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1,042 results for “model species”

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dryad40/100

Data and code for: Building use-inspired species distribution models: using multiple data types to examine and improve model performance

Species distribution models (SDMs) are becoming an important tool for marine conservation and management. Yet while there is an increasing diversity and volume of marine biodiversity data for training SDMs, little practical guidance is available on how to leverage distinct data types to build robust models. We explored the effect of different data types on the fit, performance and predictive ability of SDMs by comparing models trained with four data types for a heavily exploited pelagic fish, the blue shark (Prionace glauca), in the Northwest Atlantic: two fishery-dependent (conventional mark-recapture tags, fisheries observer records) and two fishery-independent (satellite-linked electronic tags, pop-up archival tags). We found that all four data types can result in robust models, but differences among spatial predictions highlighted the need to consider ecological realism in model selection and interpretation regardless of data type. Differences among models were primarily attributed to biases in how each data type, and the associated representation of absences, sampled the environment and summarized the resulting species distributions. Outputs from model ensembles and a model trained on all pooled data both proved effective for combining inferences across data types and provided more ecologically realistic predictions than individual models. Our results provide valuable guidance for practitioners developing SDMs. With increasing access to diverse data sources, future work should further develop truly integrative modeling approaches that can explicitly leverage strengths of individual data types while statistically accounting for limitations, such as sampling biases.

opencc-zeroMay 2023View details →

dryad40/100

Data for: A model of ecological abundance: Terrestrial species inventories

Counts of species in ecological samples are important for two reasons: they tell us about community assembly processes and they form the basis of species diversity estimates. Previous models of count distributions are either complex, widely rejected, not grounded in population dynamics, or not able to predict high unevenness. I present a new one-parameter model assuming that individual counts track the geometric series. The series' governing parameter p is set to vary randomly among species. Communities differ only in the centering of the distribution of p. To find the probability distribution, a vector of evenly-spaced initial values called q is drawn from the range 0 to 1. Values are then scaled by (1) transforming each q into the odds o = q/(1 – q), (2) multiplying each o by a fitted parameter m, and (3) back-computing each p as m o/(m o + 1). This skews the values to match the centering of the actual counts. The distribution is consistent with a population dynamics model in which the number of offspring produced in each interval by each species is distributed geometrically, rising with the number of adults. Large-scale surveys of corals, fishes, butterflies, and trees are consistent with the distribution, as are local-scale inventories of trees and assorted vertebrate and insect groups. Each local survey is used to predict counts within biogeographically and taxonomically matched surveys. When only decisive differences are considered, the model's predictions outperform those of each rival in at least 86% of all pairwise comparisons. The new distribution's estimates haves no substantial sample size bias. Thus, it is preferable to other species diversity estimation methods in the frequent cases where it is a good fit to count data.

opencc-zeroJul 2023View details →

dryad40/100

Hierarchical heuristic species delimitation under the multispecies coalescent model with migration

The multispecies coalescent (MSC) model accommodates genealogical fluctuations across the genome and provides a natural framework for comparative analysis of genomic sequence data to infer the history of species divergence and gene flow. Given a set of populations, hypotheses of species delimitation (and species phylogeny) may be formulated as instances of MSC models (e.g., MSC for one species versus MSC for two species) and compared using Bayesian model selection. This approach, implemented in the program bpp, has been found to be prone to over-splitting. Alternatively, heuristic criteria based on population parameters under the MSC model (such as population/species divergence times, population sizes, and migration rates) estimated from genomic sequence data may be used to delimit species. Here we extend the approach of species delimitation using the genealogical divergence index (𝑔𝑑𝑖) to develop hierarchical merge and split algorithms for heuristic species delimitation and implement them in a python pipeline called hhsd. Applied to data simulated under a model of isolation by distance, the approach was able to recover the correct species delimitation, whereas model comparison by bpp failed. Analyses of empirical datasets suggest that the procedure may be less prone to over-splitting. We discuss possible strategies for accommodating paraphyletic species in the procedure, as well as the challenges of species delimitation based on heuristic criteria.

opencc-zeroSep 2023View details →

zenodo40/100

Figs 51, 52.Ant species collected from 20 in A redescription of Merenius alberti Lessert, 1923 (Araneae: Corinnidae), with remarks on colour polymorphism and its relationship to ant models

Figs 51, 52.Ant species collected from 20 sites sampled in the Ndumo Game Reserve during June–July (51) and November–December 2009 (52) by pitfall trapping over a 10-day period that may be potential models for Merenius alberti Lessert, 1923. Numbers above each column indicate the total number of potential model ants sampled (Table 1), followed in parenthesis by the number of black and red morphs of M. alberti collected by hand at each site. Red crosses indicate sites where no potential ant models or M. alberti were collected. Blue bars – Streblognathus peetersi Robertson, 2002; orange bars – Anoplolepis custodiens (F. Smith, 1858); maroon bars – Camponotus cinctellus (Gerstäcker, 1859); turquoise bars –?Atopomyrmex mocquerysi André, 1889; yellow bars – Odontomachus troglodytes Santschi, 1914; green bars – Polyrhachis gagates F. Smith, 1858; red bars –?Pachycondyla caffraria (F. Smith, 1858).

opencc-by-4.0Dec 2012View details →

dryad40/100

The dataset of Liquidambar orientalis for species distribution models

The primary objective of this study was to predict the existing geographic range of Liquidambar orientalis, commonly known as the oriental sweetgum. To gain insights into the potential effects of climate change on the oriental sweetgum, the study employed species distribution models to project the model to future periods. Considering two Shared Socioeconomic Pathways (SSP1-2.6 and SSP5-8.5), the ensemble modeling approach utilized the biomod2 package in the R programming language to analyze the alterations in the spatial distribution of the species in forthcoming periods (namely, for the years 2035s, 2055s, and 2070s).

opencc-zeroOct 2023View details →

dryad40/100