Browsing by Author "Yesson, Chris"
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- Macroalgae niche modelling : a two-step approach using remote sensing and in situ observations of a native and an invasive AsparagopsisPublication . Casas, Enrique; Fernandez, Marc; Gil, Artur José Freire; Yesson, Chris; Prestes, Afonso L.; Moreu‐Badia, Ignacio; Neto, Ana I.; Arbelo, ManuelWe are facing a global loss of biodiversity due to climate change. This will lead to unpredictable changes in ecosystems, affecting the goods and services they provide introduction of non-indigenous marine species. This represents one of the major threats to marine biodiversity and therefore, there is a strong need to assess, map and monitor these alien species. The appearance of non-indigenous species is especially dangerous in fragile ecosystems and it is of great importance to better understand the invasion mechanisms of these invasive species. This is the case for invasive alga Asparagopsis armata, present in the Azores Archipelago. In this study we propose a methodology to define the realized ecological niche of this invasive alga, alongside the native Asparagopsis taxiformis, to understand better its distribution and potential impact on native communities and ecosystem services. These objectives comply with the EU Biodiversity strategy for 2020 goals and the need to map and assess ecosystems and their services. The lack of reliable high-resolution data makes this a challenging task. Within this scope, we propose a combination of Remote Sensing, Unmanned Aerial Vehicle based imagery together with in-situ field data to build ecological niche modelling approaches as a cost-effective methodology to identify and characterize vulnerable marine ecosystems. Our results show that this combination can help achieve monitoring, leading to a better understanding of ecological niches and the consequences of non-indigenous species invasion in fragile ecosystems, like small islands, when faced with limited data.
- To be or not to be : the role of absences in niche modelling for highly mobile species in dynamic marine environmentsPublication . Fernandez, Marc; Sillero, Neftali; Yesson, ChrisSpecies distribution models are valuable tools for conservation management. However, there remain challenges in developing and interpreting these models in the marine environment, such as the nature of the species used for the modelling process. When working with mobile species in dynamic environments, lack of observation is usually interpreted as an observation of absence, which can result in the introduction of biases by methodological (false) absences. Here, we explore the role of absences when modelling marine megafauna distributions. To better understand how the use of absences (or equivalent) affects the niche modelling algorithms, we used a set of 20 virtual species with different relations to the habitat (generalist static, specialist static, generalist dynamic and specialist dynamic) with different encounter rates. We tested six different modelling techniques divided into three distinct groups: presence-only, presence-background and presence-absence. We compared the outputs of the models using traditional validation metrics and overlap metrics in the geographical and environmental spaces. Algorithms characterized the ecological niche for the simulated species differently. Approaches using background data generally outperformed the other methods, suggesting that the non-observation of a species in a given location and time should not be considered as an absence. A very intense (practically unrealistic) sampling schema would be required to obtain a genuine unbiased absence when working with these species and habitats. For highly mobile species, a precautionary approach would be to consider the non-observation of a species as part of the background (a sample of the conditions available in the study area) rather than an absence. A good starting point would be to use presence-background models, complemented with presence-absence and/or presence-only models, comparing outputs from the different algorithms tested in the geographic and environmental space. Improving model performance for highly mobile marine species should lead to better-informed decision making for conservation.