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104 results for “parasite ecology”
Explosive networking: the role of adaptive host radiations and ecological opportunity in a species-rich host-parasite assembly
<p>Dataset for Cruz-Laufer et al. (2021) Explosive networking: the role of adaptive host radiations and ecological opportunity in a species-rich host-parasite assembly.</p> <p><strong>Abstract: </strong>Many species-rich ecological communities emerge from adaptive radiation events. The effects of this explosive speciation on community assembly remain poorly understood. Here, we explore the well-documented radiations of African cichlid fishes and their interactions with the flatworm gill parasites <em>Cichlidogyrus </em>spp., including 10529 reported infections and 477 different host-parasite combinations collected through a survey of peer-reviewed literature. We assess how evolutionary, ecological, and morphological parameters determine host-parasite meta-communities affected by adaptive radiation events through network metrics, host repertoire measures, and network link prediction. The hosts’ evolutionary history mostly determined host repertoires of the parasites. Ecological and evolutionary parameters determined host-parasite interactions. Generally, ecological opportunity and fitting have shaped cichlid-<em>Cichlidogyrus</em> meta-communities suggesting an invasive potential for hosts used in aquaculture. Meta-communities affected by adaptive radiations are increasingly specialised with higher environmental stability. These trends should be verified across other systems to infer generalities in the evolution of species-rich host-parasite networks.</p>
Fig. 1 in (macro-) Evolutionary ecology of parasite diversity: From determinants of parasite species richness to host diversification
Fig. 1. Expression of the basic transmission rate (R0) for the case of microparasites (i.e. viruses) and macroparasites (i.e. helminths with direct transmission) (for derivations of these expressions see Morand and Deter, 2008), emphasizing the importance of two host traits, longevity and density, as likely determinants of parasite invasion and then parasite species richness. In the right panel, relationships showing that both density and longevity are in allometry with host body mass (after Brown, 1995).
Fig. 2 in (macro-) Evolutionary ecology of parasite diversity: From determinants of parasite species richness to host diversification
Fig. 2. (A) Variability of ectoparasite species richness among 113 families of mammals (20 orders) (data from Kim, 1985;see Poulin and Morand, 2004). (B) Ectoparasite species richness is related to mammal diversification. The statistical analysis follows Nunn et al. (2004), where the change in the number of descendent clades is related to the change in the number of ectoparasite species, estimated using a modified version of the independent contrast method (Agapow and Isaac, 2002), for each node of the mammal phylogeny (from Binida-Emonds et al., 2007).
Fig. 2 in Networks and the ecology of parasite transmission: A framework for wildlife parasitology
Fig. 2. How do we use networks to understand the ecology of parasite transmission? Networks allow us to describe how the behaviour of individuals collectively affects the transmission of parasites within wildlife populations. They provide a flexible framework that enables analysis at three different levels; individual (panel A), dyadic (pair-wise associations) (panel B) and the network (population) level (panel C). Within each level of analysis, there are different metrics and analytical approaches that can be used to explore the ecology of parasite transmission.
Fig. 1 in Networks and the ecology of parasite transmission: A framework for wildlife parasitology
Fig. 1. What is a network? A network in its most elementary form is an adjacency matrix, where row and column labels represent the individuals in the network, and the remaining cells represent the pair-wise associations among individuals in the network (panel A). These associations can be weighted, as below (panel A), where stronger relationships are assigned a higher value (for example, the duration or frequency of contact). They can also be directed, to reflect the direction of the association; in this instance, the direction of possible parasite transmission. In this case, rows represent donor nodes, and columns represent recipient nodes (e.g., in panel A: from node C (donor) to node D (recipient), there is a score of 1). The matrix can be visualised as a network diagram (panel B), consisting of nodes, which represent the epidemiological unit of interest (usually individuals) connected together by a series of edges representing the measure of association (the potential for parasite transmission). In context of understanding the ecology of parasite transmission, edges represent a 'contact' between two hosts that provides an opportunity for parasite transfer. The weighting of edges represents the likelihood of parasite transmission (e.g., the frequency or intensity of contact among hosts). The definition of a contact will depend on the type of parasite considered, and how it is passed from one host to another.
Fig. 5. Parsimony splits network constructed from a per and ITS2 concatenated sequence data set. Heterozygous specimens are indicated with A and B in Ecological and geographical speciation in Lucilia bufonivora: The evolution of amphibian obligate parasitism
Fig. 5. Parsimony splits network constructed from a per and ITS2 concatenated sequence data set. Heterozygous specimens are indicated with A and B. 'bufonivora_EUROPE_A' represents a consistent haplotype present in all 12 samples from Europe (Table 1), of which just two were heterozygous ('bufonivora_frog' and 'bufonivora_NLWi'). 'bufonivora_CAN' and 'elongata_CAN' are represented by two samples each, none of which were heterozygous. Scale bar represents expected changes per site.
Fig. 2. Bayesian Inference tree constructed from Internal transcribed Spacer 2 in Ecological and geographical speciation in Lucilia bufonivora: The evolution of amphibian obligate parasitism
Fig. 2. Bayesian Inference tree constructed from Internal transcribed Spacer 2 (non-coding) sequence data. Each specimen is labelled with the species name and location abbreviation as indicated in Table 1. Green text corresponds to European samples of Lucilia bufonivora; red represents Lucilia elongata; purple represents Canadian L. bufonivora; orange represents Lucilia silvarum. Scale bar represents expected changes per site. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 6. Divergence times estimated from a in Ecological and geographical speciation in Lucilia bufonivora: The evolution of amphibian obligate parasitism
Fig. 6. Divergence times estimated from a concatenated data set of per, COX1 and ITS2 sequences for the Lucilia bufornivora species group. Substitution model and relaxed clock models were unlinked for each gene. The tree was calibrated by setting the root to the node age corresponding to the split between Luciilinae and Calliphorinae subfamilies (~19 mya) as estimated by Wallman et al. (2005). Blue bars represent 95% highest posterior density (HPD) of each node age. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 1 in Ecological and geographical speciation in Lucilia bufonivora: The evolution of amphibian obligate parasitism
Fig. 1. Location of samples for which the COX1 gene was sequenced in this study. Boxes represent the locations of individual samples: red, Lucilia elongata; orange, Lucilia silvarum; green, Lucilia bufonivora.
Fig. 1 in Environmental and ecological factors driving trematode parasite community assembly in central Alberta lakes
Fig. 1. Host-Parasite diversity correlations. Spearman rank correlations of A) snail and trematode richness, pooled by site, B) non-pooled, sample-based, snail and trematode richness, C) snail and trematode effective species based on Shannon index (exp(H)) for all lakes, and D) effective species by each site at Buffalo Lake. PP = Pelican Point, RS = Rochon Sands, TN = The Narrows.
Fig. 3 in Environmental and ecological factors driving trematode parasite community assembly in central Alberta lakes
Fig. 3. Canonical correspondence analysis (CCA) of trematode component communities. Relative abundances of trematode species by sample are constrained by environmental variables from the best-fit model (community \lake trophic status \+ ecoregion \+ latitude). Trematode species abbreviations are shown in grey. CCA results are in red as eigenvectors. Ecoregions are identified with a blue dotted line. The trophic status of each lake is identified with an ellipse.
Fig. 2 in Environmental and ecological factors driving trematode parasite community assembly in central Alberta lakes
Fig. 2. Multivariate Homogeneity of Group Dispersion for Trematode Communities. Bray-Curtis dissimilarities were used to examine the homogeneity of variance among samples (trematode species counts) when grouped by different geographical or anthropogenic-use distinctions. The left panels show the twodimensional visualizations of the data by Principal Coordinate Analysis (PCA) plots. Each grouping is labeled in the center, and ellipses represent 95% confidence intervals. The right panels provide a boxplot of the distance to centroid for each group in the multivariate analysis. A) samples grouped by site, B) grouped by river basin, C) grouped by ecoregion, D) group by site-type or anthropogenic use (beach or boat launch). Statistical significance for differences between groups is indicated by an asterisk.
Fig. 3 in Adaptations, life-history traits and ecological mechanisms of parasites to survive extremes and environmental unpredictability in the face of climate change
Fig. 3. Flow chart outlining factors that can influence the response of parasites to climate change.
Fig. 1 in A Bayesian analysis of the parasitic ecology in Jenynsia multidentata (Pisces: Anablepidae)
Fig. 1. Map of the sample sites, Salado Relief Channel (S.R.C.) in Samborombon Bay and the Sauce Chico River in Bahia Blanca estuary (B.B.), Argentina.
Fig. 2. Mean and the 95 in A Bayesian analysis of the parasitic ecology in Jenynsia multidentata (Pisces: Anablepidae)
Fig. 2. Mean and the 95% credibility range of Weight (W.) in grams (gr), total and standard length (TL and SL) in centimeters of Jenynsia multidentata Jenyns, 1842 in Salado River Channel (S.R.C.) and Low Sauce River of Bahia Blanca (B.B.), Argentina.
Fig. 10 in Diversity and ecological relationships of Cestoda and Monogenoidea parasites of freshwater stingrays (Myliobatiformes, Potamotrygonidae), in the upper Paran´a River, Brazil
Fig. 10. Morphology of Potamotrygonocestus sp.2. Morphology of scolex (A); Mature proglottid (B). Abbreviations: BH = bothridia hooks; GP = genital pore; O = ovary; S = scolex; T = testes; U = uterus, and V = vitellaria.
Fig. 8 in Diversity and ecological relationships of Cestoda and Monogenoidea parasites of freshwater stingrays (Myliobatiformes, Potamotrygonidae), in the upper Paran´a River, Brazil
Fig. 8. Morphology of Acanthobothrium quinonesi. Morphology of scolex by light microscopy (A) and SEM (B); Isolated bothridia hooks (C); Mature proglottid (D); Cirrus sac (E). Abbreviations: AL = anterior loculus; BH = bothridia hooks; Cs = cirrus sac; EC = everted cirrus; Lh = lateral hook; Mh = medial hook; ML = middle loculus; O = ovary; PL = posterior loculus; S = scolex; T = testes, and U = uterus.
Fig. 9 in Diversity and ecological relationships of Cestoda and Monogenoidea parasites of freshwater stingrays (Myliobatiformes, Potamotrygonidae), in the upper Paran´a River, Brazil
Fig. 9. Morphology of Potamotrygonocestus sp.1. Morphology of scolex (A); Isolated bothridia hooks (B); Mature proglottid (C); Cirrus sac (D); Gravid proglottid (E). Abbreviations: EC = everted cirrus; F = furca; GP = genital pore; HB = hook base; O = ovary; S = scolex; T = testes; U = uterus, and V = vitellaria.
Fig. 6 in Diversity and ecological relationships of Cestoda and Monogenoidea parasites of freshwater stingrays (Myliobatiformes, Potamotrygonidae), in the upper Paran´a River, Brazil
Fig. 6. Morphology of Rhinebothrium paratrygoni. Morphology of scolex (A); Details of bothridium (B); Terminal mature proglottid (C); Cross-copulation between mature proglottids (D), and partial strobila (E). Abbreviations: B = bothridia; Cc = Cross-copulation; O = ovary, and S = scolex.).
Fig. 1 in Diversity and ecological relationships of Cestoda and Monogenoidea parasites of freshwater stingrays (Myliobatiformes, Potamotrygonidae), in the upper Paran´a River, Brazil
Fig. 1. Collection area for potamotrygonids and their parasites. (a) Highlight (red) of the upper Paran´a River system (Brazilian portion). (b) Collection sites (red triangles), S1 with three points and S2 with one point, in the upper Paran´a River, between the states of S˜ao Paulo and Mato Grosso do Sul, Brazil. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.