Drivers of plant species’ potential to spread: the importance of demography versus seed dispersal

Understanding the ability of plants to spread is important for assessing conservation strategies, landscape dynamics, invasiveness and ability to cope with climate change. While long-distance seed dispersal is often viewed as a key process in population spread, the importance of inter-specific variation in demography is less explored. Indeed, the relative importance of demography vs seed dispersal in determining population spread is still little understood. We modelled species’ potential for population spread in terms of annual migration rates for a set of species inhabiting dry grasslands of central Europe. Simultaneously, we estimated the importance of demographic (population growth rate) vs long-distance dispersal (99th percentile dispersal distance) characteristics for among-species differences in modelled population spread. In addition, we assessed how well simple proxy measures related to demography (the number and survival of seedlings, the survival of flowering individuals) and dispersal (plant height, terminal velocity and wind speed during dispersal) predicted modelled spread rates. We found that species’ demographic rates were the more powerful predictors of species’ modelled potential to spread than dispersal. Furthermore, our simple proxies were correlated with modelled species spread rates and together their predictive power was high. Our findings highlight that for understanding variation among species in their potential for population spread, detailed information on local demography and dispersal might not always be necessary. Simple proxies or assumptions that are based primarily on species demography could be sufficient

Human‐mediated dispersal and disturbance shape the metapopulation dynamics of a long‐lived herb

As anthropogenic impacts on the natural world escalate, there is increasing interest in the role of humans in dispersing seeds. But the consequences of this Human‐Mediated Dispersal (HMD) on plant spatial dynamics are little studied. In this paper, we ask how secondary dispersal by HMD affects the dynamics of a natural plant metapopulation. In addition to dispersal between patches, we suggest within‐patch processes can be critical. To address this, we assess how variation in local population dynamics, caused by small‐scale disturbances, affects metapopulation size. We created an empirically based model with stochastic population dynamics and dispersal among patches, which represented a real‐world, cliff‐top metapopulation of wild cabbage Brassica oleracea. We collected demographic data from multiple populations by tagging plants over eight years. We assessed seed survival, and establishment and survival of seedlings in intact vegetation vs. small disturbances. We modeled primary dispersal by wind using field data and used experimental data on secondary HMD by hikers. We monitored occupancy patterns over a 14‐yr period in the real metapopulation. Disturbance had large effects on local population growth rates, by increasing seedling establishment and survival. This meant that the modeled metapopulation grew in size only when the area disturbed in each patch was above 35%. In these growing metapopulations, although only 0.2% of seeds underwent HMD, this greatly enhanced metapopulation growth rates. Similarly, HMD allowed more colonizations in declining metapopulations under low disturbance, and this slowed the rate of decline. The real metapopulation showed patterns of varying patch occupancy over the survey years, which were related to habitat quality, but also positively to human activity along the cliffs, hinting at beneficial effects of humans. These findings illustrate that realistic changes to dispersal or demography, specifically by humans, can have fundamental effects on the viability of a species at the landscape scale

A continental-scale validation of ecosystem service models

Faced with environmental degradation, governments worldwide are developing policies to safeguard ecosystem services (ES). Many ES models exist to support these policies, but they are generally poorly validated, especially at large scales, which undermines their credibility. To address this gap, we describe a study of multiple models of five ES, which we validate at an unprecedented scale against 1675 data points across sub-Saharan Africa. We find that potential ES (biophysical supply of carbon and water) are reasonably well predicted by the existing models. These potential ES models can also be used as inputs to new models for realised ES (use of charcoal, firewood, grazing resources and water), by adding information on human population density. We find that increasing model complexity can improve estimates of both potential and realised ES, suggesting that developing more detailed models of ES will be beneficial. Furthermore, in 85% of cases, human population density alone was as good or a better predictor of realised ES than ES models, suggesting that it is demand, rather than supply that is predominantly determining current patterns of ES use. Our study demonstrates the feasibility of ES model validation, even in data-deficient locations such as sub-Saharan Africa. Our work also shows the clear need for more work on the demand side of ES models, and the importance of model validation in providing a stronger base to support policies which seek to achieve sustainable development in support of human well-being

Effects of habitat fragmentation on the fitness of two common wetland species, Carex davalliana and Succisa pratensis

Small habitat size and spatial isolation may cause plant populations to suffer from genetic drift and inbreeding, leading to a reduced fitness of individual plants. We examined the germination, establishment, growth, and reproductive capacity of two characteristic species of mown fen meadows, Carex davalliana, and Succisa pratensis, common in Switzerland. Plants were grown from seeds, which were collected in 18 habitat islands, differing in size and in degree of isolation. We used both common garden and reciprocal transplant experiments to assess effects of habitat fragmentation. In the common garden, plants of Carex originating from small habitat islands yielded 35% less biomass, 30% fewer tillers, and 45% fewer flowering tillers than plants from larger ones. In contrast, plants of Succisa originating from small habitat islands yielded 19% more biomass, 14% more flower heads and 35% more flowers per flower head than plants from larger ones. Moreover, plants of Succisa from small isolated habitats yielded 32% more rosettes than did plants from small connected islands. Reciprocally transplanted plants of Succisa originating from small habitat islands produced 7% more rosettes than plants from larger ones. There was no effect of small habitat size and isolation on germination and establishment of both species in the field. Our results document genetic differences in performance attributable to habitat fragmentation in both species. We suggest that fitness loss in Carex is caused by inbreeding depression, whereas in Succisa the differences in fitness are more likely caused by genetic differentiation. Our study implies that habitat fragmentation affects common habitat-specific species, such as Carex and Succisa, as well as rare ones

Data from: A synthesis of empirical plant dispersal kernels

Dispersal is fundamental to ecological processes at all scales and levels of organization, but progress is limited by a lack of information about the general shape and form of plant dispersal kernels. We addressed this gap by synthesizing empirical data describing seed dispersal and fitting general dispersal kernels representing major plant types and dispersal modes. A comprehensive literature search resulted in 107 papers describing 168 dispersal kernels for 144 vascular plant species. The data covered 63 families, all the continents except Antarctica, and the broad vegetation types of forest, grassland, shrubland and more open habitats (e.g. deserts). We classified kernels in terms of dispersal mode (ant, ballistic, rodent, vertebrates other than rodents, vehicle or wind), plant growth form (climber, graminoid, herb, shrub or tree), seed mass and plant height. We fitted 11 widely used probability density functions to each of the 168 data sets to provide a statistical description of the dispersal kernel. The exponential power (ExP) and log-sech (LogS) functions performed best. Other 2-parameter functions varied in performance. For example, the log-normal and Weibull performed poorly, while the 2Dt and power law performed moderately well. Of the single-parameter functions, the Gaussian performed very poorly, while the exponential performed better. No function was among the best-fitting for all data sets. For 10 plant growth form/dispersal mode combinations for which we had >3 data sets, we fitted ExP and LogS functions across multiple data sets to provide generalized dispersal kernels. We also fitted these functions to subdivisions of these growth form/dispersal mode combinations in terms of seed mass (for animal-dispersed seeds) or plant height (wind-dispersed) classes. These functions provided generally good fits to the grouped data sets, despite variation in empirical methods, local conditions, vegetation type and the exact dispersal process. Synthesis. We synthesize the rich empirical information on seed dispersal distances to provide standardized dispersal kernels for 168 case studies and generalized kernels for plant growth form/dispersal mode combinations. Potential uses include the following: (i) choosing appropriate dispersal functions in mathematical models; (ii) selecting informative dispersal kernels for one's empirical study system; and (iii) using representative dispersal kernels in cross-taxon comparative studies

Plant dispersal data

Plant dispersal dat

Supplement 1. Data about seed dispersal distances and related traits for 576 plant species used in the analyses, and references for data sources.

<h2>File List</h2><div> <p><a href="DispersalDistanceData.csv">DispersalDistanceData.csv</a> (Md5: 9f51b823e51b7570a42a832bed1fbf07)</p> </div><h2>Description</h2><div> <p>DispersalDistanceData.csv – Data about seed dispersal distances and related traits for 576 plant species. For some species dispersal distance data is available for different dispersal syndromes and thus 600 data points are included. Missing values are denoted by empty cells. 601 rows (with headers), 22 columns, separator "," , decimal ".".</p> <p>Columns are as follows:</p> <blockquote> <p><b>Species</b>: accepted binomial name (without authorship) of a species, validated using Taxonstand (Cayuela et al. 2012) library </p> <p><b>Original</b>: binomial name of a species in a referenced study if different from accepted binomial name</p> <p><b>Genus</b>: taxonomic genus of a species</p> <p><b>Family</b>: taxonomic family of a species, obtained using Taxonstand (Cayuela et al. 2012) library</p> <p><b>Order</b>: taxonomic order of a species, following APG III for angiosperms (The Angiosperm Phylogeny Group 2009) and Christenhuzs and others (2011) for gymnosperms</p> <p><b>Dispersal_syndrome</b>: species’ dispersal syndrome corresponding to the dispersal distance data, derived from the referenced study, categories include ‘animal’, ‘ant’, ‘ballistic’, ‘wind (none)’, ‘wind (special)’</p> <p><b>Growth_form</b>: growth form data derived from the referenced study or databases LEDA (Kleyer et al. 2008) and PLANTS (USDA and NRCS 2011), categories include ‘herb’, ‘shrub’, ‘tree’</p> <p><b>Seed_weight_(mg)</b>: seed mass (mg) data derived from the referenced study or Seed Information Database (Royal Botanic Gardens Kew 2008)</p> <p><b>Seed_release_height_(m)</b>: seed releasing height (m) data derived from the referenced study or LEDA (Kleyer et al. 2008) database</p> <p><b>Seed_terminal_velocity_(m/s)</b>: seed terminal velocity (m/s) data derived from the referenced study or LEDA (Kleyer et al. 2008) database</p> <p><b>Maximum_recorded_dispersal_distance_(m)</b>: species’ maximum dispersal distance (m) found in the literature corresponding to a specific dispersal syndrome</p> <p><b>99th_percentile_dispersal_distance_(m)</b>: 99th percentile of a species’ dispersal distance distribution (m) corresponding to a specific dispersal syndrome</p> <p><b>90th_percentile_dispersal_distance_(m)</b>: 90th percentile of a species’ dispersal distance distribution (m) corresponding to a specific dispersal syndrome</p> <p><b>Mean_dispersal_distance_(m)</b>: species’ mean dispersal distance (m) corresponding to a specific dispersal syndrome<b> </b></p> <p><b>Mode_dispersal_distance_(m)</b>: mode of a species’ dispersal distance distribution (m) corresponding to a specific dispersal syndrome</p> <p><b>Median_dispersal_distance_(m)</b>: median of a species’ dispersal distance distribution (m) corresponding to a specific dispersal syndrome</p> <p><b>Maximum_dispersal_distance_(m)</b>: equals to maximum_recorded_dispersal_distance_(m) if given, otherwise equals to 99th_percentile_dispersal_distance_(m) or 90th_percentile_dispersal_distance_(m)</p> <p><b>Calculated_maximum_dispersal_distance_(m)</b>: when no maximum dispersal distance data was available, we estimated maximum dispersal distance using the formula log10(maximum) = 0.795 + 0.984 * log10(mean); if mean dispersal distance was not available, we used the mode or median of a species’ dispersal distance distribution </p> <p><b>Maximum_dispersal_distance_analysis_(m)</b>: data used in the analyses, equals to Maximum_dispersal_distance_(m) or (if maximum was not available) to Calculated_maximum_dispersal_distance_(m)</p> <p><b>Data_type</b>: denotes whether data source was an observational (‘field’) or modeling (‘model’) study</p> <p><b>Region</b>: region of the case study, categories include ‘temperate’ or ‘tropics’</p> <p><b>Reference</b>: reference for a data source</p> </blockquote> <p> </p> </div

Appendix A. Overview of the results for linear models to explain maximum dispersal distances using different combinations of plant traits, and histograms of maximum dispersal distance data for different dispersal syndromes and growth forms.

Overview of the results for linear models to explain maximum dispersal distances using different combinations of plant traits, and histograms of maximum dispersal distance data for different dispersal syndromes and growth forms