Environmental Data: Novel methods to correct for observer and sampling...

Keywords:
BACKGROUND DATA
CLUSTER
COVARIATE CORRECTION
ENVIRONMENTAL STRATIFICATION
INDEPENDENT DATASET
PLANT SPECIES
POINT PROCESS MODEL
RANDOM STRATIFIED SAMPLING
SURVEY EFFORT
TARGET GROUP
Keywords:
BACKGROUND DATA
CLUSTER
COVARIATE CORRECTION
ENVIRONMENTAL STRATIFICATION
INDEPENDENT DATASET
PLANT SPECIES
POINT PROCESS MODEL
RANDOM STRATIFIED SAMPLING
SURVEY EFFORT
TARGET GROUP

Description

Aim: While species distribution models (SDMs) are standard tools to predict species distributions, they can suffer from observation and sampling biases, pa...

Citation

Chauvier, Y., Zimmermann, N., Poggiato, G., Bystrova, D., Brun, P., Thuiller, W. (2021). Novel methods to correct for observer and sampling bias in presence-only species distribution models. EnviDat. https://www.doi.org/10.16904/envidat.226.

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