Ecology

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To our knowledge, this is the only work done on the terrestrial biodiversity status in the direct vicinity of a limited duration volcanic eruption. In this contribution, we document and assess the impact on the main plant and animal groups within the ecosystems during a volcanic eruption (Table 1). While some groups were clearly disadvantaged: ferns and herbaceous plants as well as invertebrates and saurians (lizards and geckos); other groups such as conifers and woody shrubs showed better resilience, as did the birds.This study is particularly important because of its location in a Mediterranean biodiversity hotspot13,14, harbouring a unique ecosystem of oceanic island organisms (38% of the Canary archipelago endemicity). Islands indeed exhibit a disproportionate amount of the world’s biodiversity but unfortunately a high number of extinctions have also occurred there14. The biodiversity in the south of the island is poorer than in the north. This is probably explained in part by the relatively frequent volcanic activity featuring seven major eruptions since 1585, including this one in 2021 (see15), which led to alternating destruction and neo-colonization processes.Concerning the flora, the Canary pine forest was the most affected ecosystem and vegetation type, as it is dominant in the vicinity of the new volcanic vents. The southern slopes of this forest were the most disturbed area due to the location of the volcano, combined with the prevailing northeasterly trade winds (Fig. 1). Tephra fallout and sulphurous gases were the main factors that affected the pine forest, over a vast surface area. Furthermore, the local xerophytic and thermophilous habitats also lost much of their surface area. In contrast to the pine forest, this drastic reduction was caused by the progressive downslope expansion of the lava flows.The Canary Island pine was thus notably affected by tephra fall, sulphuric acid aerosol12, and short episodes of acid rain. However, this conifer shows high resistance to temperature, confirming its great adaptation to volcanic events16, which is probably also one of the keys to its resistance to the more frequent present-day wildfires17. This pine species has evolved among volcanoes for the last 13 My16 and has adapted successfully to high temperatures. Moreover, thunderstorms with lightning occur in the Canaries together with abundant rainfall; consequently, wild forest fires should presumably not have been so frequent in the island’s past, before human colonization. In this habitat it is also remarkable that epiphytic lichens (U. articulata) apparently resisted on the pines until the 12th week, considering their high sensitivity to anthropogenic pollution18.The life cycle of flowering plants was drastically disrupted due to all the above factors, with great impact on foliage, photosynthesis, and growth. However, soil changes due to the deposition of tephra and its lixiviation by rain is one of the most dramatic factors affecting plants and a long-term impact of volcanic eruptions19. The nearest individuals to the crater were most directly affected by intense tephra falls and concentrated volcanic gases (SO2, HCl, HF, CO2). However, plants located in the nearest 200 m to the lava flows but at more than 2 km from the crater were presumably more disturbed by the high temperature of the slow-cooling lava and its lesser gas emissions.Large woody plants exhibited a better frequency of survival than smaller ones in the face of this extreme stress (Table S1 and19). In the Hekla area (Iceland), most trees have thickened trunks, indicating that those trees that survive have had a long life subjected to frequent volcanic damage19. Secondary woodiness of island plants (sensu20) has been traditionally related to drought20,21, ecological shift22 or a counter-selection of inbreeding depression in founding island populations23. However, this adaptation also favours the resistance of many shrubby plants to high temperatures close to craters and lava flows but primarily their resistance to the intense tephra falls that affect a much larger area. In addition, plant and stem height plays a fundamental role in overcoming the deep layers of deposits. This latter effect was particularly important up to 2.5 km from the crater (tephra thickness  > 30 cm) (Figs. 1 and 2), as the herbaceous plants were completely buried, sometimes to more than 1.5 m depth. Therefore, the seed bank has also probably been rendered largely non-functional. However, deposits were recorded over almost the whole island, indicating that longer lasting or more intense eruptions would severely affect an even larger area. Such events have been hitherto ignored in the intensely discussed “island woodiness” debate21,23,24,25,26,27. We found surviving populations of endemic woody taxa heavily impacted by tephra deposits close to lava flows, across a wide range of genera such as Rumex (R. lunaria), Echium (E. brevirame), Euphorbia (E. lamarckii, E. canariensis and E. balsamifera), Aeonium (A. davidbramwellii), Rubia (R. fruticosa), Schizogyne (S. sericea), Carlina (C. falcata) or Sonchus (S. hierrensis) (Table S2), which coincide with the general list of woody Canary plants20. Most members of these genera in other ecosystems on continents are mainly herbaceous. As such eruptions and their impacts due to ash depositions are frequent events on volcanic islands, e.g. several times within a century on La Palma, this is a “frequent” selective process at evolutionary time scales.With regard to the fauna, the invertebrate community collapsed during the first two weeks (Table S2), probably due to rapid deterioration of the growth state of plants. These changes in the invertebrates were caused by the tephra contacting the cuticular lipid layer28 and water loss due to tegument abrasion29. In this period, many insect pests (especially whitefly pupae) in banana plantations (farmers’ observations) were drastically reduced. This sudden decrease in insect populations affected the whole food web and probably caused part of the ecological collapse of saurian and some passerine communities30. In the case of lizards, smaller individuals seem to resist the adverse conditions better than large ones, as observed in other eruptions3. This could be linked to their lower food requirements and greater ease in finding refuges. Loss of body condition in lizards post-eruption has been recorded and negatively affects reproduction quality31. However, some lizards have shown a good ability to find food in the tephra substrate32. We found abundant tephra particles in some vertebrate droppings (lizards, birds, and mammals) during the eruption, probably involuntarily ingested. At least in bats, ingestion during feeding produces physiological stress that is likely related to baldness, high ectoparasite loads or possible mineral deficiencies33.As described in the Canary Islands, some passerines show high fidelity to their territories (see34). During the eruption, Sardinian warblers (Curruca melanocephala) maintained their territories until the imminent arrival of lava flows. Larger birds (kestrels F. tinnunculus, ravens C. corax and buzzards B. buteo) were well able to continue flying in the areas surrounding the crater. Furthermore, some cases like F. tinnunculus showed great feeding plasticity in the first couple of weeks. At least six times, kestrels tried to catch birds (especially small passerines and doves), contrary to their usual diet based on abundant lizards and insects35. Widening of trophic niches in island organisms has traditionally been interpreted as linked to disharmony in island ecosystems36,37,38. However, this plasticity is tremendously beneficial in ecological catastrophes, where food becomes exceptionally scarce. In the case of bats, their flight is limited by the delicate structure of their patagium, which can be damaged by the frequent pyroclastic tephra fall. Furthermore, scarcity of insects in the first few kilometres from the crater probably led to their displacement to other more distant and richer food resource zones.As we learned from the movement capacity of the vertebrate animals that still inhabited the affected area, those with greater mobility, birds and bats, resisted the eruptive process much better than those with less mobility, e.g. saurians.Lastly, during this destructive event on La Palma, we had the opportunity to increase our knowledge of how ecological-evolutionary adaptations have favoured the survival of insular organisms. Such responses are traditionally mentioned in the context of island biology. As already mentioned, one of the most interesting findings verifies the remarkable adaptation of Canary Island pine trees (P. canariensis) to volcanism (see16), including extremely harsh ecological conditions. Other insular trends related to the prevalence of woodiness in insular flowering plants20,21, or the high trophic plasticity of some vertebrates on oceanic islands36, have not previously been associated with their potential evolution along with volcanic processes. However, such evolutionary adaptations most likely played an important role in the survival of plants and animals affected by the volcano. For this reason, it is worth considering and debating whether these previously mentioned evolutionary processes are in fact also linked to repeated volcanic episodes on oceanic islands. More

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Data processingIn 2019, the CETUS data spanning between 2012 and 2017 was published open access through the Flanders Marine Institute (VLIZ) IPT portal and distributed by EMODnet and OBIS, in a first version of the CETUS dataset9. The data collected between 2018 and 2019 was prepared as the 2012–2017 data9. Methods for photographic verification/validation and to evaluate the MMOs experience were applied (see below), in order to include new variables on data quality in an updated version of the dataset. Currently, the CETUS dataset is updated, with a 2nd version available10. It comprises data from 2012 to 2017, with the following two new columns on the observers’ experience: “most experienced observer” and “least experienced observer”; and a new column associated with validation of the sightings’ identifications: “photographic validation”. The results here presented correspond to the analysis of the data from 2012 to 2019, and the open-access dataset will soon be further updated with the 2018–2019 data.Photographic verificationAll the former MMOs who have integrated the CETUS Project, between 2012 and 2019, were contacted and asked to provide any available photographic or video records of cetaceans collected during their on board periods. The collection of sighting’s images was not a requirement of the CETUS protocol, and so these records were obtained opportunistically, with availability and quality depending on several factors: observers on board having personal cameras, camera quality, intention of the observer taking the photograph (e.g., for aesthetic or identification purposes).The images obtained were organized in a folder hierarchy from the year to the day of recording. However, not all the images had metadata up to the day of recording, so these were inserted into the most appropriate hierarchy-level of the folder organization. For each set of records corresponding to a single-taxon sighting, the photos/videos with the better quality or framing (i.e., that allowed for an easier species identification) were selected for that sighting. The remaining photos/videos were only consulted in case of doubt (e.g., to look for additional details that could help with the identification).Verification consisted of the process of matching the photographic/video records with the dataset sighting registers. Whenever possible and ideally, the file metadata was used for the process. However, often, the date and/or time of the file metadata were wrong, non-existent, or in different time zones. In these cases, a conservative methodology was applied using all available information to match as many sightings as possible. An estimation of time lag was attempted (based on, at least, two obvious matches between photographs/videos and dataset registers, e.g., unique sighting of the day, close to the boat, easy/obvious identification). When not possible, further evaluation consisted in assessing whether the sighting and image record was too obvious, and accounting for unique complementary information on the sighting (e.g., the number of animals or the side of the sighting were unique for that day and/or for that species/group).Photographic validationAfter the verification process, the validation of the matched records was carried out, to confirm or correct the species identification of sightings in the 1st version of the CETUS dataset (i.e., reported by the MMOs on board). The validation approach involved, for more dubious identification through the photo/video records, the discussion between four experienced observers of the CETUS team. In cases where no consensual agreement was achieved, an external expert on cetacean identification was also consulted. Identifications made through the photographic/video records required 100% certainty, and these were then compared with the cetacean identifications provided in the 1st version of the CETUS dataset. Then, the occurrence records with originally misidentifications of cetaceans, as well as those records where validation allowed to achieve an identification to a lower taxon, were corrected in the 2nd version of the dataset (i.e., a delphinid sighting validated as common dolphin, will now appear as common dolphin). A new column “photographic validation” was added to the dataset with the following categories: “yes” (i.e., validated with photograph/video), “no” (i.e., not validated with photograph/video), and “to the family” (i.e., validation only to the family taxon).For further analysis, specifically for the model process on the identification success (see below), registers were considered “completely validated” if it was possible to complete the photographic/video identification process up to the species level (then, differentiating if the original identification from the MMOs was or not correct). For Globicephala sp. and Kogia sp., validation to the genus was considered complete, since the species from both genera are visually hardly differentiated, especially at sea.Creating a data quality criteria: evaluating MMOs experienceQuality criteria were created to evaluate the MMOs experience based on the information collected from their curricula vitae (CVs) (alumni MMOs provided as many CVs as the years of their participation in CETUS). The following quality criteria were considered: (i) the experience at sea, (ii) the experience with cetaceans’ ID, (iii) the number of species they have worked with, and (iv) the experience working with the CETUS Project protocol. Each of these quality criteria was ranked from 0 to 5, and then these were summed to generate an evaluation score, on a scale of 0 to 20, attributed to each MMO (Table 4).Table 4 Quality criteria for MMOs evaluation.Full size tableThe MMOs evaluations were computed for each cruise (i.e., the trip from one port to another), considering the experience of the MMOs based on the CV obtained for that year, plus the experience acquired during CETUS participation in previous cruises that year. Since most of the times, the team of observers on board each cruise was constituted by two MMOs, two final evaluation scores were attributed to each cruise in the 2nd version of the CETUS dataset, into two new columns: “most experienced observer” and “least experienced observer”. On rare occasions where there is only one observer on board that cruise, only the evaluation of the single observer was included under the column “most experienced observer”, leaving the column “least experienced observer” as “NULL”. To investigate the experience of MMOs on board, both individually and cumulative (LEO + MEO), the combination of the score values was computed by cruise. These were then trimmed to unique combinations of evaluation scores.The names of observers, previously presented in the online dataset for each cruise, were removed for anonymity purposes, as there is now ancillary information regarding their experience.Model fittingTwo Generalized Additive Models (GAM) were fitted to assess bias on the number of sightings recorded per survey and on the identification success of cetacean species. Details for each model are presented below. Both models were fitted in R (Version 4.1.0). Prior to modelling, Pearson correlations were calculated between all pairs of explanatory variables, considered for each model (see below), to exclude highly correlated variables, considering a threshold of 0.7524,25,28. Since the variables regarding MMOs’ experience were correlated (LEO or MEO correlated with cumulative and mean experience; and cumulative experience correlated with mean experience – Supplementary Fig. S3), these variables were not included in the first fitting stage (backward selection) but included later through forward selection (see below). Multicollinearity among explanatory variables was measured through the Variance Inflation Factor (VIF), with a threshold of 3 (Supplementary Tables S4)24,25,29. After removing the MMOs evaluation scores, no multicollinearity was observed, so all the other variables were kept for the first fitting stage.For model selection, a backward selection was applied to oversaturated models18,24,25,30,31. The Akaike Information Criterion (AIC) was used as a measure of adequation of fitness, choosing the model with the lowest AIC value at each step of the model fitting process, i.e., comparing nested models (larger model incorporating one more explanatory variable compared with the smaller model). If the AIC-difference between the two models was less than 2, an Analysis of Variance (ANOVA), through chi-square test, was used to check if the AIC-difference was significant24,25,32. If this difference was not statistically significant (p  > 0.05), the simplest model (smaller model) was kept. Through a forward selection process, the variables regarding the MMOs evaluation scores were added, one at a time, to the best model obtained in the previous backward selection. After comparing the models with each other (separate variables for LEO + MEO vs. Cumulative Evaluation vs Mean Evaluation), the best model, considering the AIC value, was kept. A final backward selection process was then applied.All GAMs were fitted with the “mgcv” package (https://cran.r-project.org/web/packages/mgcv) and a maximum of four splines (k = 4) was chosen to limit the complexity of smoothers describing the effects of the explanatory variables25,31. If a spline was close to linear (with estimated degrees of freedom of ~1), the smooth term was removed, and a linear function was fitted. To check for model quality, the “gam.check” function was used to verify the diagnostic plots and the adequacy of the number of splines (Supplementary Figs. S5 and S6). Existence of influential data points was assessed (with the threshold of 0.25 for the Hat values), as well as the correlation between model residuals and explanatory variables. In both final models, number of splines was adequate and there were no influential data points or clear correlation between residuals and explanatory variables (Supplementary Figs. S7 and S8)24,32.Bias modelling of number of sightingsTo assess the bias parameters on the number of sightings recorded per survey (i.e., a full day monitoring, from sunrise to sunset), the following detectability factors were considered as explanatory variables: weather conditions (i.e., the minimums and maximums of the sea state, wind state, and visibility), the experience of MMOs (i.e., the evaluation scores of the least and the most experienced observers, as well as the mean and cumulative evaluations of the MMOs experience) and kilometres sampled “on-effort” (i.e., periods of active survey). Sampling periods were divided into “On-effort” and “Off-effort” conditions, based on four meteorological variables: sea state (Douglas scale), wind state (Beaufort scale), visibility (measured in a categorical scale ranging from 0–10 and estimated from the distance to the horizon line and possible reference points at a known range, e.g., ships with an automatic identification system,  > 1000 km), and the occurrence of rain (see Supplementary Table S9)10. For the model fitting, only “on-effort” periods of sampling were considered. Given that the response variable was count data, a Poisson distribution was tested (with a log link function). Then, the resulting first oversaturated model was checked for overdispersion, through a Pearson estimator. Since it tested positive for overdispersion (φ = 1.99), a negative binomial distribution (with a log link function) was fitted.Bias modelling of identification successA binary response variable, based on the success in the species identification for each sighting, was generated, and a model with binomial distribution (with a logit link function) was fitted. As in the previous model, only “on-effort” records were used. The total number of non-successful identifications across the dataset (the 0 s of the model) was extrapolated from the proportion of wrong identifications obtained in the validation process. To calculate this proportion, only complete validated sightings registered “on-effort” were used. Proportions were computed and extrapolated to Odontoceti and Mysticeti, separately. This resulted in 78 non-successful identifications in delphinids, plus 17 misidentifications in baleen whales, i.e., a total of 95 “on-effort” sightings randomly selected from the dataset were defined as unsuccessful identifications (0 s in the response variable for model fitting). The remaining records were considered successful identifications (1 s in the response variable for model fitting). To assess the bias parameters on the identification success, the following independent variables were considered in the analysis: the group (i.e., Group A: Odontoceti sightings, excluding sperm whale (Physeter macrocephalus); and Group B: Mysticeti sightings, plus sperm whale), the size of the group (i.e., the best estimate of the number of animals in a sighting, from the observer’s perspective), sighting distance (i.e., a relative measure according to the scale of the binoculars), weather conditions (i.e., the sea state, wind state, and visibility at the time of each sighting), the experience of MMOs (i.e., the evaluation scores of least and most experienced observers, as well as the mean and cumulative scores of the MMOs teams). Group A and B were settled according to cetacean morphology. However, since sperm whales have closer similarities with Mysticeti species, they were also included in Group B21,33. This categorization was mostly based on body size, as this is likely the main factor, regarding the species morphology, influencing the identification. Group A is constituted by species with a medium length of less than 10 meters, while Group B includes the larger species over 10 meters (Mysticeti plus P. macrocephalus)33. Since in the CETUS Project, different binoculars have been used – with two different reticle scales – it was necessary to standardize binocular distances to the same scale. More

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