FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4313426491> ?p ?o ?g. }

• W4313426491 abstract "Abstract Joint Species Distribution Models (jSDM) are increasingly used to explain and predict biodiversity patterns. jSDMs account for species co-occurrence patterns and can include phylogeny or functional traits to better capture the processes shaping communities. Yet, several factors may limit or affect the interpretability and predictive ability of jSDMs : missing abiotic predictors, omitting ecologically-important species, or increasing the number of model parameters by adding phylogeny and/or trait information. We assessed how interpretability, explanatory and predictive power of jSDM varied across four alternative models focusing on 99 coastal benthic marine polychaete species: (1) a baseline jSDM with no additional information sources other than abiotic predictors and residual co-occurrence patterns, (2) a jSDM including phylogeny alone or (3) in combination with traits data and (4) a jSDM including monitoring information related to additional species sampled alongside the target assemblage (i.e. non-target species that are not of direct interest but potentially interact with the target assemblage). The four models fitted on both presence/absence and abundance data from a regional monitoring programme were assessed using complementary metrics. We compared performance at both species- and community-level, considering multiple facets of species responses and assemblage diversity. For both presence/absence and abundance data, all models displayed good and similar explanatory power but varied in their interpretability and predictive power. Considering trait data provides insights on species response along environmental gradients, which is a decisive element for model interpretability. Relative to the baseline model, predictive power increased by 26% when including data on additional species, whereas only marginal changes were detected for the two other models. These patterns are explained by changes in the species-environment relationships and residual co-occurrence patterns inferred by these models. Overall, this study highlights that adequate strategy to fit jSDM depends on data at hand, modelling objective and research question. To understand observed community space-time variability, adding phylogenetic or trait information is most effective. Inclusion of non-target species is however a better strategy to predict how the target species assemblage responds to environmental changes. Importantly, we provide a comprehensive toolbox for the comparative assessment of jSDM performance." @default.
• W4313426491 created "2023-01-06" @default.
• W4313426491 creator A5025715025 @default.
• W4313426491 creator A5028428000 @default.
• W4313426491 creator A5050400584 @default.
• W4313426491 creator A5062554570 @default.
• W4313426491 creator A5063386274 @default.
• W4313426491 creator A5070156855 @default.
• W4313426491 date "2022-12-19" @default.
• W4313426491 modified "2023-10-18" @default.
• W4313426491 title "Essential ingredients in Joint Species Distribution Models: influence on interpretability, explanatory and predictive power" @default.
• W4313426491 cites W1560555922 @default.
• W4313426491 cites W1834602081 @default.
• W4313426491 cites W1920817623 @default.
• W4313426491 cites W1946204838 @default.
• W4313426491 cites W1952066190 @default.
• W4313426491 cites W1971921583 @default.
• W4313426491 cites W1986520077 @default.
• W4313426491 cites W1989523953 @default.
• W4313426491 cites W1999273813 @default.
• W4313426491 cites W2017940013 @default.
• W4313426491 cites W2032081489 @default.
• W4313426491 cites W2042065621 @default.
• W4313426491 cites W2049015414 @default.
• W4313426491 cites W2090686691 @default.
• W4313426491 cites W2096694793 @default.
• W4313426491 cites W2102941100 @default.
• W4313426491 cites W2112315008 @default.
• W4313426491 cites W2138677176 @default.
• W4313426491 cites W2148534890 @default.
• W4313426491 cites W2150597302 @default.
• W4313426491 cites W2151846090 @default.
• W4313426491 cites W2170408123 @default.
• W4313426491 cites W2176706581 @default.
• W4313426491 cites W2462409464 @default.
• W4313426491 cites W2510796315 @default.
• W4313426491 cites W2564975926 @default.
• W4313426491 cites W2597532406 @default.
• W4313426491 cites W2598342138 @default.
• W4313426491 cites W2605946083 @default.
• W4313426491 cites W2616923088 @default.
• W4313426491 cites W2619821824 @default.
• W4313426491 cites W2625281704 @default.
• W4313426491 cites W2729675707 @default.
• W4313426491 cites W2740653777 @default.
• W4313426491 cites W2748579291 @default.
• W4313426491 cites W2786425804 @default.
• W4313426491 cites W2791581205 @default.
• W4313426491 cites W2793891291 @default.
• W4313426491 cites W2883251903 @default.
• W4313426491 cites W2884378407 @default.
• W4313426491 cites W2896983921 @default.
• W4313426491 cites W2899906720 @default.
• W4313426491 cites W2939014667 @default.
• W4313426491 cites W2941774662 @default.
• W4313426491 cites W2949179357 @default.
• W4313426491 cites W2969788538 @default.
• W4313426491 cites W2973425059 @default.
• W4313426491 cites W2973916775 @default.
• W4313426491 cites W3003994650 @default.
• W4313426491 cites W3004840513 @default.
• W4313426491 cites W3006909503 @default.
• W4313426491 cites W3014650038 @default.
• W4313426491 cites W3026847252 @default.
• W4313426491 cites W3088574105 @default.
• W4313426491 cites W3093626936 @default.
• W4313426491 cites W3095049225 @default.
• W4313426491 cites W3104375919 @default.
• W4313426491 cites W3131981437 @default.
• W4313426491 cites W3135946500 @default.
• W4313426491 cites W3136877606 @default.
• W4313426491 cites W3144187446 @default.
• W4313426491 cites W3145271326 @default.
• W4313426491 cites W3183965237 @default.
• W4313426491 cites W3203024908 @default.
• W4313426491 cites W3211103949 @default.
• W4313426491 cites W4200317213 @default.
• W4313426491 cites W4210984261 @default.
• W4313426491 cites W4250581733 @default.
• W4313426491 cites W4309468788 @default.
• W4313426491 doi "https://doi.org/10.1101/2022.12.19.519605" @default.
• W4313426491 hasPublicationYear "2022" @default.
• W4313426491 type Work @default.
• W4313426491 citedByCount "0" @default.
• W4313426491 crossrefType "posted-content" @default.
• W4313426491 hasAuthorship W4313426491A5025715025 @default.
• W4313426491 hasAuthorship W4313426491A5028428000 @default.
• W4313426491 hasAuthorship W4313426491A5050400584 @default.
• W4313426491 hasAuthorship W4313426491A5062554570 @default.
• W4313426491 hasAuthorship W4313426491A5063386274 @default.
• W4313426491 hasAuthorship W4313426491A5070156855 @default.
• W4313426491 hasBestOaLocation W43134264911 @default.
• W4313426491 hasConcept C106934330 @default.
• W4313426491 hasConcept C111472728 @default.
• W4313426491 hasConcept C119857082 @default.
• W4313426491 hasConcept C122325731 @default.
• W4313426491 hasConcept C130217890 @default.
• W4313426491 hasConcept C132124917 @default.
• W4313426491 hasConcept C132215390 @default.
• W4313426491 hasConcept C138885662 @default.

• next