Prescribed moorland burning meets good practice guidelines: A monitoring case study using aerial photography in the Peak District, UK

AbstractUpland moors in the UK have been managed for centuries using rotational prescribed-burning, but in recent years there has been contentious debate over its continuing use due to varying effects on moorland ecosystem services. Prescribed-burning should only be carried out using good-practice codes, which include restrictions on the size, location and frequency of burns. Good burning practice is an indicator of management standards and habitat condition in moorland landscapes. However, there has been little attempt to assess management performance with respect to these restrictions. We investigated prescribed-burning on a case-study estate (Howden Moor) in the Peak District National Park from 1988 to 2009 using management maps and aerial photography. The annual area burned (0.9%) was far below recommendations (10%) and patches were in keeping with the target sizes specified (mean±se: 2370±70 m2). The risk of a large or escaped fire was very low, with less than 1% of fires greater than 15,000m2. However, only 28.9% of the total burnable area was burned, leaving the rest unmanaged and accumulating fuel. Future guidelines might recommend the application of prescribed-burning across the range of Calluna vulgaris growth phases, to reduce fuel load and promote biodiversity at the landscape scale. We show that vegetation mapping and aerial photography are an effective method for monitoring prescribed-burning practice on moorlands. The information derived from such monitoring studies should lead to greater confidence in the standard of prescribed-burning and adherence to good-practice guidelines and requirements imposed by statutory authorities

Assessing the effect of sample bias correction in species distribution models

1. Open-source biodiversity databases contain a large number of species occurrence records but are often spatially biased; which affects the reliability of species distribution models based on these records. Sample bias correction techniques require data filtering which comes at the cost of record numbers, or require considerable additional sampling effort. Since independent data is rarely available, assessment of the correction technique often relies solely on performance metrics computed using subsets of the available – biased – data, which may prove misleading. 2. Here, we assess the extent to which an acknowledged sample bias correction technique is likely to improve models’ ability to predict species distributions in the absence of independent data. We assessed variation in model predictions induced by the aforementioned correction and model stochasticity; the variability between model replicates related to a random component (pseudo-absences sets and cross-validation subsets). We present, then, an index of the effect of correction relative to model stochasticity; the Relative Overlap Index (ROI). We investigated whether the ROI better represented the effect of correction than classic performance metrics (Boyce index, cAUC, AUC and TSS) and absolute overlap metrics (Schoener’s D, Pearson’s and Spearman’s correlation coefficients) when considering data related to 64 vertebrate species and 21 virtual species with a generated sample bias. 3. When based on absolute overlaps and cross-validation performance metrics, we found that correction produced no significant effects. When considering its effect relative to model stochasticity, the effect of correction was strong for most species at one of the three sites. The use of virtual species enabled us to verify that the correction technique improved both distribution predictions and the biological relevance of the selected variables at the specific site, when these were not correlated with sample bias patterns. 4. In the absence of additional independent data, the assessment of sample bias correction based on subsample data may be misleading. We propose to investigate both the biological relevance of environmental variables selected, and, the effect of sample bias correction based on its effect relative to model stochasticity. Accessibility maps Cross-validation Performance metrics Overlap Pseudo-absence selection Terrestrial vertebrates Variable selection Virtual speciespublishedVersio

Assembly of functional diversity in an oceanic island flora

Oceanic island floras are well known for their morphological peculiarities and exhibit striking examples of trait evolution1–3. These morphological shifts are commonly attributed to insularity and are thought to be shaped by the biogeographical processes and evolutionary histories of oceanic islands2,4. However, the mechanisms through which biogeography and evolution have shaped the distribution and diversity of plant functional traits remain unclear5. Here we describe the functional trait space of the native flora of an oceanic island (Tenerife, Canary Islands, Spain) using extensive field and laboratory measurements, and relate it to global trade-offs in ecological strategies. We find that the island trait space exhibits a remarkable functional richness but that most plants are concentrated around a functional hotspot dominated by shrubs with a conservative life-history strategy. By dividing the island flora into species groups associated with distinct biogeographical distributions and diversification histories, our results also suggest that colonization via long-distance dispersal and the interplay between inter-island dispersal and archipelago-level speciation processes drive functional divergence and trait space expansion. Contrary to our expectations, speciation via cladogenesis has led to functional convergence, and therefore only contributes marginally to functional diversity by densely packing trait space around shrubs. By combining biogeography, ecology and evolution, our approach opens new avenues for trait-based insights into how dispersal, speciation and persistence shape the assembly of entire native island floras.Fil: Barajas Barbosa, Martha Paola. Martin-luther-Universität Halle-Wittenberg; Alemania. German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig; Alemania. Universität Göttingen; AlemaniaFil: Craven, Dylan. Data Observatory Foundation; Chile. Universidad Mayor; ChileFil: Weigelt, Patrick. Universität Göttingen; AlemaniaFil: Denelle, Pierre. Universität Göttingen; AlemaniaFil: Otto, Rüdiger. Universidad de La Laguna; EspañaFil: Díaz, Sandra Myrna. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; ArgentinaFil: Price, Jonathan. University Of Hawaii At Hilo; Estados UnidosFil: Fernández Palacios, José María. Universidad de La Laguna; EspañaFil: Kreft, Holger. Universität Göttingen; Alemani

The ecological causes of functional distinctiveness in communities

Recent work has shown that evaluating functional trait distinctiveness, the average trait distance of a species to other species in a community offers promising insights into biodiversity dynamics and ecosystem functioning. However, the ecological mechanisms underlying the emergence and persistence of functionally distinct species are poorly understood. Here, we address the issue by considering a heterogeneous fitness landscape whereby functional dimensions encompass peaks representing trait combinations yielding positive population growth rates in a community. We identify four ecological cases contributing to the emergence and persistence of functionally distinct species. First, environmental heterogeneity or alternative phenotypic designs can drive positive population growth of functionally distinct species. Second, sink populations with negative population growth can deviate from local fitness peaks and be functionally distinct. Third, species found at the margin of the fitness landscape can persist but be functionally distinct. Fourth, biotic interactions (positive or negative) can dynamically alter the fitness landscape. We offer examples of these four cases and guidelines to distinguish between them. In addition to these deterministic processes, we explore how stochastic dispersal limitation can yield functional distinctiveness. Our framework offers a novel perspective on the relationship between fitness landscape heterogeneity and the functional composition of ecological assemblages

Nature and signature of the optimality in community assembly

Une combinaison phénotypique associée à une valeur maximale de taux de croissance démographique au niveau local définit une optimalité fonctionnelle locale. L’objectif de cette thèse est de comprendre le lien entre cette optimalité, les abondances et la coexistence des espèces au sein d’une communauté, à partir de données observées et d’approches de modélisation. Nous montrons en premier lieu que la moyenne fonctionnelle locale, pondérée par les abondances relatives des espèces, dépend de la distribution fonctionnelle régionale et dévie de l’optimalité fonctionnelle le long de gradients environnementaux, entrainant des biais possibles d’interprétation. Pour éviter de tels biais, nous proposons une approche d’inférence évaluant explicitement les paramètres du filtre environnemental avec un modèle mécaniste, et l’appliquons pour évaluer l’assemblage de communautés végétales le long d’une succession écologique. Nous étudions ensuite la signature de l’optimalité fonctionnelle à différentes échelles spatiales, à travers la structure de réseaux bipartis de communautés et d’espèces. La cohérence émergente des assemblages au sein du réseau permet de caractériser des ensembles fonctionnels, comme cela est illustré pour des prairies en France métropolitaine. La distribution d’occurrences des espèces entre ensembles régionaux définit une métrique nouvelle de spécialisation écologique. Nous montrons que la distance à l’optimalité fonctionnelle locale des espèces spécialistes et généralistes est fonction de leurs capacités de compétition et de tolérance à des stress physiologiques. Les espèces généralistes sont ainsi en moyenne de meilleures compétitrices éloignées de l’optimalité locale tandis que les spécialistes sont de meilleures tolérantes au stress. Nous évaluons enfin le lien entre abondances et distance à l’optimalité sous l’influence conjointe de dynamiques stochastiques, du filtre environnemental et des interactions compétitrices, en fonction des contributions des traits fonctionnels à ces mécanismes. La thèse formalise via différents modèles d’assemblage la notion d’optimalité et caractérise la signature de l’optimalité fonctionnelle à différentes échelles spatiales. Les applications à plusieurs types de communautés d'organismes illustrent le potentiel des approches mécanistes pour mieux évaluer les processus écologiques et biogéographiques générateurs des motifs de biodiversité.A phenotypic combination linked to a maximal value of demographic rate at local scale defines a functional local optimality. The goal of this thesis is to understand the linkage between this optimality, the abundances and coexistence of species within communities, using both observational and modelling approaches. We first illustrate how community weighted means are influenced by the regional distribution of functional traits and deviates from the functional optimality along environmental gradients, leading to biases of interpretation. To avoid such biases, we propose a method to explicitly infer the parameters of the environmental filtering using a mechanistic model. We apply this method to plant communities distributed along a successional gradient with the objective to assess the community assembly parameters. We then study the signature of functional optimality across different spatial scales, through the structure of bipartite networks composed of communities and species. The emergent coherence of the assemblages within the network allows characterizing functional pools of species. This has been illustrated using a database of French grassland communities. The distribution of species’ occurrences between regional pools defines a novel metrics of ecological specialization. We show that the distance to functional optimality of specialist and generalist species is function of their competitive and stress-tolerance abilities. Generalist species are in average better competitors distant from the local optimality regarding their competitive traits while specialist species express greater stress-tolerance. Finally, we assess the link between abundances and distance to optimality under the joint influence of stochastic dynamics, environmental filtering and competitive interactions, as a function of the contribution of functional traits to these mechanisms. Thanks to the use of various assembly models, this thesis defines the notion of optimality and assesses its functional signature across spatial scales. Applications to distinct types of communities illustrate the potential of mechanistic approaches towards a better assessment of ecological and biogeographical drivers of biodiversity patterns

Nature et signature de l'optimalité dans l'assemblage des communautés

A phenotypic combination linked to a maximal value of demographic rate at local scale defines a functional local optimality. The goal of this thesis is to understand the linkage between this optimality, the abundances and coexistence of species within communities, using both observational and modelling approaches. We first illustrate how community weighted means are influenced by the regional distribution of functional traits and deviates from the functional optimality along environmental gradients, leading to biases of interpretation. To avoid such biases, we propose a method to explicitly infer the parameters of the environmental filtering using a mechanistic model. We apply this method to plant communities distributed along a successional gradient with the objective to assess the community assembly parameters. We then study the signature of functional optimality across different spatial scales, through the structure of bipartite networks composed of communities and species. The emergent coherence of the assemblages within the network allows characterizing functional pools of species. This has been illustrated using a database of French grassland communities. The distribution of species’ occurrences between regional pools defines a novel metrics of ecological specialization. We show that the distance to functional optimality of specialist and generalist species is function of their competitive and stress-tolerance abilities. Generalist species are in average better competitors distant from the local optimality regarding their competitive traits while specialist species express greater stress-tolerance. Finally, we assess the link between abundances and distance to optimality under the joint influence of stochastic dynamics, environmental filtering and competitive interactions, as a function of the contribution of functional traits to these mechanisms. Thanks to the use of various assembly models, this thesis defines the notion of optimality and assesses its functional signature across spatial scales. Applications to distinct types of communities illustrate the potential of mechanistic approaches towards a better assessment of ecological and biogeographical drivers of biodiversity patterns.Une combinaison phénotypique associée à une valeur maximale de taux de croissance démographique au niveau local définit une optimalité fonctionnelle locale. L’objectif de cette thèse est de comprendre le lien entre cette optimalité, les abondances et la coexistence des espèces au sein d’une communauté, à partir de données observées et d’approches de modélisation. Nous montrons en premier lieu que la moyenne fonctionnelle locale, pondérée par les abondances relatives des espèces, dépend de la distribution fonctionnelle régionale et dévie de l’optimalité fonctionnelle le long de gradients environnementaux, entrainant des biais possibles d’interprétation. Pour éviter de tels biais, nous proposons une approche d’inférence évaluant explicitement les paramètres du filtre environnemental avec un modèle mécaniste, et l’appliquons pour évaluer l’assemblage de communautés végétales le long d’une succession écologique. Nous étudions ensuite la signature de l’optimalité fonctionnelle à différentes échelles spatiales, à travers la structure de réseaux bipartis de communautés et d’espèces. La cohérence émergente des assemblages au sein du réseau permet de caractériser des ensembles fonctionnels, comme cela est illustré pour des prairies en France métropolitaine. La distribution d’occurrences des espèces entre ensembles régionaux définit une métrique nouvelle de spécialisation écologique. Nous montrons que la distance à l’optimalité fonctionnelle locale des espèces spécialistes et généralistes est fonction de leurs capacités de compétition et de tolérance à des stress physiologiques. Les espèces généralistes sont ainsi en moyenne de meilleures compétitrices éloignées de l’optimalité locale tandis que les spécialistes sont de meilleures tolérantes au stress. Nous évaluons enfin le lien entre abondances et distance à l’optimalité sous l’influence conjointe de dynamiques stochastiques, du filtre environnemental et des interactions compétitrices, en fonction des contributions des traits fonctionnels à ces mécanismes. La thèse formalise via différents modèles d’assemblage la notion d’optimalité et caractérise la signature de l’optimalité fonctionnelle à différentes échelles spatiales. Les applications à plusieurs types de communautés d'organismes illustrent le potentiel des approches mécanistes pour mieux évaluer les processus écologiques et biogéographiques générateurs des motifs de biodiversité

bioRgeo: Bioregionalisation Methods in R

International audienc

Appendix S2, manuscript: How to distinguish the signatures of environmental filtering and trait range limits in trait-gradient analyses?

R code to simulate and analyze communities. This example is divided into two parts. The first part simulates communities using the R package ecolottery (F. Munoz, et al. ecolottery, (2017), GitHub repository, https://cran.r-project.org/web/packages/ecolottery/index.html). In the example of the paper, the parameters used to build communities are the following: species pool with 100.000 individuals belonging to 100 species with equal abundances (i.e., 1000 individuals each), and species trait values drawn from a uniform distribution between a=0 and b=1. Each community includes 500 individuals and the immigrants establishing in communities are drawn from the species pool. Stabilizing environmental filtering determines establishment probability of immigrants depending on the departure of their trait value t from a local optimum topt. We thus choose a Gaussian filtering function of mean topt, which varies among communities, and standard deviation σopt equal to 0.25. The data frame of simulated communities is called simulation. The community-weighted mean (CWM) and variance (CWV) are calculated for each community. The second section estimates the two parameters topt and σopt in each community, by comparing observed summary statistics of the community to summary statistics simulated over a broad range of topt and σopt values, with approximate Bayesian computation (ABC) analysis (coalesc_abc function; Munoz et al., in press)