), securing their position as
leaders of the co-citation network. This dual role of several top
authors indicates that the field is still developing, and has the
potential for further growth, which can help fill the gaps in covering
various taxa and regions.
The papers undergo peer review, most of them being published in
established journals and methodological correct, as indicated by the
scarcity of comments and rebuttals. However, we found it challenging to
assign modeling and design approaches to a standard nomenclature. This
challenge may arise because the field is relatively new. However, it is
also because, despite the existence of several methodological books and
articles, the field lacks clear standards for modeling workflows and
reporting of results. While there are new papers attempting to fill this
gap (e.g., Kellner et al., 2023), more work and clear guidelines are
needed for standardization (including naming e of model types).
Additionally, there is a need for guidance on what and how to report
metadata, which should include details about studied taxa, study levels,
type of sampling designs,study length, model results, standards of
accuracy (e.g., Araújo et al. 2019).
The study highlights the growing importance of applied hierarchical
modeling in population ecology, providing a powerful tool for monitoring
wildlife distribution and abundance. Despite significant growth,
particularly since 2012, this field remains largely driven by
researchers from developed countries, with a strong focus on mammals and
birds. Key findings emphasize the need for increased collaboration,
especially with researchers from megadiverse regions, to ensure a more
comprehensive understanding of global biodiversity. Additionally,
efforts to standardize modeling and reporting practices are crucial for
advancing the field’s impact. Although hierarchical modeling articles
may not yet receive the high citation counts of other ecological
studies, key authors and journals play a pivotal role in bridging the
gap between applied hierarchical modeling and broader ecological topics.
This suggests that continued growth and influence in the field are
achievable. Ultimately, this research underscores the potential of
applied hierarchical modeling to address critical conservation
challenges. To maximize its impact, researchers, practitioners, and
policymakers should work together to fully harness the potential of this
valuable tool for the preservation of global biodiversity.
References
Audisio, P. (2017). Insect taxonomy, biodiversity research and the new
taxonomic impediments. Fragmenta entomologica, 49(2), 121-124.
Anderson, D., Burnham, K., (2002) Model selection and multi-model
inference. A Practical Information-Theoretic Approach. 2nd Edition.
Springer, New York, NY.
Araújo, M.B., Anderson, R.P., Márcia Barbosa, A., Beale, C.M., Dormann,
C.F., Early, R., Garcia, R.A., Guisan, A., Maiorano, L., Naimi, B.
(2019) Standards for distribution models in biodiversity assessments.
Science advances 5, eaat4858.
Bellard, C., Bertelsmeier, C., Leadley, P., Thuiller, W., Courchamp, F.
(2012) Impacts of climate change on the future of biodiversity. Ecology
Letters 15, 365-377.
Cove, M. V., Kays, R., Bontrager, H., Bresnan, C., Lasky, M., Frerichs,
T., … & Jordan, M. J. (2021). SNAPSHOT USA 2019: a coordinated
national camera trap survey of the United States.
Dail, D., Madsen, L. (2011) Models for estimating abundance from
repeated counts of an open metapopulation. Biometrics 67, 577-587.
Dorazio, R.M., Royle, J.A. (2005) Estimating size and composition of
biological communities by modeling the occurrence of species. Journal of
the American Statistical Association 100, 389-398.
Fiske, I., Chandler, R. (2011) Unmarked: an R package for fitting
hierarchical models of wildlife occurrence and abundance. Journal of
statistical software 43, 1-23.
Gantchoff, M. G., Conlee, L., & L Belant, J. (2022). The effectiveness
of opportunistic public reports versus professional data to estimate
large carnivore distribution. Ecosphere, 13(2), e3938.
Gonzalez, A., Vihervaara, P., Balvanera, P., Bates, A.E., Bayraktarov,
E., Bellingham, P.J., Bruder, A., Campbell, J., Catchen, M.D.,
Cavender-Bares, J., Chase, J., Coops, N., Costello, M.J., Dornelas, M.,
Dubois, G., Duffy, E.J., Eggermont, H., Fernandez, N., Ferrier, S.,
Geller, G.N., Gill, M., Gravel, D., Guerra, C.A., Guralnick, R.,
Harfoot, M., Hirsch, T., Hoban, S., Hughes, A.C., Hunter, M.E., Isbell,
F., Jetz, W., Juergens, N., Kissling, W.D., Krug, C.B., Le Bras, Y.,
Leung, B., Londono-Murcia, M.C., Lord, J.M., Loreau, M., Luers, A., Ma,
K., MacDonald, A.J., McGeoch, M., Millette, K.L., Molnar, Z., Mori,
A.S., Muller-Karger, F.E., Muraoka, H., Navarro, L., Newbold, T.,
Niamir, A., Obura, D., O’Connor, M., Paganini, M., Pereira, H., Poisot,
T., Pollock, L.J., Purvis, A., Radulovici, A., Rocchini, D., Schaepman,
M., Schaepman-Strub, G., Schmeller, D.S., Schmiedel, U., Schneider,
F.D., Shakya, M.M., Skidmore, A., Skowno, A.L., Takeuchi, Y., Tuanmu,
M.N., Turak, E., Turner, W., Urban, M.C., Urbina-Cardona, N., Valbuena,
R., van Havre, B., Wright, E. (2023) A global biodiversity observing
system to unite monitoring and guide action. Nat Ecol Evol.
Hines JE (2006) PRESENCE: Software to Estimate Patch Occupancy and
Related Parame- ters. U.S. Geological Survey, Patuxent Wildlife Research
Center, Laurel, MD. http://www.mbr-pwrc.usgs.gov/software/presence.html
Hochkirch, A., Samways, M.J., Gerlach, J., Bohm, M., Williams, P.,
Cardoso, P., Cumberlidge, N., Stephenson, P.J., Seddon, M.B.,
Clausnitzer, V., Borges, P.A.V., Mueller, G.M., Pearce-Kelly, P.,
Raimondo, D.C., Danielczak, A., Dijkstra, K.B. (2021) A strategy for the
next decade to address data deficiency in neglected biodiversity.
Conserv Biol 35, 502-509.
Kays, Roland, Michael V. Cove, Jose Diaz, Kimberly Todd, Claire Bresnan,
Matt Snider, Thomas E. Lee Jr, et al. 2022. ”SNAPSHOT USA 2020: A Second
Coordinated National Camera Trap Survey of the United States during the
COVID-19 Pandemic.” Ecology e3775.https://doi.org/10.1002/ecy.3775
Kellner, K.F., Smith, A.D., Royle, J.A., Kéry, M., Belant, J.L.,
Chandler, R.B. (2023) The unmarked R package: Twelve years of advances
in occurrence and abundance modelling in ecology. Methods in Ecology and
Evolution 14, 1408-1415.
Kéry M, Schaub M (2012) Bayesian population analysis using WinBUGS: a
hierarchical perspective. Academic Press, San Diego
Li, F., Zhao, X., Li, M., He, K., Huang, C., Zhou, Y., … & Walters,
J. R. (2019). Insect genomes: progress and challenges. Insect molecular
biology, 28(6), 739-758.
Lindenmayer, D.B., Lavery, T., Scheele, B.C. (2022) Why we need to
invest in large-scale, long-term monitoring programs in landscape
ecology and conservation biology. Current Landscape Ecology Reports 7,
137-146.
Maas, B., Pakeman, R.J., Godet, L., Smith, L., Devictor, V., Primack, R.
(2021) Women and Global South strikingly underrepresented among
top‐publishing ecologists. Conservation letters 14, e12797.
MacKenzie, D.I., Nichols, J.D., Hines, J.E., Knutson, M.G., Franklin,
A.B. (2003) Estimating site occupancy, colonisation, and local
extinction when a species is detected imperfectly. Ecology 84,
2200-2207.
MacKenzie, D.I., Nichols, J.D., Lachman, G.B., Droege, S., Andrew Royle,
J., Langtimm, C.A. (2002) Estimating site occupancy rates when detection
probabilities are less than one. Ecology 83, 2248-2255.
MacKenzie, D.I., Nichols, J.D., Royle, J.A., Pollock, K.H., Bailey, L.,
Hines, J.E. (2017) Occupancy estimation and modeling: inferring patterns
and dynamics of species occurrence. Elsevier.
MacKenzie, D.I., Royle, J.A. (2005) Designing occupancy studies: general
advice and allocating survey effort. Journal of applied Ecology 42,
1105-1114.
Mammides, C., Goodale, U.M., Corlett, R.T., Chen, J., Bawa, K.S.,
Hariya, H., Jarrad, F., Primack, R.B., Ewing, H., Xia, X. (2016)
Increasing geographic diversity in the international conservation
literature: A stalled process? Biological Conservation 198, 78-83.
Mandeville, C.P., Nilsen, E.B., Herfindal, I., Finstad, A.G. (2023)
Participatory monitoring drives biodiversity knowledge in global
protected areas. Communications Earth & Environment 4.
Nichols, J.D., Bailey, L.L., O’Connell, A.F., Talancy, N.W., Grant,
E.H.C., Gilbert, A.T., Annand, E.M., Husband, T.P., Hines, J.E. (2008)
Multi-scale occupancy estimation and modelling using multiple detection
methods. Journal of Applied Ecology 45, 1321-1329.
Nita, A. (2019) Empowering impact assessments knowledge and
international research collaboration-A bibliometric analysis of
Environmental Impact Assessment Review journal. Environmental Impact
Assessment Review 78, 106283.
Page, M.J., McKenzie, J.E., Bossuyt, P.M., Boutron, I., Hoffmann, T.C.,
Mulrow, C.D., Shamseer, L., Tetzlaff, J.M., Akl, E.A., Brennan, S.E.,
Chou, R., Glanville, J., Grimshaw, J.M., Hróbjartsson, A., Lalu, M.M.,
Li, T., Loder, E.W., Mayo-Wilson, E., McDonald, S., McGuinness, L.A.,
Stewart, L.A., Thomas, J., Tricco, A.C., Welch, V.A., Whiting, P.,
Moher, D. (2021) The PRISMA 2020 statement: an updated guideline for
reporting systematic reviews. BMJ 372, n71.
Piguet, E., Kaenzig, R., Guélat, J. (2018) The uneven geography of
research on ”environmental migration”. Population and environment 39,
357-383.
Richards, S., Childers, A. and Childers, C. (2018) Editorial overview:
insect genomics: arthropod genomic resources for the 21st century: it
only counts if it’s in the database! Current Opinion in Insect Science,
25, iv–vii.
Royle, J.A. (2004) N‐mixture models for estimating population size from
spatially replicated counts. Biometrics 60, 108-115.
Royle, J.A., Dorazio, R.M. (2008) Hierarchical modeling and inference in
ecology: the analysis of data from populations, metapopulations and
communities. Elsevier.
Royle, J.A., Kery, M. (2007) A Bayesian state-space formulation of
dynamic occupancy models. Ecology 88, 1813-1823.
Valdez, J.W., Callaghan, C.T., Junker, J., Purvis, A., Hill, S.L.,
Pereira, H.M. (2023) The undetectability of global biodiversity trends
using local species richness. Ecography 2023, e06604.
van Strien, A.J., van Swaay, C.A.M., Termaat, T., Devictor, V. (2013)
Opportunistic citizen science data of animal species produce reliable
estimates of distribution trends if analysed with occupancy models.
Journal of Applied Ecology 50, 1450-1458.
Woodcock, B.A., Isaac, N.J., Bullock, J.M., Roy, D.B., Garthwaite, D.G.,
Crowe, A., Pywell, R.F. (2016) Impacts of neonicotinoid use on long-term
population changes in wild bees in England. Nat Commun 7, 12459.
Zipkin, E.F., Dewan, A., Royle, J.A. (2009) Impacts of forest
fragmentation on species richness: a hierarchical approach to community
modelling. Journal of Applied Ecology 46, 815-822.
Brunson, J. S., and Q. D. Read. 2023. ggalluvial: Alluvial Plots in
’ggplot2’.
Csárdi, G., T. Nepusz, V. Traag, S. Horvát, F. Zanini, D. Noom, and K.
Müller. 2023. igraph: Network Analysis and Visualization in R.
Smith, M., A. Ceni, N. Milic-Frayling, B. Shneiderman, E. Mendes
Rodrigues, J. Leskovec, and C. Dunne. 2023. NodeXL: a free and open
network overview, discovery and exploration add-in for Excel from the
Social Media Research Foundation.https://www.smrfoundation.org.
Team, R. C. 2023. R: A Language and Environment for Statistical
Computing.
Smith, A. F., & Alvey, D. (2023). Snapshot Europe.
van Eck, N. J., and L. Waltman. 2023. Manual for VOSviewer version
1.6.19. Leiden University, Centre for Science and Technology Studies
(CWTS), Netherlands.
White, G. C., & Cooch, E. (2001). Program Mark. Mark and recapture
survival rate estimation. Version 9. Retrieved from
http://www.phidot.org/software/mark/downloads/
Wickham, H. 2016. ggplot2: Elegant Graphics for Data Analysis.
Wickham, H., R. François, L. Henry, K. Müller, and D. Vaughan. 2023.
dplyr: A Grammar of Data Manipulation.