Are stacked species distribution models accurate at predicting multiple levels of diversity along a rainfall gradient?

Israel Del Toro, Relena R. Ribbons, Jodie Hayward, Alan N. Andersen

Research output: Contribution to journalArticle

Abstract

We use observed patterns of species richness and composition of ant communities along a 1000 mm rainfall gradient in northern Australian savanna to assess the accuracy of species richness and turnover predictions derived from stacked species distribution models (S-SDMs) and constrained by macroecological models (MEMs). We systematically sampled ants at 15 sites at 50 km intervals along the rainfall gradient in 2012 and 2013. Using the observed data, we created MEMs of species richness, composition and turnover. We built distribution models for 135 of the observed species using data from museum collections and online databases. We compared two approaches of stacking SDMs and three modelling algorithms to identify the most accurate way of predicting richness and composition. We then applied the same beta diversity metrics to compare the observed versus predicted patterns. Stacked SDMs consistently over-predicted local species richness, and there was a mismatch between the observed pattern of richness estimated from the MEM, and the pattern predicted by S-SDMs. The most accurate richness and turnover predictions occurred when the stacked models were rank-ordered by their habitat suitability and constrained by the observed MEM richness predictions. In contrast with species richness, the predictions obtained by the MEM of community similarity, composition and turnover matched those predicted by the S-SDMs. S-SDMs regulated by MEMs may therefore be a useful tool in predicting compositional patterns despite being unreliable estimators of species richness. Our results highlight that the choice of species distribution model, the stacking method used, and underlying macroecological patterns all influence the accuracy of community assembly predictions derived from S-SDMS.

Original languageEnglish
Pages (from-to)105-113
Number of pages9
JournalAustral Ecology
Volume44
Issue number1
Early online date21 Oct 2018
DOIs
Publication statusPublished - Feb 2019

Fingerprint

biogeography
rain
rainfall
species richness
species diversity
turnover
prediction
stacking
distribution
ant
Formicidae
savanna
museum
savannas
habitat

Cite this

Del Toro, Israel ; Ribbons, Relena R. ; Hayward, Jodie ; Andersen, Alan N. / Are stacked species distribution models accurate at predicting multiple levels of diversity along a rainfall gradient?. In: Austral Ecology. 2019 ; Vol. 44, No. 1. pp. 105-113.
@article{eb7abc05a1624cf081ac76595810b8ca,
title = "Are stacked species distribution models accurate at predicting multiple levels of diversity along a rainfall gradient?",
abstract = "We use observed patterns of species richness and composition of ant communities along a 1000 mm rainfall gradient in northern Australian savanna to assess the accuracy of species richness and turnover predictions derived from stacked species distribution models (S-SDMs) and constrained by macroecological models (MEMs). We systematically sampled ants at 15 sites at 50 km intervals along the rainfall gradient in 2012 and 2013. Using the observed data, we created MEMs of species richness, composition and turnover. We built distribution models for 135 of the observed species using data from museum collections and online databases. We compared two approaches of stacking SDMs and three modelling algorithms to identify the most accurate way of predicting richness and composition. We then applied the same beta diversity metrics to compare the observed versus predicted patterns. Stacked SDMs consistently over-predicted local species richness, and there was a mismatch between the observed pattern of richness estimated from the MEM, and the pattern predicted by S-SDMs. The most accurate richness and turnover predictions occurred when the stacked models were rank-ordered by their habitat suitability and constrained by the observed MEM richness predictions. In contrast with species richness, the predictions obtained by the MEM of community similarity, composition and turnover matched those predicted by the S-SDMs. S-SDMs regulated by MEMs may therefore be a useful tool in predicting compositional patterns despite being unreliable estimators of species richness. Our results highlight that the choice of species distribution model, the stacking method used, and underlying macroecological patterns all influence the accuracy of community assembly predictions derived from S-SDMS.",
keywords = "Boosted Regression Trees, Formicidae, macroecological models, MaxEnt, Maxlike, species distribution models",
author = "{Del Toro}, Israel and Ribbons, {Relena R.} and Jodie Hayward and Andersen, {Alan N.}",
year = "2019",
month = "2",
doi = "10.1111/aec.12658",
language = "English",
volume = "44",
pages = "105--113",
journal = "Australian Journal of Ecology",
issn = "1442-9985",
publisher = "Blackwell Publishing",
number = "1",

}

Are stacked species distribution models accurate at predicting multiple levels of diversity along a rainfall gradient? / Del Toro, Israel; Ribbons, Relena R.; Hayward, Jodie; Andersen, Alan N.

In: Austral Ecology, Vol. 44, No. 1, 02.2019, p. 105-113.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Are stacked species distribution models accurate at predicting multiple levels of diversity along a rainfall gradient?

AU - Del Toro, Israel

AU - Ribbons, Relena R.

AU - Hayward, Jodie

AU - Andersen, Alan N.

PY - 2019/2

Y1 - 2019/2

N2 - We use observed patterns of species richness and composition of ant communities along a 1000 mm rainfall gradient in northern Australian savanna to assess the accuracy of species richness and turnover predictions derived from stacked species distribution models (S-SDMs) and constrained by macroecological models (MEMs). We systematically sampled ants at 15 sites at 50 km intervals along the rainfall gradient in 2012 and 2013. Using the observed data, we created MEMs of species richness, composition and turnover. We built distribution models for 135 of the observed species using data from museum collections and online databases. We compared two approaches of stacking SDMs and three modelling algorithms to identify the most accurate way of predicting richness and composition. We then applied the same beta diversity metrics to compare the observed versus predicted patterns. Stacked SDMs consistently over-predicted local species richness, and there was a mismatch between the observed pattern of richness estimated from the MEM, and the pattern predicted by S-SDMs. The most accurate richness and turnover predictions occurred when the stacked models were rank-ordered by their habitat suitability and constrained by the observed MEM richness predictions. In contrast with species richness, the predictions obtained by the MEM of community similarity, composition and turnover matched those predicted by the S-SDMs. S-SDMs regulated by MEMs may therefore be a useful tool in predicting compositional patterns despite being unreliable estimators of species richness. Our results highlight that the choice of species distribution model, the stacking method used, and underlying macroecological patterns all influence the accuracy of community assembly predictions derived from S-SDMS.

AB - We use observed patterns of species richness and composition of ant communities along a 1000 mm rainfall gradient in northern Australian savanna to assess the accuracy of species richness and turnover predictions derived from stacked species distribution models (S-SDMs) and constrained by macroecological models (MEMs). We systematically sampled ants at 15 sites at 50 km intervals along the rainfall gradient in 2012 and 2013. Using the observed data, we created MEMs of species richness, composition and turnover. We built distribution models for 135 of the observed species using data from museum collections and online databases. We compared two approaches of stacking SDMs and three modelling algorithms to identify the most accurate way of predicting richness and composition. We then applied the same beta diversity metrics to compare the observed versus predicted patterns. Stacked SDMs consistently over-predicted local species richness, and there was a mismatch between the observed pattern of richness estimated from the MEM, and the pattern predicted by S-SDMs. The most accurate richness and turnover predictions occurred when the stacked models were rank-ordered by their habitat suitability and constrained by the observed MEM richness predictions. In contrast with species richness, the predictions obtained by the MEM of community similarity, composition and turnover matched those predicted by the S-SDMs. S-SDMs regulated by MEMs may therefore be a useful tool in predicting compositional patterns despite being unreliable estimators of species richness. Our results highlight that the choice of species distribution model, the stacking method used, and underlying macroecological patterns all influence the accuracy of community assembly predictions derived from S-SDMS.

KW - Boosted Regression Trees

KW - Formicidae

KW - macroecological models

KW - MaxEnt

KW - Maxlike

KW - species distribution models

UR - http://www.scopus.com/inward/record.url?scp=85055247822&partnerID=8YFLogxK

U2 - 10.1111/aec.12658

DO - 10.1111/aec.12658

M3 - Article

AN - SCOPUS:85055247822

VL - 44

SP - 105

EP - 113

JO - Australian Journal of Ecology

JF - Australian Journal of Ecology

SN - 1442-9985

IS - 1

ER -