goal. Here, we review the ecological literature on non‐random and blocked cross‐validation approaches. We also provide a series of simulations and case studies, in which we show that, for all instances tested, block cross‐validation is nearly universally more appropriate than random cross‐validation if the goal is predicting to new data or predictor space, or for selecting causal predictors. We recommend that block cross‐validation be used wherever dependence structures exist in a dataset, even if no correlation structure is visible in the fitted model residuals, or if the fitted models account for such correlations.