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Study area-Iberian Peninsula with major river basins and mountain chains where most phylogeographic refugia are located.
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The Iberian Peninsula hosts a high diversity of oak species, being a hot-spot for the conservation of European White Oaks (Quercus) due to their environmental heterogeneity and its critical role as a phylogeographic refugium. Identifying and ranking the drivers that shape the distribution of White Oaks in Iberia requires that environmental variable...
Contexts in source publication
Context 1
... Iberian Peninsula (IP) is located in southwestern Europe (bounded by longitudes -9.55 • W to 3.35 • E and latitudes 35.87 • N to 43.80 • N) and has a total area of 583 832 km 2 (Figure 1). The region holds a highly diverse geological history [53] and a climate that ranges from a dry Mediterranean in the southeast to a wet Temperate Atlantic in the north [13]. ...
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... increase in the performance by the combined set of climate and EFA is also reflected in terms of sensitivity (true positive rate), but especially in terms of specificity (true negative rate) (Figure 3). As before, these gains are reflected especially in the NDE group, and particularly for species such as Q. ×cerrioides, Q. canariensis, Q. orocantabrica and Q. estremadurensis (Table 1 and Appendix A - Figure A1). Results from the post hoc ANOVA test based on linear mixed-effects modelling (with a species and evaluation metric as random factors) showed a significant effect of the set of predictive variables on performance scores (p < 0.001). ...
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... narrow distributed; ** endemic. The consistent better performance of combined climate and EFA models, despite the relatively small increment for some species (Figures 2, 3 and A1), suggests that these two types of variables, when combined, improve the predictive models. Results improved especially for NDE species which have narrower biogeographic ranges and ecological niche breadths. ...
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... the contribution of EFA predictors brings higher accuracy for the spatial delimitation of these species' recovery areas, by reflecting the vegetation state through primary productivity and moisture conditions (better captured by NDWI-related EFA variables), and by adding it to the climatic suitability for each species. When analysing the narrowly-distributed oaks (Figure 5), Q. canariensis evidences suitable environmental conditions in its "typical" locations, from the Iberian southwest (Monchique and Algeciras) and Catalonian sub-populations, with the maintenance of residual areas in Morena System and suitable areas and Littoral shore of Centre Portugal (around Lisbon) (Figure 1). A similar pattern is followed by Q. estremadurensis, with a wider suitable area, through central Portugal up to Montes de Toledo (Spain) and an incursion in the Portuguese Douro thermophilic areas. ...
Context 5
... [90,91]. Q. orocantabrica shows a wider area out of its currently known range, across the Cantabrian mountains and three disjoint subpopulations in Rioja, Cuenca and the Central system (Figure 1), with the EFA predictors contributing to diagnose orophile conditions in the suitable environmental space. These novel spatial inferences may in turn help managers and stakeholders to devise much-needed restauration, recovery or re-introduction efforts [92][93][94][95]. ...
Context 6
... Quercus broteroi combined model shows a continuous area in western Iberia and throughout the Morena System and Montes de Toledo, with the overlap of partial models evidencing the best areas for this species recovery. By opposite, Q. faginea combined models show a wider area in the Douro Basin and throughout the Cantabrian mountains, Iberian and Betic Systems, and eastern IP, while the overlap shows a more conservative area for its recovery (Figure 1). Likewise, Q. pyrenaica presents a similar pattern, while including the central system and northwest Iberian mountain areas, with disjoint relict subpopulations in southern Iberia. ...
Context 7
... projections for all Iberian White Oak species (Figure 7) reveal areas with cumulative suitability up to seven oak taxa, highlighting the Iberian southwest mountains, the Lusitanian Basin, Montes de Toledo and to sub-littoral Catalonian mountains (Figure 1) as highly relevant. These areas can be considered the largest hotspots and the most important "lost" areas for the Iberian White Oaks conservation, being suitable for the highest diversity of oak taxa. ...
Citations
... Several trend studies did not include a precise description of the effects at the forest level (phenological changes, mortality, or productivity changes). However, these studies commonly described changes in vegetation indices and associated changes in vegetation as a result of changing climatic conditions [101,109,110,[206][207][208][209][210][211][212][213][214]. ...
Temperate forests are particularly exposed to climate change and the associated increase in weather extremes. Droughts, storms, late frosts, floods, heavy snowfalls, or changing climatic conditions such as rising temperatures or more erratic precipitation are having an increasing impact on forests. There is an urgent need to better assess the impacts of climate change and extreme weather events (EWEs) on temperate forests. Remote sensing can be used to map forests at multiple spatial, temporal, and spectral resolutions at low cost. Different approaches to forest change assessment offer promising methods for a broad analysis of the impacts of climate change and EWEs. In this review, we examine the potential of Earth observation for assessing the impacts of climate change and EWEs in temperate forests by reviewing 126 scientific papers published between 1 January 2014 and 31 January 2024. This study provides a comprehensive overview of the sensors utilized, the spatial and temporal resolution of the studies, their spatial distribution, and their thematic focus on the various abiotic drivers and the resulting forest responses. The analysis indicates that multispectral, non-high-resolution timeseries were employed most frequently. A predominant proportion of the studies examine the impact of droughts. In all instances of EWEs, dieback is the most prevailing response, whereas in studies on changing trends, phenology shifts account for the largest share of forest response categories. The detailed analysis of in-depth forest differentiation implies that area-wide studies have so far barely distinguished the effects of different abiotic drivers at the species level.
... The global biogeographic and geobotanical knowledge about the Iberian oak forests was of paramount importance for predicting the occurrence and description of the remaining nothotaxon to be unveiled among the Iberian white oaks, where both parents potentially overlap and occur in sympatry (Vila-Viçosa et al. 2015). This is particularly important for the Q. canariensis relictual forest type that is related with the preservation of peculiar paleoclimatic conditions, in extreme Atlantic areas of the Iberian southwest, which results in an hotspot for oak species conservation (Vila-Viçosa et al. 2020a, Vila-Viçosa et al. 2020b, Vila-Viçosa & Arsénio 2021. ...
The genus Quercus is widely known for its ability to hybridize among sympatric species, especially inside the same Section.
The Iberian Peninsula is a major phylogeographic hotspot for oak diversity, with almost all possible combinations of hybrids already described inside sect. Quercus. In this work we present the remaining hybrid not yet described among the Iberian
white oaks; we found it in one of the few areas (in the Southern Iberian Peninsula) where both parents (the Algerian oak, Q. canariensis, and the Lusitanian dwarf oak, Q. lusitanica) grow sympatrically. We provide a description of this nothotaxon
(which we named Quercus ×gaonae) based on morphological characters related to leaf shape and trichome analysis. This work emphasizes the practice of natural history and encourages botanists to keep up their work on describing plant diversity
in a major hotspot like the Mediterranean Basin.
... SDMs help estimate ecological niche boundaries, investigate habitat suitability, and identify geographical distribution patterns (Phillips et al., 2006;Guisan et al., 2013). Combining remote sensing with SDM techniques can provide valuable insights into the habitat quality, resilience, and threats faced by endemic and/or threatened species (Vila-Viçosa et al., 2020). Land cover and land use changes can be effectively highlighted using remote sensing, while the quantification of climate change impacts on habitat suitability serves conservation purposes at a larger scale (Guisan et al., 2013). ...
The global decline of amphibian populations, driven by anthropogenic activities, is a pressing conservation issue, with salamanders being of particular concern, as these species serve as ecological indicators vulnerable to environmental change. Mountain newts of the Neurergus genus, which are endemic to the Zagros Mountain chain from southeastern Turkey to northern Iraq and southwestern Iran, face a multitude of threats. Among these threats, climate change and land use alterations have been identified as major contributors to the decline of these species. Given the varying spatiotemporal scales at which these factors operate, in this study we aimed to assess the impacts of climate and land use/land cover (LULC) changes (LULCC) on the distribution of the Neurergus genus. We employed MaxEnt model to predict their habitat suitability under current climatic conditions. We projected the predicted model to the future, i.e., 2050, under two climate change scenarios. We then proceeded to map the LULC patterns of the identified suitable habitats for each species using Landsat satellite images, and conducted a hindcast of LULCC within these habitats for three time-slices 1988, 2005, and 2020. Finally, we evaluated the efficiency of current network of protected areas (PAs) and key biodiversity areas (KBAs) in Iran, Iraq, and Turkey to cover suitable habitats of the species. Our results revealed that climate changes would negatively influence all Neurergus species, with southern species in Iran and Iraq, i.e., N. derjugini, N. kaiseri, and N. crocatus exhibiting the greatest range loss. Conversely, LULC change detection indicated that northern species in Turkey, i.e., N. strauchii and N. barani, are more exposed to cropland developments and have experienced greatest habitat changes over the past 30 years. Ultimately, our findings underscore the insufficiency of extant conservation areas in protecting Neurergus habitats and urge the need for comprehensive conservation measures. We recommend promoting less strictly conserved areas, e.g., KBAs, implementing trans-boundary conservation plans, and designating new reserves to ensure long-term preservation of amphibians in the regions.
... (Odemira and Aljezur). The main ecological feature associated to this species distribution is summer horizontal precipitation, originated either by advection fogs, either by thermic inversion in deep valleys or interior mountain areas (Vila-Viçosa et al., 2015;Vila-Viçosa et al., 2020a;Vila-Viçosa et al., 2020b;Vila-Viçosa & Arsénio, 2021). Note: A notable distinctive feature of Quercus canariensis is the indumentum type. ...
We present a comprehensive taxonomic and nomenclatural review of the Portuguese oaks (Quercus), issuing from thorough biogeographical, historical, bibliographic, field and herbarium data. We propose a new annotated checklist for all taxa and nothotaxa belonging to Quercus L. genus in the Portuguese national territory, extending the analysis to the broader biogeographical context of the Western Mediterranean subregion. Fifteen herbaria collections were exhaustively studied and complemented by information retrieved from digital collections, resulting in the recognition of eleven native oaks. Further treatment at infraspecific level includes one subspecies and additional 23 nothotaxa. We designate lectotypes for 27 names and add a preliminary list of 148 cultivated taxa, resulting in a total of 183 oaks found in Portugal.
Moreover, we clarify the taxonomical status and reinstate Q. pseudococcifera and Q. airensis as native to the western
Mediterranean subregion. We recover the concept of Q. faginea, as the widespread gall oak in Portugal and update its synonym list. We propose Q. orocantabrica and Q. estremadurensis as autonomous species within the broad European Q. robur s.l. group, alongside two newly circumscribed subsections within Section Quercus. We describe five new nothotaxa, including four unreported to nature and one missing a suitable name (Q. ×almeidae, Q. ×alvesii, Q. ×capeloana, Q. ×eborense and Q. ×sampaioana) and we approach the nomenclatural resolution of the remaining natural hybrids. Lastly, we provide an identification key, intelligible for non-specialists, including both native taxa and most frequent nothotaxa.
We emphasise the importance of historical and literature review, combined with accurate biogeographical information, as paramount to coherent taxonomical resolution. Both specimens and their associated records were found of crucial significance to a taxonomical model that is, in the end, useful for biodiversity conservation.
... Given their particular environmental conditions at the boundary between Mediterranean and Eurosiberian climates, sub-Mediterranean regions are of special conservation interest because of their high biodiversity. These ecotone regions host species from both Mediterranean and Eurosiberian climates and many endemic species forming unique plant communities (S anchez de Dios et al., 2009;Vila-Viçosa et al., 2020a, 2020b; see Loidi (2017) for a detailed description of the sub-Mediterranean vegetation in the Iberian Peninsula). ...
Background
The Iberian Peninsula comprises one of the largest boundaries between Mediterranean and Eurosiberian vegetation, known as sub-Mediterranean zone. This ecotone hosts many unique plant species and communities and constitutes the low-latitude (warm) margin of numerous central European species which co-occur with Mediterranean vegetation. Two of the main species found in this region are the Eurosiberian European beech (Fagus sylvatica L.) and the Mediterranean Pyrenean oak (Quercus pyrenaica Willd.). It remains unclear how the different physiological and adaptive strategies of these two species reflect their niche partitioning within a sub-Mediterranean community and to what extent phenotypic variation (intraspecific variability) is driving niche partitioning across Eurosiberian and Mediterranean species.
Methods
We quantified functional niche partitioning, based on the n-dimensional hypervolume to nine traits related to resource acquisition strategies (leaf, stem and root) plus relative growth rate as an additional whole-plant trait, and the environmental niche similarity between Pyrenean oak and European beech. Further, we analyzed the degree of phenotypic variation of both target species and its relationship with relative growth rates (RGR) and environmental conditions. Plant recruitment was measured for both target species as a proxy for the average fitness.
Results
Species’ functional space was highly segregated (13.09% overlap), mainly due to differences in niche breadth (59.7%) rather than niche replacement (25.6%), and beech showed higher trait variability, i.e., had larger functional space. However, both species shared the environmental space, i.e., environmental niches were overlapped. Most plant traits were not related to abiotic variables or RGR, neither did RGR to plant traits.
Conclusions
Both target species share similar environmental space, however, show notably different functional resource-use strategies, promoting a high complementarity that contributes to maintaining a high functionality in sub-Mediterranean ecosystems. Therefore, we propose that conservation efforts be oriented to preserve both species in these habitats to maximize ecosystem functionality and resilience.
... A study by Zimmermann et al. (2007) showed that by incorporating predictors derived from remote sensing data significantly improved the accurcy of SDMs. Remote sensing data has also been used in SDMs for conservation purposes (Vila-Viçosa et al., 2020), to identify tree species (Engler et al., 2013) and to classify habitat types (Agrillo et al., 2021). Another way to using remote sensing data in SDMs is by extracting topographic variables such as altitude and slope at where species are found, then incorporating this information in models. ...
Species distribution models (SDMs) are useful for predictive and explanatory purposes, allowing biologists to identify how human and environmental factors influence distributions of plants and animals. Lack of high-resolution climatic variables is one of the challenges for accurately predicting distributions of organisms at local or landscape scales. This study used SDMs to predict four dominant tree species distributions in northern Iran temperate forests (400km 2 area) using high-resolution Sentinel-2 data (20 m) and topographic variables. We divided the explanatory variables into four datasets with increasing complexity of Sentinel-2 data and modelled distributions using four statistical and machine learning algorithms: random forest (RF), generalized boosting model (GBM), generalized linear model (GLM), and generalized additive model (GAM). Our results suggested differences in the predictive performance of the four algorithms. We found the most complex dataset, including topographic variables, Sentinel-2 bands, and vegetation and soil indices, gave the best fit for the four tree species, improving the accuracy of models for the different species between 5 and 16%. We then selected the most complex dataset to produce an ensemble model of the modeling algorithms where evaluation criteria were varied for tree species. Our result showed that the performance of SDMs improved using different satellite remote sensing data including raw bands, topographic variables and indices in the Hyrcanian forest, northern Iran. Elevation was a more significant variable than Sentinel bands and Sentinel vegetation indices variables for predicting the tree species distributions. With the Hyrcanian Forest included in this study region of northern Iran recently declared a UNESCO World Heritage site, a key result of our improved species distribution maps for these four dominant tree species is to support conservation management of forest biodiversity in this region.
... In this context, species distribution models (SDMs) therefore remain valuable tools enabling tree species mapping and monitoring (Falk and Mellert, 2011;Vila-Viçosa et al., 2020). Around the world, SDMs have been widely used to support forest management planning (Pecchi et al., 2019) and biodiversity conservation, e.g., in protected areas (Rahman et al., 2019;Swan et al., 2021). ...
... Early studies of species distributions used only climatic variables as predictors (Huntley et al., 1995;Sykes et al., 1996). Subsequently, several studies have shown that combining different types of variables, e.g., including satellite-derived climatic variables with nonclimatic variables (land cover, topography, phenology, etc.) can improve SDM performance (e. g., Austin and Van Niel, 2011;Burns et al., 2020;Fournier et al., 2017;Suárez-Seoane et al., 2004;Vila-Viçosa et al., 2020). Currently, environmental variables used in SDMs are related to climate, topography, substrate, land cover and vegetation, phenology, measures of landscape patterns, and information about other species (biotic interactions), among others (Amiri et al., 2020;Austin and Van Niel, 2011;Burns et al., 2020;Fournier et al., 2017;Gardner et al., 2019;Nguyen et al., 2015;Vila-Viçosa et al., 2020). ...
... Subsequently, several studies have shown that combining different types of variables, e.g., including satellite-derived climatic variables with nonclimatic variables (land cover, topography, phenology, etc.) can improve SDM performance (e. g., Austin and Van Niel, 2011;Burns et al., 2020;Fournier et al., 2017;Suárez-Seoane et al., 2004;Vila-Viçosa et al., 2020). Currently, environmental variables used in SDMs are related to climate, topography, substrate, land cover and vegetation, phenology, measures of landscape patterns, and information about other species (biotic interactions), among others (Amiri et al., 2020;Austin and Van Niel, 2011;Burns et al., 2020;Fournier et al., 2017;Gardner et al., 2019;Nguyen et al., 2015;Vila-Viçosa et al., 2020). ...
Around the world, SDMs have been widely used to support forest management planning and biodiversity conservation. Beyond the prediction of species distribution provided by the SDMs, this study aimed to analyze the spatial distribution of tree species diversity using SDMs. The study area is a Faidherbia albida parkland in Central Senegal. It is characterized by a tree-based farming system dominated by Faidherbia albida.
Using a robust and representative dataset of 9258 tree species occurrence, we first determined by an SDM the current potential spatial distribution of the 16 main tree species forming the parkland. Specifically, using 6 SDM algorithms and applying several modeling techniques with different categories of predictor variables (e.g., climate, topography, soil properties and human impact) we benchmarked 576 SDMs to achieve best model predictions for tree species. Then, tree species diversity maps were created on the basis of the resulting SDM predictions. Finally, the spatial dynamics of tree species diversity were discussed in relation to landscape characteristics, including heterogeneity, composition and human impact.
The results showed that there is no single ‘best’ SDM algorithm (among the 6 algorithms tested) or modeling approach for all species. Benchmarking several modeling techniques allowed strengthening SDM performance, achieving AUC values that ranged from 0.64 (intermediate accuracy) to 0.87 (very good accuracy). The spatial dynamics of tree species diversity is related to the landscape heterogeneity and composition. In the Sahelian agroforestry systems (AFS), tree diversity is sustained by anthropization. A significant negative correlation with the distance to the village was found, i.e. the closer you get to the village, the greater the diversity of trees.
This study could be crucial for analyzing tree species diversity when abundance information is not available.
... Una vez calibrados para un contexto espaciotemporal específico, los modelos pueden ser transferidos a otros escenarios espaciales y temporales. Para ello, los algoritmos aplican la fórmula que describe el nicho de la especie en el proceso de calibración, a otro conjunto de variables ambientales que pueden corresponder al pasado (como el Holoceno) y al futuro (por ejemplo a través de los diferentes Shared Socioeconomic Pathways scenarios (SSPs: 126, 245, 370 and 585) y para diferentes periodos de tiempo (por ejemplo 2060, 2080 and 2100), series temporales de datos de teledetección (como MODIS y Landsat), o a otra región geográfica o escala de resolución en las condiciones actuales (Arenas-Castro et al., 2020;Arenas-Castro and Sillero, 2021;Regos et al., 2016). No obstante, se recomienda mantener una cierta coherencia temporal entre los datos de calibración, tanto ocurrencias como variables ambientales. ...
Los ecosistemas forestales están expuestos a una gran variedad de presiones ambientales, sociales y económicas que desafían su sostenibilidad a escala planetaria. La biodiversidad que albergan puede responder de muy diversas formas a estas presiones, generando impactos variables y complejos. Por tanto, es necesario anticiparse a dichos impactos para responder de forma eficiente a estos retos de futuro con una gestión adecuada, proactiva y adaptativa. En esta tarea, los modelos de distribución de especies (SDMs en inglés) son una de las herramientas más versátiles para estimar la distribución potencial de las especies y la disponibilidad de hábitat, permitiendo predecir posibles respuestas a las diversas presiones ambientales. Este capítulo constituye una guía teórico-práctica para el desarrollo de SDMs en el ámbito forestal, con aplicación en la conservación y manejo de la biodiversidad como parte de la planificación y gestión de sistemas forestales. La primera parte del capítulo se centra en los fundamentos teóricos que soportan los modelos de nicho ecológico. Seguidamente se describen los pasos y aspectos más relevantes que se deben tener en cuenta para completar una adecuada aplicación de los modelos: fuentes de datos, técnicas de modelización, y descripción detallada de los procesos de modelización de acuerdo con el protocolo estándar recomendado. Se acompaña este capítulo con un tutorial online en R para el desarrollo de SDMs en ecosistemas forestales que permite al usuario desarrollar correctamente todo el flujo de trabajo. El capítulo termina con una breve sección sobre las limitaciones y retos de futuro de estas técnicas en el ámbito forestal.
... A novel and very promising approach that could improve cryptogam biodiversity assessments and predictions is the development of time series-derived RS variables describing ecosystem functioning attributes to characterize species habitat dynamics, such as energy balance, primary production and C02 fluxes, or vegetation or soil water content dynamics . The usefulness of this approach for prediction purposes has already been demonstrated in different taxa such as birds and vascular plants, including herbaceous and trees (Arenas-Castro et al., 2018Vila-Viçosa et al., 2020). Another ...
Cryptogams (bryophytes and lichens) are ubiquitous non-vascular species that contribute significantly to total biodiversity and play an essential ecological role in ecosystem functioning worldwide. Specifically, cryptogams influence water, carbon and nutrient cycles, as well as physical and chemical weathering, and increase stability of soils, preventing their erosion and regulating their temperature and humidity. Cryptogams facilitate ecosystem recovery following disturbances, and provide microhabitats for micro- and macroorganisms, and a food source for invertebrates and herbivores. These species are also reliable and highly sensitive indicators to environmental disturbances and currently face numerous human-induced threats mainly derived from land use and climate change. Despite this, cryptogams are generally neglected in conservation planning mostly due to current knowledge gaps in their diversity, ecology and distribution, which jeopardizes the maintenance of their species and ecological role. New technologies and data sources such as remote sensing (RS) can significantly help to fill these gaps and ultimately improve the representation of cryptogams in systematic conservation planning. The contribution of RS to cryptogam biodiversity assessments can be particularly valuable in vast and largely unknown regions such as boreal forests, where these species and their habitats face increasing human-induced threats. The general objective of this thesis is to elucidate the role that RS can play in the evaluation and generation of information on cryptogam biodiversity in a boreal context. The study region is located in the Canadian boreal forest, within the Eeyou-Istchee James Bay region in Northern Quebec. As specific objectives, Chapter II aims to predict and map diversity (species richness) patterns of i) total bryophytes, and ii) bryophyte guilds (mosses, liverworts and sphagna) using RS data; Chapter III focusses on producing predictive models of rare bryophyte species using RS-derived predictors in an Ensembles of Small Models (ESMs) framework; and Chapter IV is intended to describe and model the lichen alpha diversity (species richness) and beta diversity (species turnover) components parallelly using two set of RS-derived variables (Red and NIR; EVI2) from two sensors (Wordlview-3, WV3; Sentinel-2, S2) at different high spatial resolutions (1.2m; 10m), and ii) to identify which habitat types represent lichen biodiversity hotspots.
The Random Forest algorithm used in Chapter II allowed us to develop spatially explicit models and to generate predictive cartography at 30m resolution of total bryophyte, moss, liverwort and sphagna richness. These models explained a significant fraction of the variation in total bryophyte and guild level richness, both in the calibration (42 to 52%) and validation sets (38 to 48%), and consistently identified vegetation (mainly NDVI) and climatic variables (temperature, precipitation, and freeze-thaw events) as the most important predictors for all bryophyte groups modeled. Guild-level models identified differences in important factors determining the richness of each of the guilds and thus in their predicted richness patterns, which provide valuable information for management and conservation strategies for bryophytes. The RS-based ESMs developed in Chapter III built from Random Forest and Maxent techniques using predictors related to topography (TPI) and vegetation (EVI2, NDWI1, Vegetation Continuous fields, and PALSAR HVHH) yielded poor to excellent prediction accuracy (AUC > 0.5) for 38 of the 52 modeled species despite their low number of occurrences (< 30), with AUC values > 0.8 for 19 species. The actual presences of the 38 species modeled better than random (AUC ≤ 0.5) were accurately predicted, as supported by the high sensitivity values obtained that ranged from 0.8 to 1 with an average of 0.959 ± 0.063. The distribution of these 38 species and the richness patterns both for total rare bryophytes and rare species at the guild level were mapped at 30m resolution. Chapter III also revealed a spatial concordance between rare (present chapter) and overall bryophyte richness patterns (Chapter II) in different regions of the study area, which has important implications for conservation planning. In Chapter IV, a total of 116 lichen species were identified. While high lichen richness was generally found across our plots (36.5 ± 9 species), those richer in microhabitats often harbored more species (R2 = 0.22) regardless of the habitat type. Differences in species composition were identified among plots (25.6% explained by PCoA) and habitat types (PERMANOVA R2 = 0.35), both being supported by differences in microhabitat composition (Mantel r = 0.22 and PERMANOVA R2 = 0.29, respectively). Rocky outcrops and undisturbed coniferous forests represented the main lichen biodiversity hotspots, while other habitat types were also important for maintaining overall biodiversity. Red and NIR variables were effective for modeling alpha and beta diversity at both resolutions, while EVI2, either from WV3 or S2, was only informative for assessing beta diversity. Poisson models explained up to 32% of the variation in lichen richness. Generalized dissimilarity models described well the relationship between beta diversity and spectral dissimilarity (R2 from 0.25 to 0.30), except for the S2 EVI2 model (R2 = 0.07), confirming that more spectrally and thus environmentally different areas tend to harbor different lichen communities. While WV3 often outperformed the S2 sensor, the latter still provides a powerful tool for the study of lichens and their conservation.
This thesis demonstrated the ability for RS at medium and high spatial resolutions to characterize the habitat of inconspicuous cryptogam species, to capture diverse meaningful ecological features shaping their distribution, and thus to better understand and/or predict their biodiversity patterns. RS-based modeling frameworks proved to be informative even when the available baseline information on cryptogam biodiversity was limited. By identifying environmental drivers of cryptogam biodiversity that can guide specific management actions, and by providing predictive mapping of their spatial patterns at high level of detail across the landscape, this work unequivocally highlighted the high potential of RS technology for conservation purposes of cryptogams. This thesis thus represents a very important step to achieve the inclusion of these inconspicuous and generally overlooked species into systematic conservation planning.
... Rapidly, it was decided that the collection should include all oak species related to the remarkable Iberian biogeographic gradient that goes from temperate deciduous forests to dryadapted evergreen ones, across a peculiar sub-Mediterranean belt (Loidi 2017;Vila-Viçosa et al. 2020b). In this area, these groups of marcescent oaks frequently hybridize with sympatric species across an amazing gradient, a significant biogeographic process in the evolutionary and demographic history of the Eurasian White Oaks (Kremer and Hipp 2020;Vázquez et al. 2015;Vila-Viçosa et al. 2014;Vila-Viçosa et al. 2015;Vila-Viçosa et al. 2020a). ...
... The collection of native species includes Q. estremadurensis and Q. canariensis. The first is found as scattered isolated trees dispersed across the western Iberian Peninsula and North Africa (Vázquez et al. 2018;Vila-Viçosa et al. 2020a;Vila-Viçosa et al. 2020b). Recent work has confirmed the phylogenetic segregation of this taxon previously thought by some authors to be a subspecies of Q. robur or a synonym of Q. robur ). ...
One of BGUP’s most recent collections is of the genus Quercus. The idea for the collection was born around 2016, in the context of a Ph.D. project studying western Mediterranean White Oaks (section Quercus) at the Faculty of Sciences of the University of Porto and CIBIO-InBio. The collection started therefore with Iberian marcescent oak species, traditionally ascribed to subsection Galliferae (Spach 1842) that includes Q. canariensis, Q. faginea, Q. lusitanica, and related taxa and nothotaxa. The initial objectives of the collection were, on one hand, to cultivate a large number of individuals of these species and their hybrids to provide material for the ongoing study of interspecific and pervasive introgression among Iberian White Oaks (Vila-Viçosa et al. 2021) and, on the other, to strengthen BGUP’s role in Iberian efforts for native climax-forest conservation.
