Multitrophic Higher-Order Interactions Modulate Species Persistence

AbstractEcologists increasingly recognize that interactions between two species can be affected by the density of a third species. How these higher-order interactions (HOIs) affect species persistence remains poorly understood. To explore the effect of HOIs stemming from multiple trophic layers on a plant community composition, we experimentally built a mesocosm with three plants and three pollinator species arranged in a fully nested and modified network structure. We estimated pairwise interactions among plants and between plants and pollinators, as well as HOIs initiated by a plant or a pollinator affecting plant species pairs. Using a structuralist approach, we evaluated the consequences of the statistically supported HOIs on the persistence probability of each of the three competing plant species and their combinations. HOIs substantially redistribute the strength and sign of pairwise interactions between plant species, promoting the opportunities for multispecies communities to persist compared with a non-HOI scenario. However, the physical elimination of a plant-pollinator link in the modified network structure promotes changes in per capita pairwise interactions and HOIs, resulting in a single-species community. Our study provides empirical evidence of the joint importance of HOIs and network structure in determining species persistence within diverse communities

Effects of intraspecific variation in a native species' phenology on its coexistence with non-native plants

Intraspecific trait variation is ubiquitous and is likely to influence species coexistence. Despite theoretical progress, empirical work on the effects of intraspecific variation on the dynamics of competing species is rare. This is because of the formidable empirical requirements necessary to link intraspecific variation in species' functional traits with intraspecific variation in the demographic and competitive rates that mediate coexistence. Here we partially overcome these challenges to determine how intraspecific variation in reproductive phenology in a native Californian annual plant species Lasthenia californica affects its ability to coexist with two non-native species Bromus madritensis and Lactuca serriola that display contrasting phenological patterns. Using data from a field experiment, we empirically parameterize a model of competitive population dynamics, accounting for the effects of intraspecific phenological trait variation on the native species' response to both intra- and interspecific competition. We find that intraspecific variation in phenology drives differences in the native species' response to competition. Moreover, simulations of the parameterized model show that this variation improves the competitive performance of the native species. This occurs because of the effects of nonlinear averaging mediated by a nonlinear, concave-up competition function that is a general feature of competition across a wide range of taxa. While intraspecific variation improves competitive performance, we also find that the magnitude of the benefit is predicted to be insufficient to prevent competitive exclusion against the non-native species with early phenogy Bromus. Against the second non-native species with later phenology Lactuca, intraspecific variation is predicted to result in coexistence where competitive exclusion would otherwise occur, but we could not rule out alternative qualitative outcomes for this interaction.12 página

Beware of trees: Pine afforestation of a naturally treeless habitat reduces flower and pollinator diversity

Planting three billion trees to capture carbon and seeking measures to reverse pollinator decline are two key pledges of the EU Biodiversity Strategy for 2030. Although planting trees could be adequate to restore biodiversity in degraded landscapes and mitigate anthropogenic carbon emissions, it can also negatively impact biodiversity of naturally treeless habitats. To explore whether these two pledges might conflict, we focused on the European dry heathland, a treeless habitat frequent in the southwestern Iberian Peninsula, where it is locally known as herriza. The herriza stands out by its high plant biodiversity and profuse flowering that supports a wide range of insect pollinators. Yet, the herriza has been heavily afforested with pine trees until the onset of the 21st century. This past activity provides a unique natural experiment to assess the effect of afforestation on flower abundance and associated pollinator diversity. We conducted a two-year field study of the diversity and abundance of flowers and pollinators in five selected sites, each consisting of two adjacent plots of open and afforested herriza. Afforested herriza plots had consistently lower diversity and abundance of flowers and insect pollinators than open herriza plots. Our results highlight the negative impact of afforestation of a treeless habitat on its flowering pattern and associated insect pollinator guilds. We thus suggest seeking alternatives to afforesting naturally treeless habitats in order to avoid conflicts between carbon sequestration and pollinator conservation

The spatial configuration of biotic interactions shapes coexistence-area relationships in an annual plant community

The increase of species richness with area is a universal phenomenon on Earth. However, this observation contrasts with our poor understanding of how these species-area relationships (SARs) emerge from the collective effects of area, spatial heterogeneity, and local interactions. By combining a structuralist approach with five years of empirical observations in a highly-diverse Mediterranean grassland, we show that spatial heterogeneity plays a little role in the accumulation of species richness with area in our system. Instead, as we increase the sampled area more species combinations are realized, and they coexist mainly due to direct pairwise interactions rather than by changes in single-species dominance or by indirect interactions. We also identify a small set of transient species with small population sizes that are consistently found across spatial scales. These findings empirically support the importance of the architecture of species interactions together with stochastic events for driving coexistence- and species-area relationships. Local patterns of species coexistence across scales could determine the shape of species-area relationships. Here the authors apply a structuralist approach to empirical data on annual plant communities to assess how species interactions shape coexistence- and species-area relationships

Plant spatial aggregation modulates the interplay between plant competition and pollinator attraction with contrasting outcomes of plant fitness

Ecosystem functions such as seed production are the result of a complex interplay between competitive plant–plant interactions and mutualistic pollinator–plant interactions. In this interplay, spatial plant aggregation could work in two different directions: it could increase hetero- and conspecific competition, thus reducing seed production; but it could also attract pollinators, increasing plant fitness. To shed light on how plant spatial arrangement modulates this balance, we conducted a field study in a Mediterranean annual grassland with three focal plant species with different phenology, Chamaemelum fuscatum (early phenology), Leontodon maroccanus (middle phenology) and Pulicaria paludosa (late phenology), and a diverse guild of pollinators (flies, bees, beetles and butterflies). All three species showed spatial aggregation of conspecific individuals. Additionally, we found that the two mechanisms were working simultaneously: crowded neighborhoods reduced individual seed production via plant–plant competition, but they also made individual plants more attractive for some pollinator guilds, increasing visitation rates and plant fitness. The balance between these two forces varied depending on the focal species and the spatial scale considered. Therefore, our results indicate that mutualistic interactions do not always effectively compensate for competitive interactions in situations of spatial aggregation of flowering plants, at least in our study system. We highlight the importance of explicitly considering the spatial structure at different spatial scales of multitrophic interactions to better understand individual plant fitness and community dynamics

Does deterministic coexistence theory matter in a finite world?

Contemporary studies of species coexistence are underpinned by deterministic models that assume that competing species have continuous (i.e., noninteger) densities, live in infinitely large landscapes, and coexist over infinite time horizons. By contrast, in nature, species are composed of discrete individuals subject to demographic stochasticity and occur in habitats of finite size where extinctions occur in finite time. One consequence of these discrepancies is that metrics of species' coexistence derived from deterministic theory may be unreliable predictors of the duration of species coexistence in nature. These coexistence metrics include invasion growth rates and niche and fitness differences, which are now commonly applied in theoretical and empirical studies of species coexistence. In this study, we tested the efficacy of deterministic coexistence metrics on the duration of species coexistence in a finite world. We introduce new theoretical and computational methods to estimate coexistence times in stochastic counterparts of classic deterministic models of competition. Importantly, we parameterized this model using experimental field data for 90 pairwise combinations of 18 species of annual plants, allowing us to derive biologically informed estimates of coexistence times for a natural system. Strikingly, we found that for species expected to deterministically coexist, community sizes containing only 10 individuals had predicted coexistence times of more than 1000 years. We also found that invasion growth rates explained 60% of the variation in intrinsic coexistence times, reinforcing their general usefulness in studies of coexistence. However, only by integrating information on both invasion growth rates and species' equilibrium population sizes could most (>99%) of the variation in species coexistence times be explained. This integration was achieved with demographically uncoupled single-species models solely determined by the invasion growth rates and equilibrium population sizes. Moreover, because of a complex relationship between niche overlap/fitness differences and equilibrium population sizes, increasing niche overlap and increasing fitness differences did not always result in decreasing coexistence times, as deterministic theory would predict. Nevertheless, our results tend to support the informed use of deterministic theory for understanding the duration of species' coexistence while highlighting the need to incorporate information on species' equilibrium population sizes in addition to invasion growth rates

Pathways to global-change effects on biodiversity: new opportunities for dynamically forecasting demography and species interactions

In structured populations, persistence under environmental change may be particularly threatened when abiotic factors simultaneously negatively affect survival and reproduction of several life cycle stages, as opposed to a single stage. Such effects can then be exacerbated when species interactions generate reciprocal feedbacks between the demographic rates of the different species. Despite the importance of such demographic feedbacks, forecasts that account for them are limited as individual-based data on interacting species are perceived to be essential for such mechanistic forecasting-but are rarely available. Here, we first review the current shortcomings in assessing demographic feedbacks in population and community dynamics. We then present an overview of advances in statistical tools that provide an opportunity to leverage population-level data on abundances of multiple species to infer stage-specific demography. Lastly, we showcase a state-of-the-art Bayesian method to infer and project stage-specific survival and reproduction for several interacting species in a Mediterranean shrub community. This case study shows that climate change threatens populations most strongly by changing the interaction effects of conspecific and heterospecific neighbours on both juvenile and adult survival. Thus, the repurposing of multi-species abundance data for mechanistic forecasting can substantially improve our understanding of emerging threats on biodiversity.12 página

Fine scale prediction of ecological community composition using a two-step sequential Machine Learning ensemble

Prediction is one of the last frontiers in ecology. Indeed, predicting fine-scale species composition in natural systems is a complex challenge as multiple abiotic and biotic processes operate simultaneously to determine local species abundances. On the one hand, species intrinsic performance and their tolerance limits to different abiotic pressures modulate species abundances. On the other hand there is growing recognition that species interactions play an equally important role in limiting or promoting such abundances within ecological communities. Here, we present a joint effort between ecologists and data scientists to use data-driven models to predict species abundances using reasonably easy to obtain data. We propose a sequential data-driven modeling approach that in a first step predicts the potential species abundances based on abiotic variables, and in a second step uses these predictions to model the realized abundances once accounting for species competition. Using a curated data set over five years we predict fine-scale species abundances in a highly diverse annual plant community. Our models show a remarkable spatial predictive accuracy using only easy-to-measure variables in the field, yet such predictive power is lost when temporal dynamics are taken into account. This result suggests that predicting future abundances requires longer time series analysis to capture enough variability. In addition, we show that these data-driven models can also suggest how to improve mechanistic models by adding missing variables that affect species performance such as particular soil conditions (e.g. carbonate availability in our case). Robust models for predicting fine-scale species composition informed by the mechanistic understanding of the underlying abiotic and biotic processes can be a pivotal tool for conservation, especially given the human-induced rapid environmental changes we are experiencing. This objective can be achieved by promoting the knowledge gained with classic modelling approaches in ecology and recently developed data-driven models. Author summary Prediction is challenging but recently developed Machine Learning techniques allow to dramatically improve prediction accuracy in several domains. However, these tools are often of little application in ecology due to the hardship of gathering information on the needed explanatory variables, which often comprise not only physical variables such as temperature or soil nutrients, but also information about the complex network of species interactions that modulate species abundances. Here we present a two-step sequential modelling framework that overcomes these constraints. We first infer potential species abundances by training models just with easily obtained abiotic variables and then use this outcome to fine-tune the prediction of the realized species abundances when taking into account the rest of the predicted species in the community. Overall, our results show a promising way forward for fine scale prediction in ecology.O.G. acknowledges support provided by the Ministerio de Ciencia, Innovacion y Universidades (RYC-2017-23666). O.G. and I.B. acknowledge financial support provided by the Secretaria de Estado de Investigacion, Desarrollo e Innovacion (CGL2017-92436-EXP, SIMPLEX and RTI2018-098888-A-I00, MeDiNaS). J.G. acknowledges financial support provided by the Ministerio de Ciencia, Innovacion y Universidades (PGC2018-093854-B-I00). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

The morphometric acclimation to depth explains the long-term resilience of the seagrass Cymodocea nodosa in a shallow tidal lagoon

Cadiz Bay is a shallow mesotidal lagoon with extensive populations of the seagrass Cymodocea nodosa at intertidal and shallow subtidal elevations. This work aims to understand the mechanisms behind the resilience of this species to gradual sea level rise by studying its acclimation capacity to depth along the shallow littoral, and therefore, to gradual variations in the light environment. To address this objective, these populations have been monitored seasonally over a 10 year period, representing the longest seasonal database available in the literature for this species. The monitoring included populations at 0.4, -0.08 and -0.5 m LAT. The results show that C. nodosa has a strong seasonality for demographic and shoot dynamic properties - with longer shoots and larger growth in summer (high temperature) than in winter (low temperature), but also some losses. Moreover, shoots have different leaf morphometry depending on depth, with small and dense shoots in the intertidal areas (0.4 m) and sparse large shoots in the subtidal ones (-0.08 and 0.5 m). These differences in morphometry and shoot dynamic properties, combined with the differences in shoot density, explain the lack of differences in meadow production balance (i.e. meadow growth - meadow losses) between the intertidal (0.4 m) and the deepest population (-0.5 m), supporting the long term resilience of Cymodocea nodosa in Cadiz Bay. This study contributes to the understanding of the mechanisms behind seagrass stability and resilience, which is particularly important towards predicting the effects of climate change on these key coastal ecosystems, and also highlights the value of continuous long-term monitoring efforts to evaluate seagrass trajectories

Global disparities in surgeons’ workloads, academic engagement and rest periods: the on-calL shIft fOr geNEral SurgeonS (LIONESS) study

: The workload of general surgeons is multifaceted, encompassing not only surgical procedures but also a myriad of other responsibilities. From April to May 2023, we conducted a CHERRIES-compliant internet-based survey analyzing clinical practice, academic engagement, and post-on-call rest. The questionnaire featured six sections with 35 questions. Statistical analysis used Chi-square tests, ANOVA, and logistic regression (SPSS® v. 28). The survey received a total of 1.046 responses (65.4%). Over 78.0% of responders came from Europe, 65.1% came from a general surgery unit; 92.8% of European and 87.5% of North American respondents were involved in research, compared to 71.7% in Africa. Europe led in publishing research studies (6.6 ± 8.6 yearly). Teaching involvement was high in North America (100%) and Africa (91.7%). Surgeons reported an average of 6.7 ± 4.9 on-call shifts per month, with European and North American surgeons experiencing 6.5 ± 4.9 and 7.8 ± 4.1 on-calls monthly, respectively. African surgeons had the highest on-call frequency (8.7 ± 6.1). Post-on-call, only 35.1% of respondents received a day off. Europeans were most likely (40%) to have a day off, while African surgeons were least likely (6.7%). On the adjusted multivariable analysis HDI (Human Development Index) (aOR 1.993) hospital capacity > 400 beds (aOR 2.423), working in a specialty surgery unit (aOR 2.087), and making the on-call in-house (aOR 5.446), significantly predicted the likelihood of having a day off after an on-call shift. Our study revealed critical insights into the disparities in workload, access to research, and professional opportunities for surgeons across different continents, underscored by the HDI