TY  - JOUR
A1  - Svenning, Jens-Christian
A1  - Gravel, Dominique
A1  - Holt, Robert D.
A1  - Schurr, Frank Martin
A1  - Thuiller, Wilfried
A1  - Muenkemueller, Tamara
A1  - Schiffers, Katja H.
A1  - Dullinger, Stefan
A1  - Edwards, Thomas C.
A1  - Hickler, Thomas
A1  - Higgins, Steven I.
A1  - Nabel, Julia E. M. S.
A1  - Pagel, Jörn
A1  - Normand, Signe
T1  - The influence of interspecific interactions on species range expansion rates
JF  - Ecography : pattern and diversity in ecology ; research papers forum
Y1  - 2014
U6  - https://doi.org/10.1111/j.1600-0587.2013.00574.x
SN  - 0906-7590
SN  - 1600-0587
VL  - 37
IS  - 12
SP  - 1198
EP  - 1209
PB  - Wiley-Blackwell
CY  - Hoboken
ER  - 
TY  - JOUR
A1  - Pagel, Jörn
A1  - Anderson, Barbara J.
A1  - Cramer, Wolfgang
A1  - Fox, Richard
A1  - Jeltsch, Florian
A1  - Roy, David B.
A1  - Thomas, Chris D.
A1  - Schurr, Frank Martin
T1  - Quantifying range-wide variation in population trends from local abundance surveys and widespread opportunistic occurrence records
JF  - Methods in ecology and evolution : an official journal of the British Ecological Society
N2  - 2. We present a hierarchical model that integrates observations from multiple sources to estimate spatio-temporal abundance trends. The model links annual population densities on a spatial grid to both long-term count data and to opportunistic occurrence records from a citizen science programme. Specific observation models for both data types explicitly account for differences in data structure and quality.
 3. We test this novel method in a virtual study with simulated data and apply it to the estimation of abundance dynamics across the range of a butterfly species (Pyronia tithonus) in Great Britain between 1985 and 2004. The application to simulated and real data demonstrates how the hierarchical model structure accommodates various sources of uncertainty which occur at different stages of the link between observational data and the modelled abundance, thereby it accounts for these uncertainties in the inference of abundance variations.
 4. We show that by using hierarchical observation models that integrate different types of commonly available data sources, we can improve the estimates of variation in species abundances across space and time. This will improve our ability to detect regional trends and can also enhance the empirical basis for understanding range dynamics.
KW  - atlas data
KW  - Bayesian statistics
KW  - biogeography
KW  - butterflies
KW  - citizen science programme
KW  - conservation biology
KW  - count data
KW  - macroecology
KW  - state-space model
Y1  - 2014
U6  - https://doi.org/10.1111/2041-210X.12221
SN  - 2041-210X
SN  - 2041-2096
VL  - 5
IS  - 8
SP  - 751
EP  - 760
PB  - Wiley-Blackwell
CY  - Hoboken
ER  - 
TY  - THES
A1  - Pagel, Jörn
T1  - Statistical process-based models for the understanding and prediction of range dynamics
Y1  - 2014
ER  -