Plot size matters: Toward comparable species richness estimates across plot-based inventories

To understand the state and trends in biodiversity beyond the scope of monitoring programs, biodiversity indicators must be comparable across inventories. Species richness (SR) is one of the most widely used biodiversity indicators. However, as SR increases with the size of the area sampled, inventories using different plot sizes are hardly comparable. This study aims at producing a methodological framework that enables SR comparisons across plot-based inventories with differing plot sizes. We used National Forest Inventory (NFI) data from Norway, Slovakia, Spain, and Switzerland to build sample-based rarefaction curves by randomly incrementally aggregating plots, representing the relationship between SR and sampled area. As aggregated plots can be far apart and subject to different environmental conditions, we estimated the amount of environmental heterogeneity (EH) introduced in the aggregation process. By correcting for this EH, we produced adjusted rarefaction curves mimicking the sampling of environmentally homogeneous forest stands, thus reducing the effect of plot size and enabling reliable SR comparisons between inventories. Models were built using the Conway–Maxell–Poisson distribution to account for the underdispersed SR data. Our method successfully corrected for the EH introduced during the aggregation process in all countries, with better performances in Norway and Switzerland. We further found that SR comparisons across countries based on the country-specific NFI plot sizes are misleading, and that our approach offers an opportunity to harmonize pan-European SR monitoring. Our method provides reliable and comparable SR estimates for inventories that use different plot sizes. Our approach can be applied to any plot-based inventory and count data other than SR, thus allowing a more comprehensive assessment of biodiversity across various scales and ecosystems.publishedVersio

Landscape dynamics and diversification of the megadiverse South American freshwater fish fauna

Landscape dynamics are widely thought to govern the tempo and mode of continental radiations, yet the effects of river network rearrangements on dispersal and lineage diversification remain poorly understood. We integrated an unprecedented occurrence dataset of 4,967 species with a newly compiled, time-calibrated phylogeny of South American freshwater fishes—the most species-rich continental vertebrate fauna on Earth—to track the evolutionary processes associated with hydrogeographic events over 100 Ma. Net lineage diversification was heterogeneous through time, across space, and among clades. Five abrupt shifts in net diversification rates occurred during the Paleogene and Miocene (between 30 and 7 Ma) in association with major landscape evolution events. Net diversification accelerated from the Miocene to the Recent (c. 20 to 0 Ma), with Western Amazonia having the highest rates of in situ diversification, which led to it being an important source of species dispersing to other regions. All regional biotic interchanges were associated with documented hydrogeographic events and the formation of biogeographic corridors, including the Early Miocene (c. 23 to 16 Ma) uplift of the Serra do Mar and Serra da Mantiqueira and the Late Miocene (c. 10 Ma) uplift of the Northern Andes and associated formation of the modern transcontinental Amazon River. The combination of high diversification rates and extensive biotic interchange associated with Western Amazonia yielded its extraordinary contemporary richness and phylogenetic endemism. Our results support the hypothesis that landscape dynamics, which shaped the history of drainage basin connections, strongly affected the assembly and diversification of basin-wide fish fauna

Model averaging in ecology: a review of Bayesian, information-theoretic and tactical approaches for predictive inference

In ecology, the true causal structure for a given problem is often not known, and several plausible models and thus model predictions exist. It has been claimed that using weighted averages of these models can reduce prediction error, as well as better reflect model selection uncertainty. These claims, however, are often demonstrated by isolated examples. Analysts must better understand under which conditions model averaging can improve predictions and their uncertainty estimates. Moreover, a large range of different model averaging methods exists, raising the question of how they differ in their behaviour and performance. Here, we review the mathematical foundations of model averaging along with the diversity of approaches available. We explain that the error in model‐averaged predictions depends on each model's predictive bias and variance, as well as the covariance in predictions between models, and uncertainty about model weights. We show that model averaging is particularly useful if the predictive error of contributing model predictions is dominated by variance, and if the covariance between models is low. For noisy data, which predominate in ecology, these conditions will often be met. Many different methods to derive averaging weights exist, from Bayesian over information‐theoretical to cross‐validation optimized and resampling approaches. A general recommendation is difficult, because the performance of methods is often context dependent. Importantly, estimating weights creates some additional uncertainty. As a result, estimated model weights may not always outperform arbitrary fixed weights, such as equal weights for all models. When averaging a set of models with many inadequate models, however, estimating model weights will typically be superior to equal weights. We also investigate the quality of the confidence intervals calculated for model‐averaged predictions, showing that they differ greatly in behaviour and seldom manage to achieve nominal coverage. Our overall recommendations stress the importance of non‐parametric methods such as cross‐validation for a reliable uncertainty quantification of model‐averaged predictions

Data from: Small and ugly? Phylogenetic analyses of the “selfing syndrome” reveal complex evolutionary fates of monomorphic primrose flowers

One of the most common trends in plant evolution, loss of self-incompatibility and ensuing increases in selfing, is generally assumed to be associated with a suite of phenotypic changes, notably a reduction of floral size, termed the selfing syndrome. We investigate whether floral morphological traits indeed decrease in a deterministic fashion after losses of self-incompatibility, as traditionally expected, using a phylogeny of 124 primrose species containing nine independent transitions from heterostyly (heteromorphic incompatibility) to homostyly (monomorphic self-compatibility), a classic system for evolution of selfing. We find similar overall variability of homostylous and heterostylous species, except for diminished herkogamy in homostyles. Bayesian mixed models demonstrate differences between homostylous and heterostylous species in all traits, but net effects across species are small (except herkogamy) and directionality differs among traits. Strongly drift-like evolutionary trajectories of corolla tube length and corolla diameter inferred by Ornstein-Uhlenbeck models contrast with expected deterministic trajectories toward small floral size. Lineage-specific population genetic effects associated with evolution of selfing may explain that reductions of floral size represent one of several possible outcomes of floral evolution after loss of heterostyly in primroses. Contrary to the traditional paradigm, selfing syndromes may, but do not necessarily evolve in response to increased selfing

Binary scoring of heterostyly, 265 Primulaceae taxa

Contains scoring of breeding system for the 265 taxa of Primulaceae s.str

Species means data, 124 primrose species

Contains species means, calculated from the data in file "morphoAlldata_124taxa_Dryad.txt". Rows represent species with their means. Column "n_obs" is the number of observations from which the means were calculated. Other columns as for "morphoAlldata_124taxa_Dryad.txt"

TEMPEX: Developing spatial layers of climatic temperature extremes: Final report in the BAFU-WSL program “Forests and Climate Change”

In this project we calculated eight indices of temperature extremes and a compound index of frost frequency after leaf flush, which was tuned to four different species and calculated in a prognostic mode. The eight indices extracted from downscaled climate data, scaled to NFI plots (1 x 1 km NFI 1 network) in Switzerland for current and projected future climates, included the following: (1) the absolute annual minimum temperature; (2) absolute annual maximum temperature; (3) the annual number of frost days (Tmin < 0°C); (4) the annual number of frozen days (Tmax < 0°C); (5) the annually largest diurnal temperature range; (6) the mean annual diurnal temperature range; (7) the annually longest period of continuous frost days (Tmin < 0°C); (8) the annually longest period of continuous frozen days (Tmax < 0°C). All nine measures generated here indicate that the projected climate trends are affecting the temperature extremes as much as they do the temperature means. The extremes decrease at a rate comparable to temperature means, and the climatic conditions become gradually suitable to species that are less tolerant to low temperature extremes. By the end of the 21st Century, the climate barely reaches conditions represented as the cold limit of Mediterranean species (30-year mean of annual absolute minimum temperatures of ca. -6 °C on the Swiss Plateau). Since the climate is fluctuating quite considerably, and because the trajectory barely reaches this threshold towards the end of the 21st Century, it is not very likely that truly mediterranean species will already find suitable habitats then. Rather, sub-mediterranean and warm-temperate species will be the preferred choice for adaptive and active forest management practice. It remains to be considered that the indices presented here do not include drought, which represents another important constraint for the choice of suitable tree species. Caution is needed when interpreting the presented results. First, we only use data from one scenario (A1B), whereas there is uncertainty as to which of the usually four scenarios used is actually most suitable for describing the future trajectory of the climate. Second, we only use data from three regional climate models (CLM, RCA30, RegCM3) that are all fed by data from the same global circulation model (ECHAM5). This means that only a small variety of possible futures is explored. Finally, we had to request new versions of the obtained data from Meteotest due to errors found during the processing. While we think that most problems are solved now, we are not completely the currently used data is error-free, as we still found some patterns that are counterintuitive. Some of this may originate from the RCM data, though

Trait-CWMs From Guisane Valley in the French Alps

The file contains the necessary data to repeat the analyses presented in the article "Integrating correlation between traits improves spatial predictions of plant functional composition". The first column "plotID" contains an identifier for each plot. The following four columns list the centered values of community weighted means of the four analyzed traits, where "height" stands for vegetation height, "seedm" stands for seed mass, "ldmc" stands for leaf dry matter content, and "sla" stands for specific leaf area. The remaining four columns contain the environmental data that characterizes the plots, where "precip" stands for precipitation, "relh" stands for relative humidity, "mintemp" stands for minimal temperature, and "topo" stands for topography. Be aware that all data are centered and scaled such that each column's mean is zero, their standard deviation is one

Data from: Integrating correlation between traits improves spatial predictions of plant functional composition

Functional trait composition is increasingly recognized as key to better understand and predict community responses to environmental gradients. Predictive approaches traditionally model the weighted mean trait values of communities (CWMs) as a function of environmental gradients. However, most approaches treat traits as independent regardless of known trade-offs between them, which could lead to spurious predictions. To address this issue, we suggest jointly modeling a suit of functional traits along environmental gradients while accounting for relationships between traits. We use generalized additive mixed effect models to predict the functional composition of alpine grasslands in the Guisane Valley (France). We demonstrate that, compared to traditional approaches, joint trait models explain considerable amounts of variation in CWMs, yield less uncertainty in trait CWM predictions and provide more realistic spatial projections when extrapolating to novel environmental conditions. Modeling traits and their co-variation jointly is an alternative and superior approach to predicting traits independently. Additionally, compared to a “predict first, assemble later” approach that estimates trait CWMs post hoc based on stacked species distribution models, our “assemble first, predict later” approach directly models trait-responses along environmental gradients, and does not require data and models on species’ distributions, but only mean functional trait values per community plot. This highlights the great potential of joint trait modeling approaches in large-scale mapping applications, such as spatial projections of the functional composition of vegetation and associated ecosystem services as a response to contemporary global change