Automated Author Profile

Fichtel, Claudia

Current S-Index

17.4

Sum of Dataset Indices for all datasets

Average Dataset Index per Dataset

0.9

Average Dataset Index per dataset

Total Datasets

19

Total datasets for this author

Average FAIR Score

34.3%

Average FAIR Score per dataset

Total Citations

8

Total citations to the author's datasets

Total Mentions

0

Total mentions of the author's datasets

S-Index Interpretation

S-Index Over Time

Cumulative Citations Over Time

Cumulative Mentions Over Time

Datasets

Data from: A comprehensive analysis of autocorrelation and bias in home range estimation (Version: 1)

<b>Abstract</b><br/>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 dataset of GPS locations from 369 individuals representing 27 species distributed across 5 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 ($\hat{N}\mathrm{area}$) to quantify the information content of each dataset. 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 holdout 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 $\hat{N}\mathrm{area}$. 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 $\hat{N}\mathrm{area}$. While 72% of the 369 empirical datasets had \textgreater1000 total observations, only 4% had an $\hat{N}\mathrm{area}$ \textgreater1000, where 30% had an $\hat{N}\mathrm{area}$ \textless30. In this frequently encountered scenario of small $\hat{N}\mathrm{area}$, AKDE was the only estimator capable of producing an accurate home range estimate on autocorrelated data.

Authors

  • Noonan, Michael J. ;
  • Tucker, Marlee A. ;
  • Fleming, Christen H. ;
  • Akre, Tom S. ;
  • Alberts, Susan C. ;
  • Ali, Abdullahi H. ;
  • Altmann, Jeanne ;
  • Antunes, Pamela C. ;
  • Belant, Jerrold L. ;
  • Beyer, Dean ;
  • Blaum, Niels ;
  • Böhning-Gaese, Katrin ;
  • Cullen Jr., Laury ;
  • De Paula Cunha, Rogerio ;
  • Dekker, Jasja ;
  • Drescher-Lehman, Jonathan ;
  • Farwig, Nina ;
  • Fichtel, Claudia ;
  • Fischer, Christina ;
  • Ford, Adam T. ;
  • Goheen, Jacob R. ;
  • Janssen, René ;
  • Jeltsch, Florian ;
  • Kauffman, Matthew ;
  • Kappeler, Peter M. ;
  • Koch, Flávia ;
  • LaPoint, Scott ;
  • Markham, A. Catherine ;
  • Medici, Emilia Patricia ;
  • Morato, Ronaldo G. ;
  • Nathan, Ran ;
  • Oliveira-Santos, Luiz Gustavo R. ;
  • Olson, Kirk A. ;
  • Patterson, Bruce D. ;
  • Paviolo, Agustin ;
  • Ramalho, Emiliano E. ;
  • Rosner, Sascha ;
  • Schabo, Dana G. ;
  • Selva, Nuria ;
  • Sergiel, Agnieszka ;
  • Da Silva, Marina X. ;
  • Spiegel, Orr ;
  • Thompson, Peter ;
  • Ullmann, Wiebke ;
  • Zięba, Filip ;
  • Zwijacz-Kozica, Tomasz ;
  • Fagan, William F. ;
  • Mueller, Thomas ;
  • Calabrese, Justin M.
0 Citations0 Mentions42% FAIR1.0 Dataset Index
10.14288/1.0397835January 2020

Data from: Moving in the Anthropocene: global reductions in terrestrial mammalian movements (Version: 1)

<b>Abstract</b><br/>Animal movement is fundamental for ecosystem functioning and species survival, yet the effects of the anthropogenic footprint on animal movements have not been estimated across species. Using a unique GPS-tracking database of 803 individuals across 57 species, we found that movements of mammals in areas with a comparatively high human footprint were on average one-half to one-third the extent of their movements in areas with a low human footprint. We attribute this reduction to behavioral changes of individual animals and to the exclusion of species with long-range movements from areas with higher human impact. Global loss of vagility alters a key ecological trait of animals that affects not only population persistence but also ecosystem processes such as predator-prey interactions, nutrient cycling, and disease transmission.

Authors

  • Kane, Adam ;
  • Tucker, Marlee A. ;
  • Böhning-Gaese, Katrin ;
  • Fagan, William F. ;
  • Fryxell, John M. ;
  • Van Moorter, Bram ;
  • Alberts, Susan C. ;
  • Ali, Abdullahi H. ;
  • Allen, Andrew M. ;
  • Attias, Nina ;
  • Avgar, Tal ;
  • Bartlam-Brooks, Hattie ;
  • Bayarbaatar, Buuveibaatar ;
  • Belant, Jerrold L. ;
  • Bertassoni, Alessandra ;
  • Beyer, Dean ;
  • Bidner, Laura ;
  • Van Beest, Floris M. ;
  • Blake, Stephen ;
  • Blaum, Niels ;
  • Bracis, Chloe ;
  • Brown, Danielle ;
  • De Bruyn, P. J. Nico ;
  • Cagnacci, Francesca ;
  • Calabrese, Justin M. ;
  • Camilo-Alves, Constança ;
  • Chamaillé-Jammes, Simon ;
  • Chiaradia, Andre ;
  • Davidson, Sarah C. ;
  • Dennis, Todd ;
  • DeStefano, Stephen ;
  • Diefenbach, Duane ;
  • Douglas-Hamilton, Iain ;
  • Fennessy, Julian ;
  • Fichtel, Claudia ;
  • Fiedler, Wolfgang ;
  • Fischer, Christina ;
  • Fischhoff, Ilya ;
  • Fleming, Christen H. ;
  • Ford, Adam T. ;
  • Fritz, Susanne A. ;
  • Gehr, Benedikt ;
  • Goheen, Jacob R. ;
  • Gurarie, Eliezer ;
  • Hebblewhite, Mark ;
  • Heurich, Marco ;
  • Hewison, A. J. Mark ;
  • Hof, Christian ;
  • Hurme, Edward ;
  • Isbell, Lynne A. ;
  • Janssen, René ;
  • Jeltsch, Florian ;
  • Kaczensky, Petra ;
  • Kappeler, Peter M. ;
  • Kauffman, Matthew ;
  • Kays, Roland ;
  • Kimuyu, Duncan ;
  • Koch, Flavia ;
  • Kranstauber, Bart ;
  • LaPoint, Scott ;
  • Leimgruber, Peter ;
  • Linnell, John D. C. ;
  • López-López, Pascual ;
  • Markham, A. Catherine ;
  • Mattisson, Jenny ;
  • Medici, Emilia Patricia ;
  • Mellone, Ugo ;
  • Merrill, Evelyn ;
  • De Miranda Mourão, Guilherme ;
  • Morato, Ronaldo G. ;
  • Morellet, Nicolas ;
  • Morrison, Thomas A. ;
  • Díaz-Muñoz, Samuel L. ;
  • Mysterud, Atle ;
  • Nandintsetseg, Dejid ;
  • Nathan, Ran ;
  • Niamir, Aidin ;
  • Odden, John ;
  • O’Hara, Robert B. ;
  • Oliveira-Santos, Luiz Gustavo R. ;
  • Olson, Kirk A. ;
  • Patterson, Bruce D. ;
  • Cunha De Paula, Rogerio ;
  • Pedrotti, Luca ;
  • Reineking, Björn ;
  • Rimmler, Martin ;
  • Rogers, Tracey L. ;
  • Rolandsen, Christer Moe ;
  • Rosenberry, Christopher S. ;
  • Rubenstein, Daniel I. ;
  • Safi, Kamran ;
  • Saïd, Sonia ;
  • Sapir, Nir ;
  • Sawyer, Hall ;
  • Schmidt, Niels Martin ;
  • Selva, Nuria ;
  • Sergiel, Agnieszka ;
  • Shiilegdamba, Enkhtuvshin ;
  • Silva, João Paulo ;
  • Singh, Navinder ;
  • Solberg, Erling J. ;
  • Spiegel, Orr ;
  • Strand, Olav ;
  • Sundaresan, Siva ;
  • Ullmann, Wiebke ;
  • Voigt, Ulrich ;
  • Wall, Jake ;
  • Wattles, David ;
  • Wikelski, Martin ;
  • Wilmers, Christopher C. ;
  • Wilson, John W. ;
  • Wittemyer, George ;
  • Zięba, Filip ;
  • Zwijacz-Kozica, Tomasz ;
  • Mueller, Thomas
0 Citations0 Mentions42% FAIR0.5 Dataset Index
10.14288/1.0397846January 2020

Data &amp; scripts from Should I stay or should I go? Individual movement decisions during group departures in red-fronted lemurs

This archive contains the data and the R scripts for the analyses in the paper.

Authors

  • Sperber, Anna Lucia ;
  • Kappeler, Peter M. ;
  • Fichtel, Claudia
0 Citations0 Mentions85% FAIR2.1 Dataset Index
10.6084/m9.figshare.7830068January 2019

Data &amp; scripts from Should I stay or should I go? Individual movement decisions during group departures in red-fronted lemurs

This archive contains the data and the R scripts for the analyses in the paper.

Authors

  • Sperber, Anna Lucia ;
  • Kappeler, Peter M. ;
  • Fichtel, Claudia
0 Citations0 Mentions85% FAIR2.1 Dataset Index
10.6084/m9.figshare.7830068.v1January 2019

Data table from Are generalists more innovative than specialists? A comparison of innovative abilities in two wild sympatric mouse lemur species

Data table on which analyses are based.

Authors

  • Malsburg, Johanna Henke-Von Der ;
  • Fichtel, Claudia
0 Citations0 Mentions15% FAIR0.4 Dataset Index
10.6084/m9.figshare.6852359January 2018

Dataset Huebner et al. from Linking cognition with fitness in a wild primate: fitness correlates of problem-solving performance and spatial learning ability

Data sheet with the data supporting the paper

Authors

  • Huebner, Franziska ;
  • Fichtel, Claudia ;
  • Kappeler, Peter M.
1 Citation0 Mentions13% FAIR0.7 Dataset Index
10.6084/m9.figshare.6729188.v1January 2018

Data table from Are generalists more innovative than specialists? A comparison of innovative abilities in two wild sympatric mouse lemur species

Data table on which analyses are based.

Authors

  • Malsburg, Johanna Henke-Von Der ;
  • Fichtel, Claudia
0 Citations0 Mentions15% FAIR0.4 Dataset Index
10.6084/m9.figshare.6852359.v1January 2018

Personality data from Are generalists more innovative than specialists? A comparison of innovative abilities in two wild sympatric mouse lemur species

Data table of open field and novel object tests.

Authors

  • Malsburg, Johanna Henke-Von Der ;
  • Fichtel, Claudia
0 Citations0 Mentions13% FAIR0.3 Dataset Index
10.6084/m9.figshare.6852362January 2018

Personality data from Are generalists more innovative than specialists? A comparison of innovative abilities in two wild sympatric mouse lemur species

Data table of open field and novel object tests.

Authors

  • Malsburg, Johanna Henke-Von Der ;
  • Fichtel, Claudia
0 Citations0 Mentions13% FAIR0.3 Dataset Index
10.6084/m9.figshare.6852362.v1January 2018

Empirical GPS tracking data

No description available

Authors

  • Noonan, Michael J. ;
  • Tucker, Marlee ;
  • Fleming, Christen H. ;
  • Akre, Tom ;
  • Alberts, Susan C. ;
  • Ali, Abdullahi H. ;
  • Altmann, Jeanne ;
  • Castro Antunes, Pamela ;
  • Belant, Jerrold L. ;
  • Beyer, Dean ;
  • Blaum, Niels ;
  • Böhning-Gaese, Katrin ;
  • Cullen Jr., Laury ;
  • Cunha De Paula, Rogerio ;
  • Dekker, Jasja ;
  • Drescher-Lehman, Jonathan ;
  • Farwig, Nina ;
  • Fichtel, Claudia ;
  • Fischer, Christina ;
  • Ford, Adam ;
  • Goheen, Jacob R. ;
  • Janssen, René ;
  • Jeltsch, Florian ;
  • Kauffman, Matthew ;
  • Kappeler, Peter M. ;
  • Koch, Flávia ;
  • LaPoint, Scott ;
  • Markham, A. Catherine ;
  • Medici, Emilia Patricia ;
  • Morato, Ronaldo G. ;
  • Nathan, Ran ;
  • Oliveira-Santos, Luiz Gustavo R. ;
  • Olson, Kirk A. ;
  • Patterson, Bruce D. ;
  • Paviolo, Agustin ;
  • Ramalho, Emiliano E. ;
  • Roesner, Sascha ;
  • Schabo, Dana ;
  • Selva, Nuria ;
  • Sergiel, Agnieszka ;
  • Da Silva, Marina X. ;
  • Spiegel, Orr ;
  • Thompson, Peter ;
  • Ullmann, Wiebke ;
  • Zięba, Filip ;
  • Zwijacz-Kozica, Tomasz ;
  • Fagan, William F. ;
  • Mueller, Thomas ;
  • Calabrese, Justin M.
0 Citations0 Mentions77% FAIR1.9 Dataset Index
10.5061/dryad.v5051j2/1January 2018