TY  - JOUR
A1  - Jeltsch, Florian
A1  - Blaum, Niels
A1  - Brose, Ulrich
A1  - Chipperfield, Joseph D.
A1  - Clough, Yann
A1  - Farwig, Nina
A1  - Geissler, Katja
A1  - Graham, Catherine H.
A1  - Grimm, Volker
A1  - Hickler, Thomas
A1  - Huth, Andreas
A1  - May, Felix
A1  - Meyer, Katrin M.
A1  - Pagel, Jörn
A1  - Reineking, Björn
A1  - Rillig, Matthias C.
A1  - Shea, Katriona
A1  - Schurr, Frank Martin
A1  - Schroeder, Boris
A1  - Tielbörger, Katja
A1  - Weiss, Lina
A1  - Wiegand, Kerstin
A1  - Wiegand, Thorsten
A1  - Wirth, Christian
A1  - Zurell, Damaris
T1  - How can we bring together empiricists and modellers in functional biodiversity research?
JF  - Basic and applied ecology : Journal of the Gesellschaft für Ökologie
N2  - Improving our understanding of biodiversity and ecosystem functioning and our capacity to inform ecosystem management requires an integrated framework for functional biodiversity research (FBR). However, adequate integration among empirical approaches (monitoring and experimental) and modelling has rarely been achieved in FBR. We offer an appraisal of the issues involved and chart a course towards enhanced integration. A major element of this path is the joint orientation towards the continuous refinement of a theoretical framework for FBR that links theory testing and generalization with applied research oriented towards the conservation of biodiversity and ecosystem functioning. We further emphasize existing decision-making frameworks as suitable instruments to practically merge these different aims of FBR and bring them into application. This integrated framework requires joint research planning, and should improve communication and stimulate collaboration between modellers and empiricists, thereby overcoming existing reservations and prejudices. The implementation of this integrative research agenda for FBR requires an adaptation in most national and international funding schemes in order to accommodate such joint teams and their more complex structures and data needs.
KW  - Biodiversity theory
KW  - Biodiversity experiments
KW  - Conservation management
KW  - Decision-making
KW  - Ecosystem functions and services
KW  - Forecasting
KW  - Functional traits
KW  - Global change
KW  - Monitoring programmes
KW  - Interdisciplinarity
Y1  - 2013
U6  - https://doi.org/10.1016/j.baae.2013.01.001
SN  - 1439-1791
VL  - 14
IS  - 2
SP  - 93
EP  - 101
PB  - Elsevier
CY  - Jena
ER  - 
TY  - JOUR
A1  - Noonan, Michael J.
A1  - Tucker, Marlee A.
A1  - Fleming, Christen H.
A1  - Akre, Thomas S.
A1  - Alberts, Susan C.
A1  - Ali, Abdullahi H.
A1  - Altmann, Jeanne
A1  - Antunes, Pamela Castro
A1  - Belant, Jerrold L.
A1  - Beyer, Dean
A1  - Blaum, Niels
A1  - Boehning-Gaese, Katrin
A1  - Cullen Jr, Laury
A1  - de Paula, Rogerio Cunha
A1  - Dekker, Jasja
A1  - Drescher-Lehman, Jonathan
A1  - Farwig, Nina
A1  - Fichtel, Claudia
A1  - Fischer, Christina
A1  - Ford, Adam T.
A1  - Goheen, Jacob R.
A1  - Janssen, Rene
A1  - Jeltsch, Florian
A1  - Kauffman, Matthew
A1  - Kappeler, Peter M.
A1  - Koch, Flavia
A1  - LaPoint, Scott
A1  - Markham, A. Catherine
A1  - Medici, Emilia Patricia
A1  - Morato, Ronaldo G.
A1  - Nathan, Ran
A1  - Oliveira-Santos, Luiz Gustavo R.
A1  - Olson, Kirk A.
A1  - Patterson, Bruce D.
A1  - Paviolo, Agustin
A1  - Ramalho, Emiliano Estero
A1  - Rosner, Sascha
A1  - Schabo, Dana G.
A1  - Selva, Nuria
A1  - Sergiel, Agnieszka
A1  - da Silva, Marina Xavier
A1  - Spiegel, Orr
A1  - Thompson, Peter
A1  - Ullmann, Wiebke
A1  - Zieba, Filip
A1  - Zwijacz-Kozica, Tomasz
A1  - Fagan, William F.
A1  - Mueller, Thomas
A1  - Calabrese, Justin M.
T1  - A comprehensive analysis of autocorrelation and bias in home range estimation
JF  - Ecological monographs : a publication of the Ecological Society of America.
N2  - Home range estimation is routine practice in ecological research. While advances in animal tracking technology have increased our capacity to collect data to support home range analysis, these same advances have also resulted in increasingly autocorrelated data. Consequently, the question of which home range estimator to use on modern, highly autocorrelated tracking data remains open. This question is particularly relevant given that most estimators assume independently sampled data. Here, we provide a comprehensive evaluation of the effects of autocorrelation on home range estimation. We base our study on an extensive data set of GPS locations from 369 individuals representing 27 species distributed across five continents. We first assemble a broad array of home range estimators, including Kernel Density Estimation (KDE) with four bandwidth optimizers (Gaussian reference function, autocorrelated‐Gaussian reference function [AKDE], Silverman's rule of thumb, and least squares cross‐validation), Minimum Convex Polygon, and Local Convex Hull methods. Notably, all of these estimators except AKDE assume independent and identically distributed (IID) data. We then employ half‐sample cross‐validation to objectively quantify estimator performance, and the recently introduced effective sample size for home range area estimation ( N̂ area
) to quantify the information content of each data set. We found that AKDE 95% area estimates were larger than conventional IID‐based estimates by a mean factor of 2. The median number of cross‐validated locations included in the hold‐out sets by AKDE 95% (or 50%) estimates was 95.3% (or 50.1%), confirming the larger AKDE ranges were appropriately selective at the specified quantile. Conversely, conventional estimates exhibited negative bias that increased with decreasing N̂ area. To contextualize our empirical results, we performed a detailed simulation study to tease apart how sampling frequency, sampling duration, and the focal animal's movement conspire to affect range estimates. Paralleling our empirical results, the simulation study demonstrated that AKDE was generally more accurate than conventional methods, particularly for small N̂ area. While 72% of the 369 empirical data sets had >1,000 total observations, only 4% had an N̂ area >1,000, where 30% had an N̂ area <30. In this frequently encountered scenario of small N̂ area, AKDE was the only estimator capable of producing an accurate home range estimate on autocorrelated data.
KW  - animal movement
KW  - kernel density estimation
KW  - local convex hull
KW  - minimum convex polygon
KW  - range distribution
KW  - space use
KW  - telemetry
KW  - tracking data
Y1  - 2018
U6  - https://doi.org/10.1002/ecm.1344
SN  - 0012-9615
SN  - 1557-7015
VL  - 89
IS  - 2
PB  - Wiley
CY  - Hoboken
ER  -