Roberto Molowny-Horas’s research while affiliated with Centre for Research on Ecology and Forestry Applications and other places

Engaging ecological resource users in intervention to protect resources is challenging for governments due to the self-interest of users and uncertainty about intervention consequences. Focusing on a case of forest insect infestations, we addressed questions of resource protection and environmentally responsible behavior promotion with a conceptual model. We coupled a forest infestation model with a social model in which a governing agent applies a mechanism for the recognition and promotion of environmentally responsible behavior among several user agents. We ran the coupled model in various scenarios with a reinforcement-learning algorithm for the governing agent as well as best-case, worst-case, and random baselines. Results showed that a proper recognition policy facilitates emergence of environmentally responsible behavior. However, ecosystem health may deteriorate due to temporal differences between the social and ecological systems. Our work shows it is possible to gain insight about complexities of social–ecological systems with conceptual models through scenario analysis.

Engaging ecological resource users in intervention to protect the resource is challenging for governments due to self-interest of users and uncertainty about intervention consequences. Focusing on a case of forest insect infestations, we addressed questions of resource protection and environmentally responsible behavior promotion with a conceptual model. We coupled a forest infestation model with a social model in which a governing agent applies a mechanism for recognition and promotion of environmentally responsible behavior among several user agents. We ran the coupled model in various scenarios with a Reinforcement Learning algorithm for the governing agent as well as best-case, worst-case, and random baselines. Results showed that a proper recognition policy facilitates emergence of environmentally responsible behavior. However, ecosystem health may deteriorate due to temporal differences between the social and ecological systems. Our work shows it is possible to gain insight about complexities of social-ecological systems with conceptual models through scenario analysis.

Bee populations are exposed to multiple stressors, including land-use change, biological invasions, climate change, and pesticide exposure, that may interact synergistically. We analyze the combined effects of climate warming and sublethal insecticide exposure in the solitary bee Osmia cornuta. Previous Osmia studies show that warm wintering temperatures cause body weight loss, lipid consumption, and fat body depletion. Because the fat body plays a key role in xenobiotic detoxification, we expected that bees exposed to climate warming scenarios would be more sensitive to pesticides. We exposed O. cornuta females to three wintering treatments: current scenario (2007-2012 temperatures), near-future (2021-2050 projected temperatures), and distant-future (2051-2080). Upon emergence in spring, bees were orally exposed to three sublethal doses of an insecticide (Closer, a.i. sulfoxaflor; 0, 4.55 and 11.64 ng a.i./bee). We measured the combined effects of wintering and insecticide exposure on phototactic response, syrup consumption, and longevity. Wintering treatment by itself did not affect winter mortality, but body weight loss increased with increasing wintering temperatures. Similarly, wintering treatment by itself hardly influenced phototactic response or syrup consumption. However, bees wintered at the warmest temperatures had shorter longevity, a strong fecundity predictor in Osmia. Insecticide exposure, especially at the high dose, impaired the ability of bees to respond to light, and resulted in reduced syrup consumption and longevity. The combination of the warmest winter and the high insecticide dose resulted in a 70% longevity decrease. Smaller bees, resulting from smaller pollen-nectar provisions, had shorter longevity suggesting nutritional stress may further compromise fecundity in O. cornuta. Our results show a synergistic interaction between two major drivers of bee declines, and indicate that bees will become more sensitive to pesticides under the current global warming scenario. Our findings have important implications for pesticide regulation and underscore the need to consider multiple stressors to understand bee declines.

Mountain species are at the forefront of climate change disruption, and montane saproxylic Coleoptera are facing large‐ and small‐scale changes in their surroundings. Saproxylic Coleoptera are both functionally and taxonomically diverse and are the representatives of an imperilled fauna confronted with the realities of a changing landscape. Understanding the effects of elevation and other forest characteristics on saproxylic and non‐saproxylic Coleoptera is a step towards predicting the future of taxonomic and functional group biodiversity at the tree‐line and on mountains. The objective of this study was to examine the effect of elevation and other forest characteristics on the biodiversity of montane Coleoptera at the tree‐line using both taxonomic and functional feeding guild classifications. Our results suggest that abundance of saprotrophs is closely linked to the density of large trees rather than the volume of wood. Edge effects and elevation seem to drive abundance patterns of some species and also influence taxonomic and functional guild community patterns differently. Finally, we discuss the implications of climate change and land abandonment to future Coleoptera community structure.

Regional-level applications of dynamic vegetation models are challenging because they need to accommodate the variation in plant functional diversity, which requires moving away from broadly defined functional types. Different approaches have been adopted in the last years to incorporate a trait-based perspective into modeling exercises. A common parametrization strategy involves using trait data to represent functional variation between individuals while discarding taxonomic identity. However, this strategy ignores the phylogenetic signal of trait variation and cannot be employed when predictions for specific taxa are needed, such as in applications to inform forest management planning. An alternative strategy involves adapting the taxonomic resolution of model entities to that of the data source employed for large-scale initialization and estimating functional parameters from available plant trait databases, adopting diverse solutions for missing data and non-observable parameters. Here we report the advantages and limitations of this second strategy according to our experience in the development of MEDFATE (version 2.9.3), a novel cohort-based and trait-enabled model of forest dynamics, for its application over a region in the western Mediterranean Basin. First, 217 taxonomic entities were defined according to woody species codes of the Spanish National Forest Inventory. While forest inventory records were used to obtain some empirical parameter estimates, a large proportion of physiological, morphological, and anatomical parameters were matched to measured plant traits, with estimates extracted from multiple databases and averaged at the required taxonomic level. Estimates for non-observable key parameters were obtained using meta-modeling and calibration exercises. Missing values were addressed using imputation procedures based on trait covariation, taxonomic averages or both. The model properly simulated observed historical changes in basal area, with a performance similar to an empirical model trained for the same region. While strong efforts are still required to parameterize trait-enabled models for multiple taxa, and to incorporate intra-specific trait variability, estimation procedures such as those presented here can be progressively refined, transferred to other regions or models and iterated following data source changes by employing automated workflows. We advocate for the adoption of trait-enabled and population-structured models for regional-level projections of forest function and dynamics.

Regional-level applications of dynamic vegetation models are challenging because they need to accommodate the variation in plant functional diversity, which requires moving away from broadly-defined functional types. Different approaches have been adopted in the last years to incorporate a trait-based perspective into modeling exercises. A common parametrization strategy involves using trait data to represent functional variation between individuals while discard taxonomic identity, but this strategy ignores the phylogenetic signal of trait variation and cannot be employed when predictions for specific taxa are needed, as in applications to inform forest management planning. An alternative strategy involves adapting the taxonomic resolution of model entities to that of the data source employed for large-scale initialization and estimating functional parameters from available plant trait databases while adopting alternative solutions for missing data and non-observable parameters. Here we report the advantages and limitations of this second strategy according to our experience in the development of MEDFATE (v. 2.8.1), a novel cohort-based and trait-enabled model of forest dynamics, for its application over a region in the Western Mediterranean Basin. First, 217 taxonomic entities were defined according to woody species codes of the Spanish National Forest Inventory. While forest inventory data were used to obtain some empirical parameter estimates, a large proportion of physiological, morphological, and anatomical parameters were mapped to measured plant traits, with estimates extracted from multiple databases and averaged at the required taxonomic level. Estimates for non-observable key parameters were obtained using meta-modeling and calibration exercises. Missing values were filled using imputation procedures based on trait coordination, taxonomic averages or both. The model properly simulated observed historical basal area changes, with a performance similar to an empirical model trained for the same region. While strong efforts are still required to parameterize trait-enabled models for multiple taxa, estimation procedures can be progressively refined, transferred to other regions or models and iterated following data source changes by employing automated workflows. We advocate for the adoption of trait-enabled population-structured models for regional-level projections of forest function and dynamics.

In recent decades, die-off events in Pinus sylvestris populations have increased. The causes of these phenomena, which are usually related to local and regional extreme hot droughts, have been extensively investigated from a physiological viewpoint. However, the consequences of die-off process in terms of demography and vegetation dynamics have been less thoroughly addressed. Here, we projected P. sylvestris plot dynamics after a die-off event, under climate change scenarios, considering also their early demographic stages (i.e., seedlings, saplings and ingrowth from the sapling to adult class), to assess the resilience of P. sylvestris populations after such events. We used Integral Projection Models (IPMs) to project future plot structure under current climate, and under RCP4.5 and RCP8.0 climate scenarios, using climatic suitability – extracted from Species Distribution Models – as a covariable in the estimations of vital rates over time. Field data feeding IPMs were obtained from two successive surveys, at the end of the die-off event (2013) and four years later (2017), undertaken on populations situated across the P. sylvestris range of distribution in Catalonia (NE Spain). Plots affected by die-off experienced a loss of large trees, which causes that basal area, tree diameter and tree density will remain lower for decades relative to unaffected plots. After the event, this situation is partially counterbalanced in affected plots by a greater increase in basal area and seedling recruitment into tree stage, thus promoting resilience. However, resilience is delayed under the climate-change scenarios with warmer and drier conditions involving additional physiological stress, due to a reduced abundance of seedlings and a smaller plot basal area. The study shows lagged effect of drought-induced die-off events on forest structure, also revealing stabilizing mechanisms, such as recruitment and tree growth release, which enhance resilience. However, these mechanisms would be jeopardized by oncoming regional warming.

Carbon sequestration and storage in biomass is one of the most important measures to mitigate climate change. Mediterranean woody crops can sequestrate carbon in the biomass of their permanent structures for decades; however, very few studies have focused on an assessment of biomass and carbon sequestration in these types of crops. This study is the first to estimate above- and belowground biomass carbon stock in Mediterranean woody crops through a bottom-up approach in the NE Iberian Peninsula in 2013. Moreover, this is the first time that an assessment of the annual changes in carbon stock in the study area over a six-year period is presented. For this purpose, eight crop- and site-specific equations relating biomass or biometric variables to crop age were calculated. Most of the data were our own measurements, but unpublished data supplied from other authors as well as data from literature were also considered. Census of Agriculture data was used to scale results from individual data up to the municipality level at the regional scale. Results show that in woody cropland in NE Spain the total biomass carbon stock in 2013 was 5.48 Tg C, with an average value of 16.44 ± 0.18 Mg C ha−1. Between 2013 and 2019, although there was a 2.8% mean annual decrease in the area covered by woody crops, the carbon stock in the biomass of these crops increased annually by 3.8% due to the growth of the remaining woody cropland. This new estimation of carbon stocks may contribute to better understand carbon balances and serve as a baseline to global inventories. It may also serve to assess and manage carbon storage as an ecosystem service provided by Mediterranean woody cropland for mitigating climate change and, in combination with adaptive strategies, for supporting a productive and resilient agro-food system.

ContextValidation of models of Land Use and Cover Change often involves comparing maps of simulated and reference change. The interpretation of differences between simulated and reference change depends on the characteristics of the process being studied. Our paper focuses on validation of models of one-way land change processes that spread in space.Objectives Our objective is to develop a method for validation of one-way land change models, such that the method provides objective information about the spatial distribution of errors.Methods Using distance analysis on reference data, we build a baseline model for comparison with simulations. We then simultaneously compare the four maps of reference at initial time, reference at final time, simulation at final time, and baseline at final time. We also use Total Operating Characteristic curves and multiple-resolution map comparison. We illustrate the methods with a simulation of forest insect infestations.ResultsThe methods give insights concerning the reference data and the spatial distribution of misses, hits, and false alarms with respect to initial points of infestations. The new methods reveal that the simulations underestimated change near initial points of spread.Conclusions The spatial distribution of errors is a topic of land change models that deserves attention. For models of one-way, geographically-spreading processes, we recommend that validation should distinguish between near and far allocation errors with respect to initial points of spread.