Miquel De Cáceres

Associations between species and groups of sites: indices and statistical inference

Ecologists often face the task of studying the association between single species and one or several groups of sites representing habitat types, community types, or other categories. Besides characterizing the ecological preference of the species, the strength of the association usually presents a lot of interest for conservation biology, landscape mapping and management, and natural reserve design, among other applications. The indices most frequently employed to assess these relationships are the phi coefficient of association and the indicator value index (IndVal). We compare these two approaches by putting them into a broader framework of related measures, which includes several new indices. We present permutation tests to assess the statistical significance of species-site group associations and bootstrap methods for obtaining confidence intervals. Correlation measures, such as the phi coefficient, are more context-dependent than indicator values but allow focusing on the preference of the species. In contrast, the two components of an indicator value index directly assess the value of the species as a bioindicator because they can be interpreted as its positive predictive value and sensitivity. Ecologists should select the most appropriate index of association strength according to their objective and then compute confidence intervals to determine the precision of the estimate.

Beta diversity as the variance of community data: dissimilarity coefficients and partitioning

Beta diversity can be measured in different ways. Among these, the total variance of the community data table Y can be used as an estimate of beta diversity. We show how the total variance of Y can be calculated either directly or through a dissimilarity matrix obtained using any dissimilarity index deemed appropriate for pairwise comparisons of community composition data. We addressed the question of which index to use by coding 16 indices using 14 properties that are necessary for beta assessment, comparability among data sets, sampling issues and ordination. Our comparison analysis classified the coefficients under study into five types, three of which are appropriate for beta diversity assessment. Our approach links the concept of beta diversity with the analysis of community data by commonly used methods like ordination and anova. Total beta can be partitioned into Species Contributions (SCBD: degree of variation of individual species across the study area) and Local Contributions (LCBD: comparative indicators of the ecological uniqueness of the sites) to Beta Diversity. Moreover, total beta can be broken up into within- and among-group components by manova, into orthogonal axes by ordination, into spatial scales by eigenfunction analysis or among explanatory data sets by variation partitioning.

How Fire History, Fire Suppression Practices and Climate Change Affect Wildfire Regimes in Mediterranean Landscapes

Available data show that future changes in global change drivers may lead to an increasing impact of fires on terrestrial ecosystems worldwide. Yet, fire regime changes in highly humanised fire-prone regions are difficult to predict because fire effects may be heavily mediated by human activities We investigated the role of fire suppression strategies in synergy with climate change on the resulting fire regimes in Catalonia (north-eastern Spain). We used a spatially-explicit fire-succession model at the landscape level to test whether the use of different firefighting opportunities related to observed reductions in fire spread rates and effective fire sizes, and hence changes in the fire regime. We calibrated this model with data from a period with weak firefighting and later assess the potential for suppression strategies to modify fire regimes expected under different levels of climate change. When comparing simulations with observed fire statistics from an eleven-year period with firefighting strategies in place, our results showed that, at least in two of the three sub-regions analysed, the observed fire regime could not be reproduced unless taking into account the effects of fire suppression. Fire regime descriptors were highly dependent on climate change scenarios, with a general trend, under baseline scenarios without fire suppression, to large-scale increases in area burnt. Fire suppression strategies had a strong capacity to compensate for climate change effects. However, strong active fire suppression was necessary to accomplish such compensation, while more opportunistic fire suppression strategies derived from recent fire history only had a variable, but generally weak, potential for compensation of enhanced fire impacts under climate change. The concept of fire regime in the Mediterranean is probably better interpreted as a highly dynamic process in which the main determinants of fire are rapidly modified by changes in landscape, climate and socioeconomic factors such as fire suppression strategies.

Community surveys through space and time: testing the space?time interaction in the absence of replication

In order to test hypotheses about changes in the environment induced by man, including climatic change, ecologists are sampling portions of the environment repeatedly across time. This paper describes a method for testing a space-time interaction in repeated ecological survey data, when there is no replication at the level of individual sampling units (sites). This methodological development is important for the analysis of long-term monitoring data, including systems under anthropogenic influence. In these systems, an interaction may indicate that the spatial structure of community composition has changed in the course of time or that the temporal evolution is not the same at all sites. This paper describes ANOVA models corresponding to the steps leading to a solution to the problem, which is based on the representation of space and time by principal coordinates of neighbor matrices (PCNM eigenfunctions) in the ANOVA. Numerical simulations showed that ANOVA Model 5 was the model of choice for the analysis of the space-time interaction because it always had correct rates of Type I error, and its power was always equal to or higher than those of other possible models of analysis. If the hypothesis of absence of interaction is not rejected, one cannot conclude that a change has occurred in the spatial structure of the response data across time; one should follow the ordinary rules of two-way ANOVA if testing the significance of the main factors is of interest. If the hypothesis of absence of interaction is rejected, one should model the spatial structure of each time period in a separate way. One can also conduct a single test involving a separate model of the spatial structure for each time period. This paper presents two applications to real ecological data.

Using unplanned fires to help suppressing future large fires in mediterranean forests

Despite the huge resources invested in fire suppression, the impact of wildfires has considerably increased across the Mediterranean region since the second half of the 20th century. Modulating fire suppression efforts in mild weather conditions is an appealing but hotly-debated strategy to use unplanned fires and associated fuel reduction to create opportunities for suppression of large fires in future adverse weather conditions. Using a spatially-explicit fire–succession model developed for Catalonia (Spain), we assessed this opportunistic policy by using two fire suppression strategies that reproduce how firefighters in extreme weather conditions exploit previous fire scars as firefighting opportunities. We designed scenarios by combining different levels of fire suppression efficiency and climatic severity for a 50-year period (2000–2050). An opportunistic fire suppression policy induced large-scale changes in fire regimes and decreased the area burnt under extreme climate conditions, but only accounted for up to 18–22% of the area to be burnt in reference scenarios. The area suppressed in adverse years tended to increase in scenarios with increasing amounts of area burnt during years dominated by mild weather. Climate change had counterintuitive effects on opportunistic fire suppression strategies. Climate warming increased the incidence of large fires under uncontrolled conditions but also indirectly increased opportunities for enhanced fire suppression. Therefore, to shift fire suppression opportunities from adverse to mild years, we would require a disproportionately large amount of area burnt in mild years. We conclude that the strategic planning of fire suppression resources has the potential to become an important cost-effective fuel-reduction strategy at large spatial scale. We do however suggest that this strategy should probably be accompanied by other fuel-reduction treatments applied at broad scales if large-scale changes in fire regimes are to be achieved, especially in the wider context of climate change.

Beals smoothing revisited

Beals smoothing is a multivariate transformation specially designed for species presence/absence community data containing noise and/or a lot of zeros. This transformation replaces the observed values of the target species by predictions of occurrence on the basis of its co-occurrences with the remaining species. In many applications, the transformed values are used as input for multivariate analyses. As Beals smoothing values provide a sense of “probability of occurrence”, they have also been used for inference. However, this transformation can produce spurious results, and it must be used with caution. Here we study the statistical and ecological bases underlying the Beals smoothing function, and the factors that may affect the reliability of transformed values are explored using simulated data sets. Our simulations demonstrate that Beals predictions are unreliable for target species that are not related to the overall ecological structure. Furthermore, the presence of these “random” species may diminish the quality of Beals smoothing values for the remaining species. A statistical test is proposed to determine when observed values can be replaced with Beals smoothing predictions. Two real-data example applications are presented to illustrate the potentially false predictions of Beals smoothing and the necessary checking step performed by the new test.

Align or not to align? Resolving species complexes within the Caloplaca saxicola group as a case study

The Caloplaca saxicola group is the main group of saxicolous, lobed-effigurate species within genus Caloplaca (Teloschistaceae, lichen-forming Ascomycota). A recent monographic revision by the first author detected a wide range of morphological variation. To confront the phenotypically based circumscription of these taxa and to resolve their relationships morphological and ITS rDNA data were obtained for 56 individuals representing eight Caloplaca species belonging to the C. saxicola group. We tested the monophyly of these eight morphospecies by performing maximum parsimony, maximum likelihood and two different types of Bayesian analyses (with and without a priori alignments). Restricting phylogenetic analyses to unambiguously aligned portions of ITS was sufficient to resolve, with high bootstrap support, five of the eight previously recognized species within the C. saxicola group. However, phylogenetic resolution of all or most of the eight species currently included as two distinct subgroups within the C. saxicola group was possible only by combining morphological characters and signal from ambiguously aligned regions with the unambiguously aligned ITS sites or when the entire ITS1 and 2 regions were not aligned a priori and included as an integral component of a Bayesian analysis (BAli-Phy). The C. arnoldii subgroup includes C. arnoldii, comprising four subspecies, and the C. saxicola subgroup encompasses seven species. Contrary to the C. saxicola subgroup, monophyly of taxa included within the C. arnoldii subgroup and their relationships could not be resolved with combined ITS and morphological data. Unequivocal morphological synapomorphies for all species except C. arnoldii and C. pusilla are recognized and presented.

Dissimilarity measurements and the size structure of ecological communities

Summary 1. Measurements of community resemblance in ecology are often based on species composition, and the starting point for calculations is usually a site-by-species data table. However, resemblance measurements may not be sufficiently accurate when communities are described using species composition only. Characteristics such as the size of their constituting organisms are also important to understand community organization. 2. Here, we provide a framework that generalizes conventional resemblance measurements by incorporating the size structure of the compared communities. We first introduce the concept of cumulative abundance profile, which generalizes traditional species abundance values, and describe how to calculate it. We then explain our approach to compare cumulative abundance profiles in community resemblance measurements and use a small simulation study to determine which resemblance coefficients appropriately deal with compositional and structural differences. After that, we present an illustrative example where we study the structural and compositional variation between and within six Douglas-fir forest plots in British Columbia, Canada. 3. According to our investigations, the generalizations we suggest for the percentage difference (alias Bray–Curtis dissimilarity) and the Ru��cka coefficients are appropriate to measure community resemblance in terms of size structure, species composition or both. 4. Our framework allows community resemblance to be measured in terms of either size structure or species composition, or both. A broad range of applications is expected. In the case of terrestrial plant communities, potential applications include analyses of community dynamics and classification of vegetation.

A Resource-Based Modelling Framework to Assess Habitat Suitability for Steppe Birds in Semiarid Mediterranean Agricultural Systems

European agriculture is undergoing widespread changes that are likely to have profound impacts on farmland biodiversity. The development of tools that allow an assessment of the potential biodiversity effects of different land-use alternatives before changes occur is fundamental to guiding management decisions. In this study, we develop a resource-based model framework to estimate habitat suitability for target species, according to simple information on species’ key resource requirements (diet, foraging habitat and nesting site), and examine whether it can be used to link land-use and local species’ distribution. We take as a study case four steppe bird species in a lowland area of the north-eastern Iberian Peninsula. We also compare the performance of our resource-based approach to that obtained through habitat-based models relating species’ occurrence and land-cover variables. Further, we use our resource-based approach to predict the effects that change in farming systems can have on farmland bird habitat suitability and compare these predictions with those obtained using the habitat-based models. Habitat suitability estimates generated by our resource-based models performed similarly (and better for one study species) than habitat based-models when predicting current species distribution. Moderate prediction success was achieved for three out of four species considered by resource-based models and for two of four by habitat-based models. Although, there is potential for improving the performance of resource-based models, they provide a structure for using available knowledge of the functional links between agricultural practices, provision of key resources and the response of organisms to predict potential effects of changing land-uses in a variety of context or the impacts of changes such as altered management practices that are not easily incorporated into habitat-based models.

Mediterranean fire regime effects on pine-oak forest landscape mosaics under global change in NE Spain

Abstract Afforestation after land abandonment and the occurrence of large fires have significantly altered the composition of pine-oak ecosystems in the Mediterranean since 1950s, the latter favouring the prevalence of oak forests and shrublands to that of pine forests. Nevertheless, our ability to integrate the processes driving these changes in modelling tools and to project them under future global change scenarios is scarce. This study aims at investigating how Mediterranean forest landscape composition and seral stages may be affected by mid-term changes in fire regime and climate. Taking Catalonia (NE Spain) as study area, we predicted yearly changes in forest landscape composition using the MEDFIRE model which allows assessing the effects of different fire regimes on landscape dynamics such as post-fire regeneration and afforestation. We considered three climatic treatments based on observed and projected climate, two fire regimes largely differing in the amount of area burnt and the number of large fires, and two fire suppression strategies. While projected afforestation continued to increase forest cover in the 2050 horizon, a climate-related harsher fire regime (higher amounts of area burnt) accelerated a shift towards landscapes progressively dominated by oaks and shrublands, thus precluding general forest maturation. Fire-sensitive pine species contributed to net forest cover loss in the worst scenarios. An active fire suppression strategy partially compensated the effects of a climate-related harsher fire regime on pine forest loss and rejuvenation, whereas variability in climate projections weakly affected spatial fire allocation and afforestation. Our results highlight the need to explicitly incorporate fire suppression strategies in forest landscape composition forecasts in the Mediterranean. At mid-term, large-scale afforestation, post-fire forest rejuvenation and landscape composition changes may alter forest ecosystem functioning and potentially interact with fire suppression planning.