Patterns of α‐, β‐ and γ‐diversity of terrestrial vertebrates in continental scale

Biodiversity is not randomly distributed in space and understanding of diversity patterns can shed light on the factors and processes that shape it. The aim of this thesis is to detect and study spatial diversity patterns. Using an alternative sampling method, I estimated the area that contains a specified number of species (minA) and the species-area relationship (SAR) resulting from minA (Chapter 1). The spatial patterns of different diversity dimensions (α-, β- and γ-diversity) vary with the scale of observation (Chapter 2). The scale of observation depends on the taxonomic group’s characteristics, while differences between the human and taxonomic group’s scale of observation could add noise to the observed patterns. The sampling design is defined by the researcher and affects the spatial patterns. Contrariwise, the sample size and shape in the minA approach are defined by the species presence data eliminating the scale effect. The minA is defined as the minimum convex hull having as vertices the presence points of S species closer to the sampling’s starting point. The minA has a negative relationship with abundance equitability and increases with species spatial aggregation. The minA patterns for the African reptiles (Sauria, Serpentes, Amphisbaenia και Testudines) vary among taxonomic groups and with the requested number of species, are consistent with regions of high species richness, but cannot identify regions with medium diversity. The intercept and slope of SAR, the increase of species number with area, are considered indices of α- and β-diversity respectively, but depend on the sampling design, thus complicating the identification of underlying processes (Chapter 3). The species-minimum area relationship (minSAR) is introduced that results from minA method. The minSAR slope increases with abundance equitability and species random placement. In African reptiles, the slope exhibits the pattern Sauria>Serpentes>Amphisbaenia>Testudines and the intercept Testudines>Serpentes>Amphisbaenia >Sauria. The patterns of α- and β-diversity differ among taxonomic groups suggesting that minSAR can decipher the underlying mechanisms. The diversity patterns are shaped by species distributions and their overlap, which are defined by abiotic and biotic factors like biotic interactions, whose effect can be detected even in large scales (Chapter 4). The effect of prey species number was examined on the distribution of a generalist predator, the grass snake (Natrix natrix). Two species distribution models were applied with predictors: abiotic factors (abiotic) and the addition of prey species number (biotic) and taking into account dispersal ability. The two models had similar performance, with temperature and isothermality being important factors in both. Yet, the prey species number was the most important variable in the biotic model. The dispersal ability will define the future distribution of grass snake, either by moving northwards or significant reduction if the species cannot disperse. The species distribution in the south part of its range will not change according to the biotic model underlining the role of biotic interactions as potential response mechanism of biodiversity to climate change.Η βιοποικιλότητα δε διανέμεται τυχαία στον χώρο και η γνώση των προτύπων ποικιλότητας μπορεί να συμβάλλει στην αποσαφήνιση των παραγόντων που τα διαμορφώνουν. Σκοπός της διατριβής είναι η αποτύπωση και διερεύνηση των χωρικών προτύπων ποικιλότητας. Με τη βοήθεια μιας εναλλακτικής μεθόδου δειγματοληψίας, εκτίμησα την επιφάνεια που περιέχει έναν ζητούμενο αριθμό ειδών (minA) και τη σχέση αριθμού ειδών-επιφάνειας (SAR) που προκύπτει από αυτήν (Κεφάλαιο 1). Τα χωρικά πρότυπα των διαφορετικών διαστάσεων της ποικιλότητας (α-, β- και γ- ποικιλότητα) μεταβάλλονται με την κλίμακα παρατήρησης (Κεφάλαιο 2). Η κλίμακα παρατήρησης εξαρτάται από τα χαρακτηριστικά της ταξινομικής ομάδας, ενώ διαφορές μεταξύ της κλίμακας παρατήρησης του ανθρώπου και της ταξινομικής ομάδας εισάγουν θόρυβο στα παρατηρούμενα πρότυπα. Το δειγματοληπτικό σχέδιο ορίζεται από τον μελετητή και επηρεάζει τα χωρικά πρότυπα. Ωστόσο, το μέγεθος και σχήμα του δειγματολήπτη στην προσέγγιση της minA ορίζονται από τα δεδομένα εμφάνισης των ειδών, απαλείφοντας την επίδραση της κλίμακας. Η minA ορίζεται ως ελάχιστο κυρτό πολύγωνο με κορυφές τα σημεία εμφάνισης S ειδών που βρίσκονται εγγύτερα στο σημείο έναρξης της δειγματοληψίας. Η minA έχει αρνητική σχέση με την ισοδιανομή της αφθονίας και αυξάνει με τη χωρική συσσωμάτωση των ειδών. Τα πρότυπα της minA για τα ερπετά της Αφρικής (Sauria, Serpentes, Amphisbaenia και Testudines) διαφοροποιούνται μεταξύ των ταξινομικών ομάδων και με τον ζητούμενο αριθμό ειδών, συμπίπτουν με πλούσιες σε είδη περιοχές, αλλά δεν εντοπίζουν περιοχές με ενδιάμεση ποικιλότητα. Η αρχική τιμή και η κλίση της SAR, της αύξησης του αριθμού ειδών με την επιφάνεια, θεωρούνται μετρικές α- και β-ποικιλότητας αντίστοιχα, αλλά μεταβάλλονται με το δειγματοληπτικό σχέδιο περιπλέκοντας την ταυτοποίηση των υποκείμενων διεργασιών (Κεφάλαιο 3). Εισάγεται η σχέση αριθμού ειδών-ελάχιστης επιφάνειας (minSAR) που προκύπτει από τη μέθοδο minA. Η κλίση της minSAR αυξάνει με την ισοδιανομή αφθονίας και την τυχαία τοποθέτηση των ειδών. Στα ερπετά της Αφρικής, η κλίση έχει πρότυπο Sauria>Serpentes>Amphisbaenia>Testudines και η αρχική τιμή Testudines>Serpentes>Amphisbaenia>Sauria. Τα πρότυπα α- και β-ποικιλότητας διαφέρουν μεταξύ των ταξινομικών ομάδων υποδεικνύοντας ότι η minSAR μπορεί να αποκαλύψει τους υποκείμενους μηχανισμούς. Τα πρότυπα ποικιλότητας διαμορφώνονται από τις διανομές των ειδών και την επικάλυψή τους που καθορίζονται από αβιοτικούς και βιοτικούς παράγοντες όπως οι βιοτικές αλληλεπιδράσεις, η επίδραση των οποίων εντοπίζεται και σε μεγάλες κλίμακες (Κεφάλαιο 4). Εξετάστηκε η επίδραση του αριθμού ειδών λείας στη διανομή ενός γενικότροπου θηρευτή, του νερόφιδου (Natrix natrix). Χρησιμοποιήθηκαν δύο μοντέλα διανομής ειδών με προβλεπτικές μεταβλητές: αβιοτικούς παράγοντες (αβιοτικό) και με προσθήκη του αριθμού ειδών λείας (βιοτικό) και συνυπολογίζοντας την ικανότητα διασποράς. Τα δύο μοντέλα είχαν παρόμοια επίδοση, με την θερμοκρασία και την ισοθερμικότητα να είναι σημαντικοί παράγοντες και στα δύο μοντέλα. Ωστόσο, ο αριθμός ειδών λείας ήταν η σημαντικότερη παράμετρος στο βιοτικό μοντέλο. Η ικανότητα διασποράς θα καθορίσει τη μελλοντική διανομή του νερόφιδου, είτε με μετατόπιση προς τον βορρά ή σημαντική μείωση αν το είδος δεν μπορέσει να μετακινηθεί. Η διανομή του είδους στο νότιο τμήμα της εξάπλωσής του δε θα μεταβληθεί σύμφωνα με το βιοτικό μοντέλο υπογραμμίζοντας το ρόλο των βιοτικών αλληλεπιδράσεων ως πιθανό μηχανισμό απόκρισης της βιοποικιλότητας στην κλιματική αλλαγή

Temperature and Prey Species Richness Drive the Broad-Scale Distribution of a Generalist Predator

The ongoing climate change and the unprecedented rate of biodiversity loss render the need to accurately project future species distributional patterns more critical than ever. Mounting evidence suggests that not only abiotic factors, but also biotic interactions drive broad-scale distributional patterns. Here, we explored the effect of predator-prey interaction on the predator distribution, using as target species the widespread and generalist grass snake (Natrix natrix). We used ensemble Species Distribution Modeling (SDM) to build a model only with abiotic variables (abiotic model) and a biotic one including prey species richness. Then we projected the future grass snake distribution using a modest emission scenario assuming an unhindered and no dispersal scenario. The two models performed equally well, with temperature and prey species richness emerging as the top drivers of species distribution in the abiotic and biotic models, respectively. In the future, a severe range contraction is anticipated in the case of no dispersal, a likely possibility as reptiles are poor dispersers. If the species can disperse freely, an improbable scenario due to habitat loss and fragmentation, it will lose part of its contemporary distribution, but it will expand northwards

How Biodiversity, Climate and Landscape Drive Functional Redundancy of British Butterflies

Biodiversity promotes the functioning of ecosystems, and functional redundancy safeguards this functioning against environmental changes. However, what drives functional redundancy remains unclear. We analyzed taxonomic diversity, functional diversity (richness and β-diversity) and functional redundancy patterns of British butterflies. We explored the effect of temperature and landscape-related variables on richness and redundancy using generalized additive models, and on β-diversity using generalized dissimilarity models. The species richness-functional richness relationship was saturating, indicating functional redundancy in species-rich communities. Assemblages did not deviate from random expectations regarding functional richness. Temperature exerted a significant effect on all diversity aspects and on redundancy, with the latter relationship being unimodal. Landscape-related variables played a role in driving observed patterns. Although taxonomic and functional β-diversity were highly congruent, the model of taxonomic β-diversity explained more deviance than the model of functional β-diversity did. Species-rich butterfly assemblages exhibited functional redundancy. Climate- and landscape-related variables emerged as significant drivers of diversity and redundancy. Τaxonomic β-diversity was more strongly associated with the environmental gradient, while functional β-diversity was driven more strongly by stochasticity. Temperature promoted species richness and β-diversity, but warmer areas exhibited lower levels of functional redundancy. This might be related to the land uses prevailing in warmer areas (e.g., agricultural intensification)

The Effect of Climate and Human Pressures on Functional Diversity and Species Richness Patterns of Amphibians, Reptiles and Mammals in Europe

The ongoing biodiversity crisis reinforces the urgent need to unravel diversity patterns and the underlying processes shaping them. Although taxonomic diversity has been extensively studied and is considered the common currency, simultaneously conserving other facets of diversity (e.g., functional diversity) is critical to ensure ecosystem functioning and the provision of ecosystem services. Here, we explored the effect of key climatic factors (temperature, precipitation, temperature seasonality, and precipitation seasonality) and factors reflecting human pressures (agricultural land, urban land, land-cover diversity, and human population density) on the functional diversity (functional richness and Rao’s quadratic entropy) and species richness of amphibians (68 species), reptiles (107 species), and mammals (176 species) in Europe. We explored the relationship between different predictors and diversity metrics using generalized additive mixed model analysis, to capture non-linear relationships and to account for spatial autocorrelation. We found that at this broad continental spatial scale, climatic variables exerted a significant effect on the functional diversity and species richness of all taxa. On the other hand, variables reflecting human pressures contributed significantly in the models even though their explanatory power was lower compared to climatic variables. In most cases, functional richness and Rao’s quadratic entropy responded similarly to climate and human pressures. In conclusion, climate is the most influential factor in shaping both the functional diversity and species richness patterns of amphibians, reptiles, and mammals in Europe. However, incorporating factors reflecting human pressures complementary to climate could be conducive to us understanding the drivers of functional diversity and richness patterns

Diversity Patterns of Different Life Forms of Plants along an Elevational Gradient in Crete, Greece

Elevational gradients provide a unique opportunity to explore species responses to changing environmental conditions. Here, we focus on an elevational gradient in Crete, a climate-vulnerable Mediterranean plant biodiversity hotspot and explore the diversity patterns and underlying mechanisms of different plant life forms. We found that the significant differences in life forms’ elevational and environmental ranges are reflected in α- diversity (species richness at local scale), γ-diversity (species richness at regional scale) and β-diversity (variation in species composition). The α- and γ-diversity decreased with elevation, while β-diversity followed a hump-shaped relationship, with the peak varying between life forms. However, β-deviation (deviation from null expectations) varied significantly with elevation but was life formindependent. This suggests that species composition is shaped by the size of the available species pool which depends on life form, but also by other deterministic or stochastic processes that act in a similar way for different life forms. The strength of these processes varies with elevation, with hotter–drier conditions and increased human activities filtering species composition at lowlands and large-scale processes determining the species pool size overriding local ecological processes at higher elevations