Dose-response curves: the next frontier in plant ecology

A large fraction of experimental work in plant ecology, and thus also on ecosystem functioning and the delivery of ecosystem services, describes experiments that have been carried out under controlled (glasshouse) conditions. Controlled growth settings commonly sacrifice realism through, for example, reducing the densities of plant species in the pots and controlling how environmental settings such as moisture and light vary in favor of a higher mechanistic resolution, which makes these studies particularly suitable for subsequent syntheses. We explore the possibility that further integration of dose-response curves can maximize the impact of existing studies. We suggest that we can expand considerably the scope of the dose and response variables that are considered in plant ecology.

Metabarcoding is (usually) more cost effective than seining or qPCR for detecting tidewater gobies and other estuarine fishes

Many studies have shown that environmental DNA (eDNA) sampling can be more sensitive than traditional sampling. For instance, past studies found a specific qPCR probe of a water sample is better than a seine for detecting the endangered northern tidewater goby, Eucyclogobius newberryi. Furthermore, a metabarcoding sample often detects more fish species than a seine detects. Less consideration has been given to sampling costs. To help managers choose the best sampling method for their budget, I estimated detectability and costs per sample to compare the cost effectiveness of seining, qPCR and metabarcoding for detecting endangered tidewater gobies as well as the associated estuarine fish community in California. Five samples were enough for eDNA methods to confidently detect tidewater gobies, whereas seining took twice as many samples. Fixed program costs can be high for qPCR and seining, whereas metabarcoding had high per-sample costs, which led to changes in relative cost-effectiveness with the number of locations sampled. Under some circumstances (multiple locations visited or an already validated assay), qPCR was a bit more cost effective than metabarcoding for detecting tidewater gobies. Under all assumptions, seining was the least cost-effective method for detecting tidewater gobies or other fishes. Metabarcoding was the most cost-effective sampling method for multiple species detection. Despite its advantages, metabarcoding has gaps in sequence databases, can yield vague results for some species, and can lead novices to serious errors. Seining remains the only way to rapidly assess densities, size distributions, and fine-scale spatial distributions.

Climate-associated variation in the drivers of benthic macroinvertebrate species-area relationships across shallow freshwater lakes

The island species-area relationship (ISAR) describes how species richness increases with increasing area of a given island or island-like habitat, such as freshwater lakes. While the ISAR is one of the most common phenomena observed in ecology, there is variation in both the form of the relationship and its underlying mechanisms. We compiled a global data set of benthic macroinvertebrates from 524 shallow freshwater lakes, ranging from 1 to 293,300 ha in area. We used individual-based rarefaction to determine the degree to which ISAR was influenced by mechanisms other than passive sampling (larger islands passively sample more individuals from the regional pool and, therefore, have more species than smaller islands), which would bias results away from expected relationships between rarefied species richness (and other measures that capture relative abundances) and lake area. We also examined how climate may alter the shape of the ISARs. We found that both rarefied species richness (the number of species standardized by area or number of individuals) and a measure of evenness emphasizing common species exhibit shallow slopes in relationships with lake area, suggesting that the expected ISARs in these lakes most likely result from passive sampling. While there was considerable variation among ISARs across the investigated lakes, we found an overall positive rarefied ISAR for lakes in warm (i.e. tropical/subtropical) regions (n = 195), and in contrast, an overall negative rarefied ISAR in cool (i.e. north temperate) lakes (n = 329). This suggested that mechanisms beyond passive sampling (e.g. colonization-extinction dynamics and/or heterogeneity) were more likely to operate in warm lakes. One possible reason for this difference is that the area-dependent intensity of fish predation, which can lead to flatter ISARs, is weaker in warmer relative to cooler lakes. Our study illustrates the importance of understanding both the pattern and potential processes underlying the ISARs of freshwater lakes in different climatic regions. Furthermore, it provides a baseline for understanding how further changes to the ecosystem (i.e. in lake area or climate) might influence biodiversity patterns.

Species-area relationships in microbial-mediated mutualisms

Symbioses involving microorganisms prevail in nature and are key to regulating numerous ecosystem processes and in driving evolution. A major concern in understanding the ecology of symbioses involving microorganisms arises in the effectiveness of sampling strategies to capture the contrasting size of organisms involved. In many mutualisms, including mycorrhizas and gut systems, hosts interact simultaneously with multiple smaller sized mutualists, the identity of which determines success for the host. This complicates quantifying the diversity of mutualisms because sampling techniques fail to capture effectively the diversity of each partner. Here we propose the use of species-area relationships (SARs) to explicitly consider the spatial scale of microbial partners in symbioses, which we propose will improve our understanding of the ecology of mutualisms.

On the shape and origins of the freshwater species-area relationship

The species-area relationship (SAR) has over a 150-year-long history in ecology, but how its shape and origins vary across scales and organisms remains incompletely understood. This is the first subcontinental freshwater study to examine both these properties of the SAR in a spatially explicit way across major organismal groups (diatoms, insects, and fish) that differ in body size and dispersal capacity. First, to describe the SAR shape, we evaluated the fit of three commonly used models, logarithmic, power, and Michaelis-Menten. Second, we proposed a hierarchical framework to explain the variability in the SAR shape, captured by the parameters of the SAR model. According to this framework, scale and species group were the top predictors of the SAR shape, climatic factors (heterogeneity and median conditions) represented the second predictor level, and metacommunity properties (intraspecific spatial aggregation, γ-diversity, and species abundance distribution) the third predictor level. We calculated the SAR as a sample-based rarefaction curve using 60 streams within landscape windows (scales) in the United States, ranging from 160,000 to 6,760,000 km² . First, we found that all models provided good fits (R²  ≥ 0.93), but the frequency of the best-fitting model was strongly dependent on organism, scale, and metacommunity properties. The Michaelis-Menten model was most common in fish, at the largest scales, and at the highest levels of intraspecific spatial aggregation. The power model was most frequent in diatoms and insects, at smaller scales, and in metacommunities with the lowest evenness. The logarithmic model fit best exclusively at the smallest scales and in species-poor metacommunities, primarily fish. Second, we tested our framework with the parameters of the most broadly used SAR model, the log-log form of the power model, using a structural equation model. This model supported our framework and revealed that the SAR slope was best predicted by scale- and organism-dependent metacommunity properties, particularly spatial aggregation, whereas the intercept responded most strongly to species group and γ-diversity. Future research should investigate from the perspective of our framework how shifts in metacommunity properties due to climate change may alter the SAR.

A triad of kicknet sampling, eDNA metabarcoding, and predictive modeling to assess richness of mayflies, stoneflies and caddisflies in rivers

Monitoring biodiversity is essential to understand the impacts of human activities and for effective management of ecosystems. Thereby, biodiversity can be assessed through direct collection of targeted organisms, through indirect evidence of their presence (e.g. signs, environmental DNA, camera trap, etc.), or through extrapolations from species distribution and species richness models. Differences in approaches used in biodiversity assessment, however, may come with individual challenges and hinder cross-study comparability. In the context of rapidly developing techniques, we compared three different approaches in order to better understand assessments of aquatic macroinvertebrate diversity. Specifically, we compared the community composition and species richness of three orders of aquatic macroinvertebrates (mayflies, stoneflies, and caddisflies, hereafter EPT) obtained via eDNA metabarcoding and via traditional in situ kicknet sampling to catchment-level based predictions of a species richness model. We used kicknet data from 24 sites in Switzerland and compared taxonomic lists to those obtained using eDNA amplified with two different primer sets. Richness detected by these methods was compared to the independent predictions made by a statistical species richness model, that is, a generalized linear model using landscape-level features to estimate EPT diversity. Despite the ability of eDNA to consistently detect some EPT species found by traditional sampling, we found important discrepancies in community composition between the kicknet and eDNA approaches, particularly at a local scale. We found the EPT-specific primer set fwhF2/EPTDr2n, detected a greater number of targeted EPT species compared to the more general primer set mlCOIintF/HCO2198. Moreover, we found that the species richness measured by eDNA from either primer set was poorly correlated to the richness measured by kicknet sampling (Pearson correlation = 0.27) and that the richness estimated by eDNA and kicknet were poorly correlated with the prediction of the species richness model (Pearson correlation = 0.30 and 0.44, respectively). The weak relationships between the traditional kicknet sampling and eDNA with this model indicates inherent limitations in upscaling species richness estimates, and possibly a limited ability of the model to meet real world expectations. It is also possible that the number of replicates was not sufficient to detect ambiguous correlations. Future challenges include improving the accuracy and sensitivity of each approach individually, yet also acknowledging their respective limitations, in order to best meet stakeholder demands and address the biodiversity crisis we are facing.

Impact of marine protected areas on temporal stability of fish species diversity

Preserving biodiversity over time is a pressing challenge for conservation science. A key goal of marine protected areas (MPAs) is to maintain stability in species composition, via reduced turnover, to support ecosystem function. Yet, this stability is rarely measured directly under different levels of protection. Rather, evaluations of MPA efficacy generally consist of static measures of abundance, species richness, and biomass, and rare measures of turnover are limited to short-term studies involving pairwise (beta diversity) comparisons. Zeta diversity is a recently developed metric of turnover that allows for measurement of compositional similarity across multiple assemblages and thus provides more comprehensive estimates of turnover. We evaluated the effectiveness of MPAs at preserving fish zeta diversity across a network of marine reserves over 10 years in Batemans Marine Park, Australia. Snorkel transect surveys were conducted across multiple replicated and spatially interspersed sites to record fish species occurrence through time. Protection provided by MPAs conferred greater stability in fish species turnover. Marine protected areas had significantly shallower decline in zeta diversity compared with partially protected and unprotected areas. The retention of harvested species was four to six times greater in MPAs compared with partially protected and unprotected areas, and the stabilizing effects of protection were observable within 4 years of park implementation. Conversely, partial protection offered little to no improvement in stability, compared with unprotected areas. These findings support the efficacy of MPAs for preserving temporal fish diversity stability. The implementation of MPAs helps stabilize fish diversity and may, therefore, support biodiversity resilience under ongoing environmental change.

The number of tree species on Earth

One of the most fundamental questions in ecology is how many species inhabit the Earth. However, due to massive logistical and financial challenges and taxonomic difficulties connected to the species concept definition, the global numbers of species, including those of important and well-studied life forms such as trees, still remain largely unknown. Here, based on global ground-sourced data, we estimate the total tree species richness at global, continental, and biome levels. Our results indicate that there are ∼73,000 tree species globally, among which ∼9,000 tree species are yet to be discovered. Roughly 40% of undiscovered tree species are in South America. Moreover, almost one-third of all tree species to be discovered may be rare, with very low populations and limited spatial distribution (likely in remote tropical lowlands and mountains). These findings highlight the vulnerability of global forest biodiversity to anthropogenic changes in land use and climate, which disproportionately threaten rare species and thus, global tree richness.

Addressing context dependence in ecology

Context dependence is widely invoked to explain disparate results in ecology. It arises when the magnitude or sign of a relationship varies due to the conditions under which it is observed. Such variation, especially when unexplained, can lead to spurious or seemingly contradictory conclusions, which can limit understanding and our ability to transfer findings across studies, space, and time. Using examples from biological invasions, we identify two types of context dependence resulting from four sources: mechanistic context dependence arises from interaction effects; and apparent context dependence can arise from the presence of confounding factors, problems of statistical inference, and methodological differences among studies. Addressing context dependence is a critical challenge in ecology, essential for increased understanding and prediction.

Abiotic factors influence species co-occurrence patterns of lake fishes

Abiotic factors are recognized for their strong influence on community structure. Habitat diversity is related to resource availability that influences species richness and abundance. In lakes, surface area and depth have been used as measures of the size and diversity of habitat, and have strong effects on the structure of entire communities. We tested whether abiotic variables, related to habitat size, influence co-occurrence patterns of species pairs of fishes by analysing groups of lakes within a specific area and depth categories in two regions in Ontario, Canada. We used null models to obtain co-occurrence patterns and standard effect sizes for each species pair within each area and depth category. We estimated standard effect sizes relative to lake area or depth and determined whether species co-occurrence patterns change systematically as these measures of habitat increase. We evaluated groups of species where factors such as predation and habitat filtering have been shown to structure those assemblages, and we tested whether area and depth alter the species associations and our interpretation of these relationships. We found significant differences between the observed and expected distributions of regression slopes relating co-occurrences to area and depth in both regions across all species, which indicated the strong influence of both variables on the overall co-occurrence patterns. We observed a significant negative trend of the co-occurrence patterns across lake area categories for the predator-prey species, indicating that the effect of predation was stronger in smaller lakes, but it was reduced in larger lakes, possibly due to increased habitat and resource availability. We show that pooling results as done in standard community null models can lead to Type II errors due to the 'cancellation' of opposing ecological signals. Our results demonstrate the effect of environmental variables on species co-occurrence patterns, but the divergent results obtained between geographical regions suggest that such patterns are context-dependent. This study emphasizes the importance of considering abiotic factors in null models of species co-occurrence to obtain reliable and detailed information about the association patterns between species.

DynaMETE: a hybrid MaxEnt-plus-mechanism theory of dynamic macroecology

The Maximum Entropy Theory of Ecology (METE) predicts the shapes of macroecological metrics in relatively static ecosystems, across spatial scales, taxonomic categories and habitats, using constraints imposed by static state variables. In disturbed ecosystems, however, with time-varying state variables, its predictions often fail. We extend macroecological theory from static to dynamic by combining the MaxEnt inference procedure with explicit mechanisms governing disturbance. In the static limit, the resulting theory, DynaMETE, reduces to METE but also predicts a new scaling relationship among static state variables. Under disturbances, expressed as shifts in demographic, ontogenic growth or migration rates, DynaMETE predicts the time trajectories of the state variables as well as the time-varying shapes of macroecological metrics such as the species abundance distribution and the distribution of metabolic rates over individuals. An iterative procedure for solving the dynamic theory is presented. Characteristic signatures of the deviation from static predictions of macroecological patterns are shown to result from different kinds of disturbance. By combining MaxEnt inference with explicit dynamical mechanisms of disturbance, DynaMETE is a candidate theory of macroecology for ecosystems responding to anthropogenic or natural disturbances.

Biased-corrected richness estimates for the Amazonian tree flora

Amazonian forests are extraordinarily diverse, but the estimated species richness is very much debated. Here, we apply an ensemble of parametric estimators and a novel technique that includes conspecific spatial aggregation to an extended database of forest plots with up-to-date taxonomy. We show that the species abundance distribution of Amazonia is best approximated by a logseries with aggregated individuals, where aggregation increases with rarity. By averaging several methods to estimate total richness, we confirm that over 15,000 tree species are expected to occur in Amazonia. We also show that using ten times the number of plots would result in an increase to just ~50% of those 15,000 estimated species. To get a more complete sample of all tree species, rigorous field campaigns may be needed but the number of trees in Amazonia will remain an estimate for years to come.

Patterns of zeta diversity in ectoparasite communities harboured by small mammals at three hierarchical scales: taxon-invariance and scale-dependence

We studied compositional turnover in communities of fleas and mites harboured by small mammals using zeta diversity metric (similarity between multiple communities) and asked whether the patterns of zeta diversity decline with an increase in the number of communities differ between taxa and hierarchical scales [infracommunities (parasite assemblages on individual hosts), component communities (parasite assemblages harboured by host populations), and compound communities (all parasite species in a locality)]. The average number of shared species declined with an increasing number of communities (zeta order). It attained zero at higher orders in infracommunities of both taxa with the shape of the zeta decline being best fitted by the negative exponential function, and the retention rate curves being modal. In contrast, zeta diversity values for compound communities of mites and fleas did not attain zero at higher zeta orders, and the form of the zeta decline was best fitted by the power-law function, whereas the retention rate curves were asymptotic. In component communities, the form of zeta decline was best fitted by either exponential or power-law function in dependence of whether communities were considered within a host across localities or across hosts within a locality and whether ubiquitous species were taken into account. Our main conclusions are that (a) the rules governing compositional turnover in parasite communities for the lowest and the highest hierarchical scales are taxon-invariant but scale-dependent and (b) species composition of infracommunities is mainly driven by stochastic assembly processed, whereas that of compound communities is mainly driven by niche-based processes.

Patterns and drivers of species richness and turnover of neo-endemic and palaeo-endemic vascular plants in a Mediterranean hotspot: the case of Crete, Greece

Background

Exploring species richness and turnover patterns and their drivers can provide new insights into underlying mechanisms shaping community assembly, with significant implications for biodiversity conservation. Here, we explored diversity patterns of non-endemic, neo-endemic and palaeo-endemic vascular plants in Crete, Greece, a Mediterranean hotspot of plant richness and endemism. We evaluated the relationship between α-diversity and environmental (bioclimatic variables, topography), and anthropogenic variables by Generalized Additive Models, after accounting for spatial autocorrelation. Then, we quantified turnover using the novel concept of zeta diversity (the number of shared species by multiple sites), a framework which allows to explore the full spectrum of compositional turnover, the contribution of rare and widespread species to observed patterns and the underlying processes shaping them. Finally, we explored the abiotic and biotic effects, i.e. how well one category of species (non-endemics, palaeo-endemics, neo-endemics) predicts the patterns of the other categories, on zeta diversity by multi-site Generalized Dissimilarity Modelling.

Results

We found a strong correlation between neo-endemic and palaeo-endemic α-diversity, with climate, topography, and human impact driving species richness. Zeta diversity analysis revealed a sharper decrease of shared palaeo-endemic species, followed by neo-endemics, and then by non-endemics with the number of sites considered to estimate compositional turnover. Perhaps, the narrow distributions of palaeo-endemics as relict species and often habitat specialists, thus persisting locally, and of neo-endemics that may have not reached yet their potential geographical range, resulted in the observed zeta diversity decline pattern. Deterministic processes controlled species turnover of rare non-endemic and neo-endemic species, while deterministic and stochastic processes contributed similarly to palaeo-endemic turnover. However, stochasticity dominates in the case of widespread species in all occasions. The environmental and anthropogenic variables were poor predictors of compositional turnover, especially of widespread species. However, the non-endemic species composition was correlated to rare palaeo-endemics and neo-endemics, highlighting the importance of biotic effects in driving turnover patterns.

Conclusions

It seems that centers of neo-endemism of vascular plants coincide with centers of palaeo-endemism in Crete, but species richness and species turnover are shaped by different drivers.

Measuring continuous compositional change using decline and decay in zeta diversity

Incidence, or compositional, matrices are generated for a broad range of research applications in biology. Zeta diversity provides a common currency and conceptual framework that links incidence-based metrics with multiple patterns of interest in biology, ecology, and biodiversity science. It quantifies the variation in species (or OTU) composition of multiple assemblages (or cases) in space or time, to capture the contribution of the full suite of narrow, intermediate, and wide-ranging species to biotic heterogeneity. Here we provide a conceptual framework for the application and interpretation of patterns of continuous change in compositional diversity using zeta diversity. This includes consideration of the survey design context, and the multiple ways in which zeta diversity decline and decay can be used to examine and test turnover in the identity of elements across space and time. We introduce the zeta ratio-based retention rate curve to quantify rates of compositional change. We illustrate these applications using 11 empirical data sets from a broad range of taxa, scales, and levels of biological organization-from DNA molecules and microbes to communities and interaction networks-including one of the original data sets used to express compositional change and distance decay in ecology. We show (1) how different sample selection schemes used during the calculation of compositional change are appropriate for different data types and questions, (2) how higher orders of zeta may in some cases better detect shifts and transitions, and (3) the relative roles of rare vs. common species in driving patterns of compositional change. By exploring the application of zeta diversity decline and decay, including the retention rate, across this broad range of contexts, we demonstrate its application for understanding continuous turnover in biological systems.

Estimating species pools for a single ecological assemblage

BACKGROUND

The species pool concept was formulated over the past several decades and has since played an important role in explaining multi-scale ecological patterns. Previous statistical methods were developed to identify species pools based on broad-scale species range maps or community similarity computed from data collected from many areas. No statistical method is available for estimating species pools for a single local community (sampling area size may be very small as ≤ 1 km²). In this study, based on limited local abundance information, we developed a simple method to estimate the area size and richness of a species pool for a local ecological community. The method involves two steps. In the first step, parameters from a truncated negative trinomial model characterizing the distributional aggregation of all species (i.e., non-random species distribution) in the local community were estimated. In the second step, we assume that the unseen species in the local community are most likely the rare species, only found in the remaining part of the species pool, and vice versa, if the remaining portion of the pool was surveyed and was contrasted with the sampled area. Therefore, we can estimate the area size of the pool, as long as an abundance threshold for defining rare species is given. Since the size of the pool is dependent on the rarity threshold, to unanimously determine the pool size, we developed an optimal method to delineate the rarity threshold based on the balance of the changing rates of species absence probabilities in the sampled and unsampled areas of the pool.

RESULTS

For a 50 ha (0.5 km²) forest plot in the Barro Colorado Island of central Panama, our model predicted that the local, if not regional, species pool for the 0.5 km² forest plot was nearly the entire island. Accordingly, tree species richness in this pool was estimated as around 360. When the sampling size was smaller, the upper bound of the 95% confidence interval could reach 418, which was very close to the flora record of tree richness for the island. A numerical test further demonstrated the power and reliability of the proposed method, as the true values of area size and species richness for the hypothetical species pool have been well covered by the 95% confidence intervals of the true values.

CONCLUSIONS

Our method fills the knowledge gap on estimating species pools for a single local ecological assemblage with little information. The method is statistically robust and independent of sampling size, as proved by both empirical and numerical tests.