An R package for simulating metapopulation dynamics and range expansion under environmental change

The metapopulation paradigm is central in ecology and conservation biology to understand the dynamics of spatially-structured populations in fragmented landscapes. Metapopulations are often studied using simulation modelling, and there is an increasing demand of user-friendly software tools to simulate metapopulation responses to environmental change. Here we describe the MetaLandSim R package, mwhich integrates ideas from metapopulation and graph theories to simulate the dynamics of real and virtual metapopulations. The package offers tools to (i) estimate metapopulation parameters from empirical data, (ii) to predict variation in patch occupancy over time in static and dynamic landscapes, either real or virtual, and (iii) to quantify the patterns and speed of metapopulation expansion into empty landscapes. MetaLandSim thus provides detailed information on metapopulation processes, which can be easily combined with land use and climate change scenarios to predict metapopulation dynamics and range expansion for a variety of taxa and ecological systems

Survey of the amphibians in “Fânațele Clujului – Copârșaie”, part of the “Dealurile Clujului de Est” (ROSCI0295) Natura 2000 protected area

As habitat loss poses challenge to conservation, it is becoming increasingly important to address questions about the extent to which connectivity between habitat patches is changing, and how this affects the local population of different species in these patches. The objective of our research was to monitor ponds and the pond-breeding amphibian species in a protected area. Therefore, we conducted day and night surveys, and compare the data collected in 2022 with the results of the latest available survey (2019), to simulate the patch occupancy of amphibian species over a 25-year timeframe. We found that combining the species occupancy data collected from both day and night surveys lead to higher patch occupancy values and higher number of registered individuals, compared to data collected only during daytime. The number of ponds decreased from 2019 to 2022, and further habitat loss could result in the disappearance of the local population if the area continues to dry out. Climate and landscape change could be major contributors to habitat loss in the future, therefore, in order to ensure the persistence of these local populations, we recommend the development of climate and habitat scenarios, and the planning of conservation measures based on these scenarios

Species traits, patch turnover and successional dynamics: when does intermediate disturbance favour metapopulation occupancy?

Research articleBackground: In fragmented landscapes, natural and anthropogenic disturbances coupled with successional processes result in the destruction and creation of habitat patches. Disturbances are expected to reduce metapopulation occupancy for species associated with stable habitats, but they may benefit species adapted to transitory habitats by maintaining a dynamic mosaic of successional stages. However, while early-successional species may be favoured by very frequent disturbances resetting successional dynamics, metapopulation occupancy may be highest at intermediate disturbance levels for species with mid-successional habitat preferences, though this may be conditional on species traits and patch network characteristics. Here we test this ‘intermediate disturbance hypothesis’ applied to metapopulations (MIDH), using stochastic patch occupancy simulation modelling to assess when does intermediate disturbance favour metapopulation occupancy. We focused on 54 virtual species varying in their habitat preferences, dispersal abilities and local extinction and colonization rates. Long-term metapopulation dynamics was estimated in landscapes with different habitat amounts and patch turnover rates (i.e. disturbance frequency). Results: Equilibrium metapopulation occupancy by late-successional species strongly declined with increasing disturbance frequency, while occupancy by early-successional species increased with disturbance frequency at low disturbance levels and tended to level-off thereafter. Occupancy by mid-successional species tended to increase along with disturbance frequency at low disturbance levels and declining thereafter. Irrespective of habitat preferences, occupancy increased with the amount of habitat, and with species dispersal ability and colonisation efficiency. Conclusions: Our study suggests that MIDH is verified only for species associated with mid-successional habitats. These species may be particularly sensitive to land use changes causing either increases or decreases in disturbance frequency. This may be the case, for instance, of species associated with traditional agricultural and pastoral mosaic landscapes, where many species disappear either through intensification or abandonment processes that change disturbance frequencyinfo:eu-repo/semantics/publishedVersio

Synergistic effects of climate change and habitat fragmentation on species range shifts and metapopulation persistence

The effects of climate and landscape change on biodiversity have been widely acknowledged. However, there is still limited understanding on how the interaction among these processes affects species persistence over large spatial scales. This thesis aims to study the synergistic effects of climate and landscape change on species persistence and range shift dynamics. Using the Cabrera vole as model species, and combining ecological niche modeling (ENM), non-invasive genetic analysis and field sampling at predicted range margins, it is shown that forecasting range shifts of metapopulations under climate change, should require detailed sampling at the extremes of the ecological niche and that combining these three techniques allows an effective assessment of the niche. Analysis of metapopulation persistence and range expansion under landscape and climate change involved the development of an R package, ‘MetaLandSim’. This package offers a set of simulation tools integrating concepts from metapopulation and graph theories, providing an opportunity for testing ecological theories and evaluating species responses to environmental change. A first example of the package use is demonstrated by combining ENM projections with dispersal models (DM) considering three different connectivity scenarios. It is clearly demonstrated that combining range shift with lower connectivity will result in narrower range sizes for the Cabrera vole, highlighting the relevance of both, climate change and landscape connectivity in range dynamics evaluation. Finally, ‘MetaLandSim’ was used to test the hypothesis that intermediate levels of landscape disturbance may increase species persistence under certain species and landscape traits. Using a set of 54 virtual species differing in their ecological traits, it is shown that species with mid to higher dispersal and early successional preferences were more likely to benefit from intermediate disturbance. Overall, this study provides important insights for improving predictions on metapopulation persistence and range dynamics under various scenarios of landscape and climate change; Efeitos sinergéticos das alterações climáticas e fragmentação de habitat na distribuição das espécies e persistência metapopulacional Resumo: Os efeitos das alterações de paisagem e climáticas são reconhecidos na literatura científica. Esta tese tem por objetivo contribuir para a clarificação e compreensão dos efeitos sinergéticos das alterações climáticas e de paisagem na persistência das metapopulações e na alteração das áreas de distribuição das espécies. Usando o ratode- Cabrera como modelo, e combinando a modelação de nicho ecológico (ENM) com técnicas de genética não-invasiva e amostragens de campo nas margens da área de distribuição, demonstra-se a combinação das três técnicas e a inclusão de amostras nos extremos do nicho ecológico tornam mais eficaz que a previsão das novas áreas de distribuição num contexto de alterações climáticas. A análise da persistência metapopulacional e da expansão da área de distribuição em condições de alterações ambientais envolveu o desenvolvimento do package de R ‘MetaLandSim’. Este oferece um conjunto de ferramentas de simulação combinando as teorias dos grafos e metapopulações, o que permite testar teorias ecológicas e avaliar respostas das espécies às alterações ambientais. Usa-se este package para gerar modelos de dispersão (DM) que consideram simultaneamente a conectividade da paisagem e capacidade de dispersão. Estes DM, com três cenários de conectividade, foram projetados para o futuro e combinados com as projeções dos ENM. Demonstra-se que a perda de conectividade, associada à movimentação da janela climática, terá reduzirá a área de distribuição do rato-de-Cabrera. Finalmente, o ‘MetaLandSim’ é usado para testar a hipótese de que níveis intermédios de perturbação da paisagem podem beneficiar algumas espécies. Foram usadas 54 espécies virtuais com diferentes características ecológicas demonstrando que espécies com dispersões médias a elevadas e preferência por manchas nos primeiros estados da sucessão beneficiam de níveis intermédios de dinamismo. Este estudo fornece informação relevante para melhorar previsões da persistência das metapopulações e das dinâmicas das áreas de distribuição sob diversos cenários de alterações ambientais

Suojelualueverkosto muuttuvassa ilmastossa – esiselvitys

Suomen ilmasto tulee muuttumaan jo lähivuosikymmeninä. Vuotuisen sademäärän on ennustettu lisääntyvän Suomessa 8 - 20 % ja lämpötilojen nousevan Suomessa 1,5 - 2 kertaa nopeammin kuin maapallolla keskimäärin, eli 2 - 6 astetta vuosisadan loppuun mennessä. Ilmastonmuutoksella ennakoidaan olevan merkittävä vaikutus suojelualueverkoston kykyyn turvata luonnon monimuotoisuutta. Luonnonsuojelu onkin huomattavien haasteiden edessä, sillä suojelualuesuunnittelussa ei ole yleensä varauduttu voimakkaisiin muutoksiin. Näiden haasteiden hallintaa vaikeuttaa myös ilmastonmuutoksen vaikutusten ennustamiseen liittyvät epävarmuudet. Suojelualueverkoston riittävyyttä ja kykyä säilyttää luonnon monimuotoisuus muuttuvassa ilmastossa voidaan arvioida eliölajiston, luontotyyppien ja ekosysteemien levinneisyyden ja ekologisten piirteiden, sekä ilmaston-muutoksen voimakkuuden alueellisten erojen ja suojelualueiden biogeofysikaalisten tekijöiden avulla. Tietoa tarvitaan erityisesti lajien ja luontotyyppien herkkyydestä ilmastonmuutokselle. Maisematason arvioinneissa voidaan käyttää yleisluonteisia kriteereitä kuten suojelualueiden määrä ja puskurialueiden laajuus, ekologisten käytävien esiintyminen ja maisemamatriisin soveltuvuus lajien leviämiseen. Itse suojelualueita voidaan arvioida niiden koon, maanpinnan muotojen ja elinympäristöjen monipuolisuuden, pienilmastollisten refugioiden esiintymisen sekä paikallisen ilmastonmuutoksen voimakkuuden perusteella. Suojelualueverkoston arvioinnissa tulee huomioida myös verkoston ulkopuolisen maankäytön vaikutuksia, sillä suojelualueiden ulkopuolella monimuotoisuutta turvaavilla toimilla voidaan edistää verkoston sopeutumista ilmastonmuutoksen vaikutuksiin. Ilmastonmuutokseen sopeutumiseen liittyvät toimet (esimerkiksi talousmetsissä) ja muu maankäyttö voivat toisaalta johtaa luonnon monimuotoisuudelle haitallisiin vaikutuksiin suojelualueverkoston sisällä ja laajemminkin. Toimivien ekologisten yhteyksien säilyttäminen on keskeistä muuttuvassa ilmastossa. Suomen lajistoon tulee täydennystä valtion rajojen ulkopuolelta. Siksi olisi tärkeää selvittää, kuinka hyvin erilaiset rajat ylittävät ekologiset käytävät, kuten Fennoskandian vihreä vyöhyke, toimivat lajien liikkumisreitteinä muuttuvassa ilmastossa. Luonnonsuojelualueverkosto on merkittävä ekosysteemipalvelujen tuottaja muuttuvassa ilmastossa. Yksi tärkeä suojelualueiden tuottama ekosysteemipalvelu on toimiminen hiilivarastona ja hiilinieluna. Siten suojelualueverkostolla on merkitystä ilmastonmuutoksen hillinnässä ja siihen sopeutumisessa, mutta tätä asiaa ei ole aiemmin tutkittu Suomessa

Uncertainty, errors and virtual ecology: using artificial data to improve species distribution models

With the growing pressures exerted by anthropogenic activities (e.g. land-use changes, habitat fragmentation, greenhouse gas emissions) and environmental changes (e.g. climate change, biological invasions), biodiversity is being threatened worldwide. It is therefore important to sufficiently understand which factors influence the distribution and composition of species assemblages, develop tools allowing us to accurately predict them under current and future environmental conditions. Species distribution models (SDMs) are especially useful to tackle these challenges since they allow the modelling of the distribution of species and their assemblages at different spatial and temporal scales. This is done by simply relating species observations with environmental conditions where they occur. However, different factors (e.g. sample size, modelling technique) and errors/bias (i.e. false presences/absences) were shown to affect the prediction accuracy of single species and assemblage SDMs (i.e. S-SDMs). SDMs can also provide biased projections when predicting to regions or time periods with environmental conditions outside the range of data used for model calibration (i.e. model transferability) or when that data doesn’t capture the full conditions occupied by the species (i.e. truncated datasets). While the majority of SDMs use real species data, it is important to assess their accuracy by having complete control of the data and factors influencing species distributions, hence the use of virtual or simulated species. In the first chapter of my thesis, I used virtual species data to test SDM/S-SDMs and determine the degree to which different types and levels of errors in species data (i.e. false presences or absences) affect the predictions of individual species models, and how this is reflected in metrics that are frequently used to evaluate the prediction accuracy of SDMs. I found that interpretation of models’ performance depended on the data and metrics used to evaluate them, with model performance being more affected by false positives. In the second chapter, I assessed how different factors (sample size, sampling method, sampling prevalence, modelling technique and thresholding method) affect the prediction accuracy of S-SDMs. I found that prediction accuracy is mostly affected by modelling technique followed by sample size and that a ‘plot-like’ sampling method is recommended when sampling species data (i.e. best approximation of the species’ true prevalence). In my third chapter I tested the potential causes that increasingly truncated datasets have on the predictive accuracy of species assemblages and if the variables used to calibrate the models also influence that accuracy, finding that the degree of truncation has more influence on species with wide realized niches. Finally, on my last main chapter, I tested and compared how accurate different modelling strategies are at predicting species assemblages under current and future climatic conditions, assessing their transferability. I found that when using presence/pseudo-absence data, all the strategies failed to predict accurate species assemblages, being better when presence-absence data is used (under current environmental conditions). -- La biodiversité est actuellement mondialement menacée par l’augmentation de la pression due aux activités anthropiques (p. ex. changement dans l’utilisation du territoire, fragmentation des habitats, émission de gaz à effet de serre) et aux changements environnementaux (p. ex. changements climatiques, invasions biologiques). Il est donc capital de comprendre les facteurs influençant la distribution et la composition des assemblages d’espèces ainsi que de développer des outils pour les prédire précisément autant dans des conditions environnementales actuelles que future. Les modèles prédictifs de distribution (MPDs) sont des outils particulièrement utiles pour appréhender ce genre de challenges, car ils permettent de modéliser la distribution des espèces ainsi que leurs assemblages à différentes échelles spatiales et temporelles. Cela peut se faire en reliant des observations d’espèces avec les conditions environnementales dans lesquelles elles se trouvent. Cependant, il a été montré que différent facteurs (p. ex. taille d’échantillonnage, techniques de modélisation) et erreur/biais (c.-à-d. fausses présences/absences) peuvent affecter la qualité des prédictions obtenues lors de la modélisation prédictive de la distribution de simples espèces (MPD) et d’assemblages (S-SDMs). Les MPDs peuvent aussi créer des projections biaisées lorsqu’ils prédisent dans des régions ou des périodes de temps qui possèdent des conditions environnementales en dehors de la gamme de données utilisées lors de la calibration du modèle (c.-à-d. transférabilité du modèle) ou quand les données ne représentent pas l’entier des conditions occupées par l’espèce (c.-à-d. jeu de données tronqué). Bien que la majorité des MPDs utilisent des données d’espèces réelles, il est important de pouvoir évaluer leurs précisions en ayant le contrôle complet des données ainsi que des facteurs pouvant influencer la distribution des espèces. Seul l’utilisation d’espèces virtuelles ou simulées permet d’obtenir ce contrôle total. Dans le premier chapitre de ma thèse, j’ai utilisé des données d’espèces virtuelles afin de déterminer, à l’aide de MPDs/S-SDMs, dans quelle mesure différents types et niveaux d’erreurs dans les données d’espèces (c.-à-d. fausses présences ou absences) pouvaient affecter les prédictions obtenues. J’ai aussi cherché à comprendre comment cela se reflète sur les métriques habituellement utilisées pour évaluer la qualité des prédictions de ces MPDs. J’ai découvert que l’interprétation des performances des modèles dépends des données et des métriques utilisées pour les évaluer. Cette performance est particulièrement affectée par les faux positifs. Dans le second chapitre, j’ai évalué comment différents facteurs (taille d’échantillonnage, méthode d’échantillonnage, prévalence d’échantillonnage, technique de modélisation et méthode de définition des seuils) affectent la qualité des prédictions obtenues à l’aide de S- SDMs. J’ai trouvé que la qualité des prédictions est principalement affectée par les techniques de modélisation, suivie par la taille de l’échantillonnage. Une méthode d’échantillonnage dite « plot-like » est recommandée lors de la récolte de données (c.-à-d. qu’elle donne la meilleure approximation de la réelle prévalence de l’espèce). Dans mon troisième chapitre, j’ai testé quels pouvaient être les potentiels effets de l’utilisation de jeux de données de plus en plus tronqués sur la qualité des prédictions des assemblages d’espèces ainsi que l’influence des variables utilisées lors de la calibration. Il s’avère que le degré de troncature a plus d’effet sur les espèces ayant une large niche réalisée. Finalement, dans mon dernier chapitre, j’ai testé différentes stratégies de modélisation puis j’ai comparé leur aptitude à prédire des assemblages d’espèces dans des conditions présentes et futures pour évaluer leur transférabilité. J’ai découvert que lors de l’utilisation de données de présences/pseudo-absences, toutes les stratégies échouaient à prédire de manière précise les assemblages. L’utilisation de données de présence/absences a permis, quant à elle, d’obtenir de meilleurs résultats, principalement dans des conditions environnementales présentes