Assessment of the effects of climate change on littoral ecosystems

RESUMEN: El objetivo de esta tesis es evaluar los efectos del cambio climático en la distribución de macroalgas en Europa. Para ello, en primer lugar, se desarrolló una base de datos de variables ambientales con sentido ecológico, tanto para el periodo histórico como en escenarios de cambio climático (OCLE, http://ocle.ihcantabria.com). Los datos se recopilaron de fuentes con series temporales homogéneas y largas (1985-2015 y 2015-2099) para 16 variables relevantes en la distribución de macroalgas. Posteriormente se seleccionaron cinco especies representativas en Europa: Saccorhiza polyschides, Gelidium spinosum, Sargassum muticum, Pelvetia canaliculata y Cystoseira baccata. Su riesgo frente al cambio climático se evaluó mediante modelos de distribución de especies. Para reducir su incertidumbre en las proyecciones temporales, se desarrolló una metodología para seleccionar los algoritmos más transferibles en el tiempo, que se aplicó a los escenarios RCP 4.5 y 8.5 para el medio (2040-2069) y el largo plazo (2070-2099). Los resultados contribuyen a la mejora del conocimiento de la relación entre la ecología de las macroalgas y los factores ambientales, por un lado, y provee de herramientas a gestores e investigadores por el otro, para la construcción de modelos robustos con una metodología objetiva, reproducible, eficiente y aplicable a nivel mundial.ABSTRACT: The objective of this thesis is to assess the effects of climate change on macroalgae distribution in Europe. First, an ecologically-driven database of present and future drivers for marine life in Europe, the Open access database on Climate change effects on Littoral and oceanic Ecosystems (OCLE), was developed (http://ocle.ihcantabria.com). Data were gathered for homogeneous and long time series (1985-2015 and 2015-2099) for 16 variables relevant in seaweeds distribution. the Five species were selected as representative of European macroalgae: Saccorhiza polyschides, Gelidium spinosum, Sargassum muticum, Pelvetia canaliculata and Cystoseira baccata. To assess the risk due to climate change, species distribution models were selected as an appropriate tool. To reduce uncertainty in temporal extrapolation, a step-wise methodology to select the most transferable algorithms in time was developed. It was applied to RCPs 4.5 and 8.5 for the mid-term (2040-2069) and the long term (2070-2099). Results help to fill the gap in knowledge between seaweeds ecology and environmental drivers on the one-hand and between science and managers on the other, by paying particular attention to building robust models with objective, reproducible, globally applicable and efficient methodology.Esta tesis no habría sido posible sin el apoyo económico de la Fundación Instituto de Hidráulica Ambiental de la Universidad de Cantabria y el Ministerio de Economía y Competitividad (BES‐2016‐076434)

Predicting non-native seaweeds global distributions: The importance of tuning individual algorithms in ensembles to obtain biologically meaningful results

ABSTRACT: Modelling non-native marine species distributions is still a challenging activity. This study aims to predict the global distribution of five widespread introduced seaweed species by focusing on two mains aspects of the ensemble modeling process: (1) Does the enforcement of less complex models (in terms of number of predictors) help in obtaining better predictions? (2) What are the implications of tuning the configuration of individual algorithms in terms of ecological realism? Regarding the first aspect, two datasets with different number of predictors were created. Regarding the second aspect, four algorithms and three configurations were tested. Models were evaluated using common evaluation metrics (AUC, TSS, Boyce index and TSS-derived sensitivity) and ecological realism. Finally, a stepwise procedure for model selection was applied to build the ensembles. Models trained with the large predictor dataset generally performed better than models trained with the reduced dataset, but with some exceptions. Regarding algorithms and configurations, Random Forest (RF) and Generalized Boosting Models (GBM) scored the highest metric values in average, even though, RF response curves were the most unrealistic and non-smooth and GBM showed overfitting for some species. Generalized Linear Models (GLM) and MAXENT, despite their lower scores, fitted smoother curves (especially at intermediate complexity levels). Reliable and biologically meaningful predictions were achieved. Inspecting the number of predictors to include in final ensembles and the selection of algorithms and its complexity have been demonstrated to be crucial for this purpose. Additionally, we highlight the importance of combining quantitative (based on multiple evaluation metrics) and qualitative (based on ecological realism) methods for selecting optimal configurations.This work was funded by the National Plan for Research in Science and Technological Innovation from the Spanish Government 2017-2020 [grant number C3N-pro project PID2019-105503RB-I00] and co-funded by the European Regional Development’s funds. SS-V acknowledges financial support under a predoctoral grant from the Spanish Ministry of Education andVocational Training [grantnumber:FPU18/03573]. CH acknowledges the financial support from the Government of Cantabria through the Fénix Programme and under a postdoctoral grant from the University of Cantabria [grant number: POS-UC- 2020-07]. This work is part of the PhD project of SS-V

Perceived multiple stressor effects depend on sample size and stressor gradient length

Multiple stressors are continuously deteriorating surface waters worldwide, posing many challenges for their conservation and restoration. Combined effect types of multiple stressors range from single-stressor dominance to complex interactions. Identifying prevalent combined effect types is critical for environmental management, as it helps to prioritise key stressors for mitigation. However, it remains unclear whether observed single and combined stressor effects reflect true ecological processes unbiased by sample size and length of stressor gradients. Therefore, we examined the role of sample size and stressor gradient lengths in 158 paired-stressor response cases with over 120,000 samples from rivers, lakes, transitional and marine ecosystems around the world. For each case, we split the overall stressor gradient into two partial gradients (lower and upper) and investigated associated changes in single and combined stressor effects. Sample size influenced the identified combined effect types, and stressor interactions were less likely for cases with fewer samples. After splitting gradients, 40 % of cases showed a change in combined effect type, 30 % no change, and 31 % showed a loss in stressor effects. These findings suggest that identified combined effect types may often be statistical artefacts rather than representing ecological processes. In 58 % of cases, we observed changes in stressor effect directions after the gradient split, suggesting unimodal stressor effects. In general, such non-linear responses were more pronounced for organisms at higher trophic levels. We conclude that observed multiple stressor effects are not solely determined by ecological processes, but also strongly depend on sampling design. Observed effects are likely to change when sample size and/or gradient length are modified. Our study highlights the need for improved monitoring programmes with sufficient sample size and stressor gradient coverage. Our findings emphasize the importance of adaptive management, as stress reduction measures or further ecosystem degradation may change multiple stressor-effect relationships, which will then require associated changes in management strategies.Scenarios of Biodiversity and Ecosystem Services II programme, jointly supported by Belmont Forum, Biodiversa and the European Commission; Deutsche Forschungsgemeinschaft; Fénix Programme, Government of Cantabria; Augusto González de Linares, Programa de ayudas de la Universidad de Cantabria para contratos posdoctorales, grant number: POS-UC-2020-07; Environmental Protection Agency (Ireland); Marine Institute (Ireland); Basque Water Agency (URA) and Consorcio de Aguas Bilbao-Bizkaia (Spain); Cawthron Institute, New Zealand; Ministry for the Environment, New Zealand; Oranga Taiao Oranga Tangata research programme, New Zealand Ministry of Business, Innovation and Employment grant number MAUX1502; The project FRESHABIT LIFE IP (LIFE14/IPE/FI/023); USA National Science Foundation, Dimensions of Biodiversity Program (1831096); Irish Research Council Laureate Award (IRCLA/2017/112)