Cristina Llopis-Belenguer

Host–parasite interactions are influenced by present and past eco‐evolutionary interactions and the local environment. An ecological community defines the potential host range of each parasite and the potential parasite diversity of each host species. Past and present processes translate potential to realised interaction niches of parasite and host species. Host–parasite interactions are antagonistic, which may slow the saturation of their interaction niches. Intervality, a property of bipartite networks, measures saturation of interaction niches. Intervality of a community increases as the interaction niches of species of one guild (e.g. hosts) become saturated for their interactions with another guild (e.g. parasites). Characteristics driving intervality in host and parasite communities are largely unknown, as well as the effect of environmental change on intervality of these communities. In our study, we assess if the characteristics ‘phylogenetic relatedness' and ‘overlap in ecological interactions' explain intervality of rodent host–helminth parasite communities. In addition, we contrast intervality of these communities from habitats that differ in their history of human‐driven modification. We performed the analyses for the interaction niches of both parasites and hosts, independently. Our results indicated that host and parasite communities were non‐interval or significantly less interval than expected by chance. Phylogenetic relatedness and overlap in ecological interactions did not explain the maximum values of intervality. We speculate that antagonistic coevolution in host–parasite communities may hinder communities to reach saturation, which would explain why it is difficult to find the characteristics that explain intervality of a community. Interestingly, intervality of the interaction niche of parasites (host range) increased with habitat modification (i.e. saturation increased), whereas intervality of the interaction niche of hosts (parasite diversity) decreased as habitat modification increased. These opposite trends suggest that interaction niches of parasites and hosts respond differently to habitat modification.

Bipartite network analysis is a powerful tool to study the processes structuring interactions in ecological communities. In applying the method, it is assumed that the sampled interactions provide an accurate representation of the actual community. However, acquiring a representative sample may be difficult as not all species are equally abundant or easily identifiable. Two potential sampling issues can compromise the conclusions of bipartite network analyses: failure to capture the full range of interactions (sampling completeness) and use of a taxonomic level higher than species to evaluate the network (taxonomic resolution). We asked how commonly used descriptors of bipartite antagonistic communities (modularity, nestedness, connectance, and specialization [H₂′]) are affected by reduced host sampling completeness, parasite taxonomic resolution, and their crossed effect, as they are likely to co‐occur. We used a quantitative niche model to generate weighted bipartite networks that resembled natural host–parasite communities. The descriptors were more sensitive to uncertainty in parasite taxonomic resolution than to host sampling completeness. When only 10% of parasite taxonomic resolution was retained, modularity and specialization decreased by ~76% and ~12%, respectively, and nestedness and connectance increased by ~114% and ~345% respectively. The loss of taxonomic resolution led to a wide range of possible communities, which made it difficult to predict its effects on a given network. With regards to host sampling completeness, standardized nestedness, connectance, and specialization were robust, whereas modularity was sensitive (~30% decrease). The combination of both sampling issues had an additive effect on modularity. In communities with low effort for both sampling issues (50%–10% of sampling completeness and taxonomic resolution), estimators of modularity, and nestedness could not be distinguished from those of random assemblages. Thus, the categorical description of communities with low sampling effort (e.g., if a community is modular or not) should be done with caution. We recommend evaluating both sampling completeness and taxonomic certainty when conducting bipartite network analyses. Care should also be exercised when using nonrobust descriptors (the four descriptors for parasite taxonomic resolution; modularity for host sampling completeness) when sampling issues are likely to affect a dataset.

Bipartite network analysis is a powerful tool to study the processes structuring interactions in antagonistic ecological communities. In applying the method, we assume that the sampled interactions provide an accurate representation of the actual community. However, acquiring a representative sample may be difficult as not all species are equally abundant or easily identifiable. Two potential sampling issues can compromise the conclusions of bipartite network analyses: failure to capture the full range of interactions of species (sampling completeness) and failure to identify species correctly (taxonomic resolution). These sampling issues are likely to co-occur in community ecology studies. We asked how commonly used descriptors (modularity, nestedness, connectance and specialisation (H ₂ ′)) of bipartite communities are affected by reduced host sampling completeness, parasite taxonomic resolution and their crossed effect. We used a quantitative niche model to generate replicates of simulated weighted bipartite networks that resembled natural host-parasite communities. The combination of both sampling issues had an additive effect on modularity and nestedness. The descriptors were more sensitive to uncertainty in parasite taxonomic resolution than to host sampling completeness. All descriptors in communities capturing less than 70% of correct taxonomic resolution strongly differed from correctly identified communities. When only 10% of parasite taxonomic resolution was retained, modularity and specialisation decreased ∼0.3 and ∼0.1-fold respectively, and nestedness and connectance changed ∼0.7 and ∼3.2-fold respectively. The loss of taxonomic resolution made the confidence intervals of estimates wider. Reduced taxonomic resolution led to smaller size of the communities, which emphasised the larger relative effect of taxonomic resolution on smaller communities. With regards to host sampling completeness, connectance and specialisation were robust, nestedness was reasonably robust (∼0.2-fold overestimation), and modularity was sensitive (∼0.5-fold underestimation). Nonetheless, most of the communities with low resolution for both sampling issues were structurally equivalent to correctly sampled communities (i.e., more modular and less nested than random assemblages). Therefore, modularity and nestedness were useful as categorical rather than quantitative descriptors of communities affected by sampling issues. We recommend evaluating both sampling completeness and taxonomic certainty when conducting bipartite network analyses. We also advise to apply the most robust descriptors in circumstances of unavoidable sampling issues. Open Research statement: we provide permanent and open access links to data sources and replication code in Appendix S1.

Introduced species can alter the dynamics and structure of a native community. Network analysis provides a tool to study host–parasite interactions that can help to predict the possible impact of biological invasions or other disturbances. In this study, we used weighted bipartite networks to assess differences in the interaction patterns between hosts and helminth parasites of native (Sea of Japan) and invasive (Black Sea and Sea of Azov) populations of Planiliza haematocheilus (Teleostei: Mugilidae). We employed three quantitative network descriptors, connectance, weighted nestedness and modularity, to gain insight into the structure of the host–parasite networks in the native and invaded areas. The role of parasite species in the networks was assessed using the betweenness centrality index. We analyzed networks encompassing the whole helminth community and subsets of species classified by their transmission strategy. The analyses were downscaled to host individual-level to consider intraspecific variation in parasite communities. We found significant differences between networks in the native and invaded areas. The latter presented a higher value of nestedness, which may indicate a co-occurrence between parasite species with many connections in the network and species with fewer interactions within the same individual-host. In addition, modularity was higher in the native area’s networks than those of the invaded area, with subgroups of host individuals that interact more frequently with certain parasite species than with others. Only the networks composed of actively transmitted parasites and ectoparasites did not show significant differences in modularity between the Sea of Azov and the Sea of Japan, which could be due to the introduction of a part of the native community into the invaded environment, with a lower diversity and abundance of species. We show that network analysis provides a valuable tool to illuminate the changes that occur in host–parasite interactions when an invasive species and its parasite community are introduced into a new area.

Most species in ecological communities are rare, whereas only a few are common. This distributional paradox has intrigued ecologists for decades but the interpretation of species abundance distributions remains elusive. We present Fuzzy Quantification of Common and Rare Species in Ecological Communities (FuzzyQ) as an R package. FuzzyQ shifts the focus from the prevailing species‐categorization approach to develop a quantitative framework that seeks to place each species along a rarity‐commonness gradient. Given a community surveyed over a number of sites, quadrats, or any other convenient sampling unit, FuzzyQ uses a fuzzy clustering algorithm that estimates a probability for each species to be common or rare based on abundance–occupancy information. Such a probability can be interpreted as a commonness index ranging from 0 to 1. FuzzyQ also provides community‐level metrics about the coherence of the allocation of species into the common and rare clusters that are informative of the nature of the community under study. The functionality of FuzzyQ is shown with two real datasets. We demonstrate how FuzzyQ can effectively be used to monitor and model spatiotemporal changes in species commonness, and assess the impact of species introductions on ecological communities. We also show that the approach works satisfactorily with a wide range of communities varying in species richness, dispersion and abundance currencies. FuzzyQ produces ecological indicators easy to measure and interpret that can give both clear, actionable insights into the nature of ecological communities and provides a powerful way to monitor environmental change on ecosystems. Comparison among communities is greatly facilitated by the fact that the method is relatively independent of the number of sites or sampling units considered. Thus, we consider FuzzyQ as a potentially valuable analytical tool in community ecology and conservation biology.

1. Most species in ecological communities are rare whereas only a few are common. This distributional paradox has intrigued ecologists for decades but the interpretation of species abundance distributions remains elusive. 2. We present Fuzzy Quantification of Common and Rare Species in Ecological Communities (FuzzyQ) as an R package. FuzzyQ shifts the focus from the prevailing species-categorization approach to develop a quantitative framework that seeks to place each species along a rare-commonness gradient. Given a community surveyed over a number of sites, quadrats, or any other convenient sampling unit, FuzzyQ uses a fuzzy clustering algorithm that estimates a probability for each species to be common or rare based on abundance-occupancy information. Such as probability can be interpreted as a commonness index ranging from 0 to 1. FuzzyQ also provides community-level metrics about the coherence of the allocation of species into the common and rare clusters that are informative of the nature of the community under study. 3. The functionality of FuzzyQ is shown with two real datasets. We demonstrate how FuzzyQ can effectively be used to monitor and model spatio-temporal changes in species commonness, and assess the impact of species introductions on ecological communities. We also show that the approach works satisfactorily with a wide range of communities varying in species richness, dispersion and abundance currencies. 4. FuzzyQ produces ecological indicators easy to measure and interpret that can give both clear, actionable insights into the nature of ecological communities and provides a powerful way to monitor environmental change on ecosystems. Comparison among communities is greatly facilitated by the fact that the method is relatively independent of the number of sites or sampling units considered. Thus, we consider FuzzyQ as a potentially valuable analytical tool in community ecology and conservation biology.

The recent finding of gas embolism (GE) and decompression sickness (DCS) in loggerhead sea turtles (Caretta caretta) in the Mediterranean Sea challenged the conventional understanding of marine vertebrate diving physiology. Additionally, it brought to light a previously unknown source of mortality associated with fisheries bycatch for this vulnerable species. In this paper, we use ultrasonography to describe GE in a leatherback sea turtle (Dermochelys coriacea), a green sea turtle (Chelonia mydas), and an olive ridley sea turtle (Lepidochelys olivacea) from accidental capture in a gillnet, bottom trawl, and pair-bottom trawl, respectively. This is the first description of this condition in these three species worldwide. These cases of GE suggest that this may be a threat faced by all sea turtle species globally.