Biology subjects

Compagnoni, A.

Publications and source records attributed to Compagnoni, A..

14 recordsLinked to original sources

bioRxiv2024

Forecasting range shifts of a dioecious plant species under climate change

Global warming has triggered an urgent need for predicting the reorganization of Earths biodiversity. Currently, the vast majority of models used to forecast population viability and range shifts in response to climate change ignore the complication of sex structure, and thus the potential for females and males to differ in their sensitivity to climate drivers. We developed demographic models of range limitation, parameterized from geographically distributed common garden experiments, with females and males of a dioecious grass species (Poa arachnifera) throughout and beyond its range in the south-central U.S. Female-dominant and two-sex model versions both predict that climate change will alter population viability and will induce a poleward niche shift beyond current northern limits. However, the magnitude of niche shift was underestimated by the female-dominant model, because females have broader temperature tolerance than males and become mate-limited under female-biased sex ratios. Our result illustrate how explicit accounting for both sexes could enhance population viability forecasts and conservation planning for dioecious species in response to climate change.

ecology↗

bioRxiv2024

Population responses to environmental stochasticity are primarily driven by survival-reproduction trade-offs and mediated by aridity

Forecasting responses of natural populations to increasingly stochastic environments is a major challenge in Ecology and Conservation Biology. We now know that populations can modulate how their vital rates (e.g., survival, reproduction) change through time to minimise the negative impacts of environmental stochasticity. However, despite the important analytical and theoretical advances that have led to this knowledge, we still do not know (1) how much this ability of natural populations to buffer against environmental stochasticity can vary in nature, nor (2) the drivers of these strategies, with likely candidates including the environmental regimes themselves, as well as the life history traits and phylogenetic ancestry of the species of interest. To address these questions, we parameterised a Bayesian generalised linear mixed model with high-resolution vital rate data from 134 natural populations across 89 species of plants and animals. We show that population responses to environmental stochasticity vary three orders of magnitude along a demographic buffering continuum. Furthermore, the position of a given population along said continuum is predicted by a survival-reproduction trade-off and by the degree of aridity the population experiences. Our findings open a promising avenue of research to improve ecological forecasts and management of natural populations in the Anthropocene.

ecology↗

bioRxiv2023

Structured demographic buffering: A framework to explore the environment drivers and demographic mechanisms underlying demographic buffering

Environmental stochasticity is a key determinant of population viability. Decades of work exploring how environmental stochasticity influences population dynamics have highlighted the ability of some natural populations to limit the negative effects of environmental stochasticity, one of these strategies being demographic buffering. Whilst various methods exist to quantify demographic buffering, we still do not know which environment factors and demographic characteristics are most responsible for the demographic buffering observed in natural populations. Here, we introduce a framework to quantify the relative effects of three key drivers of demographic buffering: environment components (e.g., temporal autocorrelation and variance), population structure, and demographic rates (e.g., progression and fertility). Using Integral Projection Models, we explore how these drivers impact the demographic buffering abilities of three plant species with different life histories and demonstrate how our approach successfully characterises a populations capacity to demographically buffer against environmental stochasticity in a changing world.

ecology↗

bioRxiv2023

A standard protocol to report discrete stage-structured demographic information

O_LIStage-based demographic methods, such as matrix population models (MPMs), are powerful tools used to address a broad range of fundamental questions in ecology, evolutionary biology, and conservation science. Accordingly, MPMs now exist for over 3,000 species worldwide. These data are being digitised as an ongoing process and periodically released into two large open-access online repositories: the COMPADRE Plant Matrix Database and the COMADRE Animal Matrix Database. During the last decade, data archiving and curation of COMPADRE and COMADRE, and subsequent comparative research, have revealed pronounced variation in how MPMs are parameterized and reported. C_LIO_LIHere, we summarise current issues related to the parameterisation and reporting of MPMs that arise most frequently and outline how they affect MPM construction, analysis, and interpretation. To quantify variation in how MPMs are reported, we present results from a survey identifying key aspects of MPMs that are frequently unreported in manuscripts. We then screen COMPADRE and COMADRE to quantify how often key pieces of information are omitted from manuscripts using MPMs. C_LIO_LIOver 80% of surveyed researchers (n=60) state a clear benefit to adopting more standardised methodologies for reporting MPMs. Furthermore, over 85% of the 300 MPMs assessed from COMPADRE and COMADRE omitted one or more elements that are key to their accurate interpretation. Based on these insights, we identify fundamental issues that can arise from MPM construction and communication and provide suggestions to improve clarity, reproducibility, and future research utilising MPMs and their required metadata. To fortify reproducibility and empower researchers to take full advantage of their demographic data, we introduce a standardized protocol to present MPMs in publications. This standard is linked to www.compadre-db.org, so that authors wishing to archive their MPMs can do so prior to submission of publications, following examples from other open-access repositories such as DRYAD, Figshare, and Zenodo. C_LIO_LICombining and standardising MPMs parameterized from populations around the globe and across the tree of life opens up powerful research opportunities in evolutionary biology, ecology, and conservation research. However, this potential can only be fully realised by adopting standardised methods to ensure reproducibility. C_LI

ecology↗

bioRxiv2022

Diversity-stability relationships become decoupled across spatial scales: a synthesis of organism and ecosystem types

The relationship between biodiversity and stability, or its inverse, temporal variability, is multidimensional and complex. Temporal variability in aggregate properties, like total biomass or abundance, is typically lower in communities with higher species diversity (i.e., the diversity-stability relationship or DSR). Recent work has shown that, at broader spatial extents, regional-scale aggregate variability is also lower with higher regional diversity (in plant systems) and with lower spatial synchrony. However, it is not yet clear whether regional DSRs hold across a broad range of organisms and ecosystem types. Furthermore, focusing exclusively on aggregate properties of communities may overlook potentially destabilizing compositional shifts. To test these questions, we compiled a large collection of long-term spatial metacommunity data spanning a wide range of taxonomic groups (e.g., birds, fish, plants, invertebrates) and ecosystem types (e.g., deserts, forests, oceans). We applied a newly developed quantitative framework for jointly analyzing aggregate and compositional variability across scales. We quantified DSRs for composition and total abundance in local communities and metacommunities. At the local scale, compositional DSRs suggested that higher local () diversity was associated with lower variability in animal populations but higher variability in plant populations, while aggregate DSRs supported the classic stabilizing effects of diversity. Spatial synchrony differed among taxa (birds had the lowest, plants the highest), suggesting differences in stabilization by spatial processes. Spatial synchrony declined with higher diversity among sites ({beta}) for both compositional and aggregate properties. However, at the regional ({gamma}) scale, we found no aggregate DSR, but a positive compositional DSR. Across a broader range of taxa, our results suggest that high {gamma}-diversity does not consistently stabilize aggregate properties at regional scales without sufficient spatial {beta}-diversity to reduce spatial synchrony. Open research statementAll data sets are accessible via the Environmental Data Initiative, and a specific data package of the data sets used in this analysis will be made publicly available (doi: pending). Citations to original sources are included in Appendix S1. Code to reproduce the analyses is found in a Zenodo archive (doi: pending) of the GitHub repository for this project (https://github.com/sokole/ltermetacommunities/tree/master/Manuscripts/MS3).

ecology↗

bioRxiv2022

Spatial replication should be prioritized to advance our understanding of population responses to climate

Understanding the responses of plant populations dynamics to climatic variability is frustrated by the need for long-term datasets. Here, we advocate for new studies that estimate the effects of climate by sampling replicate populations in locations with similar climate. We first use data analysis on spatial locations in the conterminous USA to assess how far apart spatial replicates should be from each other to minimize temporal correlations in climate. We find that on average spatial locations separated by 316 Km (SD = 149Km) have moderate (0.5) correlations in annual precipitation. Second, we use simulations to demonstrate that spatial replication can lead to substantial gains in the range of climates sampled during a given set of years so long as the climate correlations between the populations are at low to moderate levels. Third, we use simulations to quantify how many spatial replicates would be necessary to achieve the same statistical power of a single-population, long-term data set under different strengths and directions of spatial correlations in climate between spatial replicates. Our results indicate that spatial replication is an untapped opportunity to study the effects of climate on demography and to rapidly fill important knowledge gaps in the field of population ecology.

ecology↗

bioRxiv2022

Antecedent effect models as an exploratory rather than predictive tool to understand the response of plant populations to climate drivers

Understanding mechanisms and predicting natural population responses to climate is a key goal of Ecology. However, studies explicitly linking climate to population dynamics remain limited. Antecedent effect models are a set of statistical tools that capitalize on the evidence provided by climate and population data to select time windows correlated with a response (e.g., survival, reproduction). Thus, these models can serve as both a predictive and exploratory tool. We compare the predictive performance of antecedent effect models against simpler models, and showcase their exploratory analysis potential by selecting a case study with high predictive power. We fit three antecedent effect models: (1) weighted mean models (WMM), which weigh the importance of monthly anomalies based on a Gaussian curve, (2) stochastic antecedent models (SAM), which weigh the importance of monthly anomalies using a Dirichlet process, and (3) regularized regressions using the Finnish Horseshoe prior (FHM), which estimate a separate effect size for each monthly anomaly. We compare these approaches to a linear model using a yearly climatic predictor and a null model with no predictors. We use demographic data from 77 natural populations of 34 plant species ranging between seven and 36 years of length. We then fit models to the asymptotic population growth rate ({lambda}) and its underlying vital rates: survival, development, and reproduction. We find that models including climate do not consistently outperform null models. We hypothesize that the effect of yearly climate is too complex, weak, and confounded by other factors to be easily predicted using monthly precipitation and temperature data. On the other hand, in our case study, antecedent effect models show biologically sensible correlations between two precipitation anomalies and multiple vital rates. We conclude that, in temporal datasets with limited sample sizes, antecedent effect models are better suited as exploratory tools for hypothesis generation. Open Research statementData and code to reproduce the analyses are available on zenodo at https://dx.doi.org/10.5281/zenodo.7839199.

ecology↗

bioRxiv2022

Rpadrino: an R package to access and use PADRINO, an open access database of Integral Projection Models

O_LIDiscrete time structured population projection models are an important tool for studying population dynamics. Within this field, Integral Projection Models (IPMs) have become a popular method for studying populations structured by continuously distributed traits (e.g. height, weight). Databases of discrete time, discrete state structured population models, for example DATLife (life tables) and COMPADRE & COMADRE (matrix population models), have made quantitative syntheses straightforward to implement. These efforts allow researchers to address questions in both basic and applied ecology and evolutionary biology. There are now over 300 peer-reviewed publications containing IPMs. We describe a novel framework to quickly reconstruct these models for subsequent analyses using Rpadrino R package, which serves as an interface to PADRINO, a new database of structured population models. C_LIO_LIWe introduce an R package, Rpadrino, which enables users to download, subset, reconstruct, and extend published IPMs. Rpadrino makes use of recently created software, ipmr, to provide an engine to reconstruct a wide array of IPMs from their symbolic representations and conduct subsequent analyses. Rpadrino and ipmr are extensively documented to help users learn their usage. C_LIO_LIRpadrino currently enables users to reconstruct 280 IPMs from 40 publications that describe the demography of 14 animal and 26 plant species. All of these IPMs are tested to ensure they reproduce published estimates. Rpadrino provides an interface to augment PADRINO with external data and modify parameter values, creating a platform to extend models beyond their original purpose while retaining full reproducibility. C_LIO_LIPADRINO and Rpadrino provide a toolbox for asking new questions and conducting syntheses with peer-reviewed published IPMs. Rpadrino provides a user-friendly interface so researchers do not need to worry about the database structure or syntax, and can focus on their research questions and analyses. Additionally, Rpadrino is thoroughly documented, and provides numerous examples of how to perform analyses which are not included in the packages functionality. C_LI

ecology↗

bioRxiv2021

ipmr: Flexibly implement Integral Projection Models in R

O_LIIntegral projection models (IPMs) are an important tool for studying the dynamics of populations structured by one or more continuous traits (e.g. size, height, color). Researchers use IPMs to investigate questions ranging from linking drivers to plant population dynamics, planning conservation and management strategies, and quantifying selective pressures in natural populations. The popularity of stage-structured population models has been supported by R scripts and packages (e.g. IPMpack, popbio, popdemo, lefko3) aimed at ecologists, which have introduced a broad repertoire of functionality and outputs. However, pressing ecological, evolutionary, and conservation biology topics require developing more complex IPMs, and considerably more expertise to implement them. Here, we introduce ipmr, a flexible R package for building, analyzing, and interpreting IPMs. C_LIO_LIThe ipmr framework relies on the mathematical notation of the models to express them in code format. Additionally, this package decouples the model parameterization step from the model implementation step. The latter point substantially increases ipmrs flexibility to model complex life cycles and demographic processes. C_LIO_LIipmr can handle a wide variety of models, including density dependence, discretely and continuously varying stochastic environments, and multiple continuous and/or discrete traits. ipmr can accommodate models with individuals cross-classified by age and size. Furthermore, the package provides methods for demographic analyses (e.g. asymptotic and stochastic growth rates) and visualization (e.g. kernel plotting). C_LIO_LIipmr is a flexible R package for integral projection models. The package substantially reduces the amount of time required to implement general IPMs. We also provide extensive documentation with six vignettes and help files, accessible from an R session and online. C_LI

ecology↗

bioRxiv2021

The dual nature of metacommunity variability

There is increasing interest in measuring ecological stability to understand how communities and ecosystems respond to broad-scale global changes. One of the most common approaches is to quantify the variation through time in community or ecosystem aggregate attributes (e.g., total biomass), referred to as aggregate variability. It is now widely recognized that aggregate variability represents only one aspect of communities and ecosystems, and compositional variability, the changes in the relative frequency of species in an assemblage, is equally important. Recent contributions have also begun to explore ecological stability at regional spatial scales, where interconnected local communities form metacommunities, a key concept in managing complex landscapes. However, the conceptual frameworks and measures of ecological stability in space have only focused on aggregate variability, leaving a conceptual gap. Here, we address this gap with a novel framework for quantifying the aggregate and compositional variability of communities and ecosystems through space and time. We demonstrate that the compositional variability of a metacommunity depends on the degree of spatial synchrony in compositional trajectories among local communities. We then provide a conceptual framework in which compositional variability of (i) the metacommunity through time and (ii) among local communities combine into four archetype scenarios: spatial stasis (low/low); spatial synchrony (high/low); spatial asynchrony (high/high) and spatial compensation (low/high). We illustrate this framework based on numerical examples and a case study of a macroalgal metacommunity in which low spatial synchrony reduced variability in aggregate biomass at the metacommunity scale, while masking high spatial synchrony in compositional trajectories among local communities. Finally, we discuss the role of dispersal, environmental heterogeneity, species interactions and suggest future avenues. We believe this framework will be helpful for considering both aspects of variability simultaneously which is important to better understand ecological stability in natural and complex landscapes in response to environmental changes.

ecology↗

bioRxiv2020

Increasing temperature threatens an already endangered coastal dune plant

Climate change has the potential to reduce the abundance and distribution of species and threaten global biodiversity, but it is typically not listed as a threat in classifying species conservation status. This likely occurs because demonstrating climate change as a threat requires data-intensive demographic information. Moreover, the threat from climate change is often studied in specific biomes, such as polar or arid ones. Other biomes, such as coastal ones, have received little attention, despite being currently exposed to substantial climate change effects. We forecast the effect of climate change on the demography and population size of a federally endangered coastal dune plant (Lupinus tidestromii). We use data from a 14-year demographic study across seven extant populations of this endangered plant. Using model selection, we found that survival and fertility measures responded negatively to temperature anomalies. We then produced forecasts based on stochastic individual based population models that account for uncertainty in demographic outcomes. Despite large uncertainties, we predict that all populations will decline if temperatures increase by 1{degrees} Celsius. Considering the total number of individuals across all seven populations, the most likely outcome is a population decline of 90%. Moreover, we predict extinction is certain for one of our seven populations. These results demonstrate that climate change will profoundly decrease the current and future population growth rates of this plant, and its chance of persistence. Thus, our study provides the first evidence that climate change is an extinction threat for a plant species classified as endangered under the USA Endangered Species Act.

ecology↗

bioRxiv2020

Short-lived plants have stronger demographic responses to climate

To mitigate and adapt to climate change, there is an urgent need to synthesize the state of our knowledge on plant responses to climate. The availability of open-access data, combined with our understanding of plant physiology and life history theory provide opportunities to examine quantitative generalizations regarding which biomes and species are most responsive to climate drivers. Here, we synthesized time series of structured population models from 165 populations from 62 plants around the globe to link plant population growth rates to precipitation and temperature drivers. We expected: (1) more pronounced demographic responses to precipitation than temperature, especially in arid biomes; (2) a higher climate sensitivity in short-lived rather than long-lived species; and (3) a stronger response to climate by species that reproduce more frequently. We found that precipitation anomalies have a nearly three-fold larger effect on{lambda} than temperature. Precipitation has substantially more pronounced effects in more arid sites, but large noise makes this relationship non-significant. Species with shorter generation time have much stronger absolute responses to climate anomalies, while the degree of iteroparity does not correlate with population responses to climate. We conclude that key species-level traits can predict plant population responses to climate, and discuss the relevance of this generalization for conservation planning and evolutionary theory.

ecology↗

bioRxiv2019

Global analysis reveals complex demographic responses of mammals to climate change

Approximately 25 % of mammals are threatened globally with extinction, a risk that is amplified under climate change1. Persistence under climate change is determined by the combined effects of climatic factors on multiple demographic rates (survival, development, reproduction), and hence, on population dynamics2. Thus, to quantify which species and places on Earth are most vulnerable to climate-driven extinction, a global understanding of how demographic rates respond to climate is needed3. We synthesise information on such responses in terrestrial mammals, where extensive demographic data are available4. Given the importance of assessing the full spectrum of responses, we focus on studies that quantitatively link climate to multiple demographic rates. We identify 106 such studies, corresponding to 86 mammal species. We reveal a strong mismatch between the locations of demographic studies and the regions and taxa currently recognised as most vulnerable to climate change5,6. Moreover, we show that the effects of climate change on mammals will operate via complex demographic mechanisms: a vast majority of mammal populations display projected increases in some demographic rates but declines in others. Assessments of population viability under climate change therefore need to account for multiple demographic responses. We advocate to prioritise coordinated actions to assess mammal demography holistically for effective conservation worldwide.

ecology↗

bioRxiv2019

Testing Finch’s hypothesis: the role of organismal modularity on the escape from actuarial senescence

O_LIUntil recently, senescence was assumed to be a universal phenomenon. Evolutionary theories of senescence predict that no organism may escape the physiological decline that results in an increase in mortality risk and/or decline in fertility with age. However, evidence both in animals and plants has emerged in the last decade defying such predictions. Researchers are currently seeking mechanistic explanations for the observed variation in ageing trajectories.\nC_LIO_LIWe argue that the historical view on the inevitability of senescence is due, in part, to the development of its classical theories, which targeted primarily unitary organisms. In unitary species, the integration of resources and functions is high, and adult size is determined. In contrast, the architecture of modular organisms is indeterminate and built upon repeated modules. The isolation of mortality risk in species like hydra (Hydra spp.) or creosote brush (Larrea tridentata) may explain their null or even negative senescence.\nC_LIO_LICaleb Finch hypothesised three decades ago that species with the ability to compartmentalise risk may escape senescence. Here, we first review the evidence on organisms that slow down or even avoid senescence in the context of their architecture, along a continuum of unitarity-modularity. Then, we use open-access databases to comparatively analyse various moments of senescence and link longevity to the degree of anatomic modularity. Our analysis compares 138 plants and 151 animals. Our comparative analysis reveals that plant species that are more modular do indeed tend to escape from senescence more often than those that are unitary. The role of modularity in animal senescence is less clear.\nC_LIO_LIIn light of novel support for Finchs hypothesis across a large diversity of plant species, and with less conclusive findings in animals, we identify new research directions. We highlight opportunities related to age-dependent mortality factors. Other areas for further research include the role of modularity in relation to endocrine actions, and the costs of modular anatomies.\nC_LI O_QD\"The actinozooid is a living thing which knows no time of youthful vigour, no waxing to a period of adult life, no waxing to senility - it knows no age - it practically knows no natural death.\" - Wood-Jones (1912)\n\nC_QD

ecology↗