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1
Variable responses of individual species to
tropical forest degradation
Robert M. EWERS 1; William D. PEARSE 1; C. David L. ORME 1; Priyanga AMARASEKARE 2; Tijmen DE
LORM 1; Natasha GRANVILLE 1; Rahayu ADZHAR 1,3; David C. ALDRIDGE 4; Marc ANCRENAZ 5,6;
Georgina ATTON 7; Holly BARCLAY 8; Maxwell V. L. BARCLAY 9; Henry BERNARD 10; Jake E. BICKNELL
11; Tom R. BISHOP 1,12,13; Joshua BLACKMAN 14; Sabine BOTH 15; Michael J. W. BOYLE 1,16; Hayley
BRANT 1; Ella BRASINGTON 1; David F.R.P. BURSLEM 17; Emma R. BUSH 18; Kerry CALLOWAY 9; Chris
CARBONE 19; Lauren CATOR 1; Philip M. CHAPMAN 1,20; Vun Khen CHEY 21; Arthur CHUNG 21; Elizabeth
L. CLARE 14,22; Jeremy CUSACK 1,23; Martin DANČÁK 24; Zoe G. DAVIES 11; Charles W. DAVISON 1,25,26;
Mahadimenakbar M. DAWOOD 10; Nicolas J. DEERE 11; Katharine J. M. DICKINSON 27; Raphael K.
DIDHAM 28,29; Timm F. DÖBERT 28,29,30; Rory A. DOW 31,32; Rosie DRINKWATER 14; David P. EDWARDS
33,63; Paul EGGLETON 9; Aisyah FARUK 34; Tom M. FAYLE 14,35; Arman Hadi FIKRI 10; Robert J. FLETCHER
36; Hollie FOLKARD-TAPP 1; William A. FOSTER 4; Adam FRASER 1; Richard GILL 1; Ross E. J. GRAY 1;
Ryan GRAY 37; Nichar GREGORY 1,38; Jane HARDWICK 39; Martina F. HARIANJA 4; Jessica K. HAYSOM 11;
David R. HEMPRICH-BENNETT 14,40; Sui Peng HEON 1,37; Michal HRONEŠ 41; Evyen W. JEBRAIL 10; Nick
JONES 42; Palasiah JOTAN 43; Victoria A. KEMP 14; Lois KINNEEN 44; Roger KITCHING 45; Oliver KONOPIK
46; Boon Hee KUEH 10; Isolde LANE-SHAW 1,47; Owen T. LEWIS 40; Sarah H. LUKE 4,48,49; Emma
MACKINTOSH 1,50; Catherine S. MACLEAN 1; Noreen MAJALAP 21; Yadvinder MALHI 51; Stephanie
MARTIN 1,52; Michael MASSAM 1,53; Radim MATULA 43; Sarah MAUNSELL 45; Amelia R. MCKINLAY 1;
Simon MITCHELL 11; Katherine E. MULLIN 11; Reuben NILUS 21; Ciar D. NOBLE 1,54; Jonathan M.
PARRETT 55; Marion PFEIFER 56; Annabel PIANZIN 10; Lorenzo PICINALI 57; Rajeev PILLAY 36,58; Frederica
POZNANSKY 1,59; Aaron PRAIRIE 1,60; Lan QIE 1,61; Homathevi RAHMAN 10; Terhi RIUTTA 1,51,62; Stephen
J. ROSSITER 14; J. Marcus ROWCLIFFE 19; Gabrielle Briana ROXBY 1; Dave J. I. SEAMAN 11; Sarab S.
SETHI 1,63; Adi SHABRANI 64,65; Adam SHARP 1,66; Eleanor M. SLADE 67; Jani SLEUTEL 68; Nigel STORK 69;
Matthew STRUEBIG 11; Martin SVÁTEK 70; Tom SWINFIELD 4; Heok Hui TAN 71; Yit Arn TEH 56; Jack
THORLEY 4; Edgar C. TURNER 1,4; Joshua P. TWINING 1,72; Maisie VOLLANS 1,40; Oliver WEARN 1,73;
Bruce L. WEBBER 28,29; Fabienne WIEDERKEHR 1,74; Clare L WILKINSON 1,75; Joseph WILLIAMSON 14,76;
Anna WONG 77; Darren C. J. YEO 71,75; Natalie YOH 11,78; Kalsum M. YUSAH 10,79; Genevieve YVON-
DUROCHER 80; Nursyamin ZULKIFLI 81; Olivia DANIEL 1; Glen REYNOLDS 37; Cristina BANKS-LEITE 1
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1 Georgina Mace Centre for the Living Planet, Department of Life Sciences, Imperial College London,
Silwood Park Campus, Buckhurst Road, Ascot SL5 7PY, UK
2 Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA, United
States
3 Department of Geographical Sciences, University of Maryland, College Park, MD, USA
4 Department of Zoology, The David Attenborough Building, University of Cambridge, Cambridge CB2
3QZ
5 Borneo Futures, Bandar Seri Begawan, Brunei
6 Kinabatangan Orang-Utan Conservation Programme, Kota Kinabalu, Sabah, Malaysia
7 Faculty of Health Sciences, University of Bristol, BS8 1UD, UK
8 School of Science, Monash University, Jalan Lagoon Selatan, 47500 Subang Jaya, Selangor Darul
Ehsan, Malaysia
9 Department of Life Sciences, The Natural History Museum London, SW7 5BD, UK
10 Institute for Tropical Biology and Conservation, Universiti Malaysia Sabah, Jalan UMS, 88400 Kota
Kinabalu, Sabah, Malaysia
11 Durrell Institute of Conservation and Ecology (DICE), School of Anthropology and Conservation,
University of Kent, Canterbury, CT2 7NR, UK
12 Department of Zoology and Entomology, University of Pretoria, Pretoria, South Africa
13 School of Biosciences, Cardiff University, Cardiff, UK
14 School of Biological and Behavioural Sciences, Queen Mary University of London, London, E1 4NS,
UK
15 School of Environmental and Rural Science, Faculty of Science, Agriculture, Business and Law, Uni-
versity of New England, Armidale NSW 2351, Australia
16 School of Biological Sciences, The University of Hong Kong, Hong Kong City, Hong Kong
17 School of Biological Sciences, University of Aberdeen, Aberdeen, AB24 3UU UK
18 Royal Botanic Gardens Edinburgh, Arboretum Place, Edinburgh EH3 5NZ, UK
19 Institute of Zoology, Zoological Society of London, NW1 4RY, UK
20 BSG Ecology, Worton Park, Worton, Witney, Oxfordshire, OX29 4SX, UK
21 Forest Research Centre, Sabah Forestry Department, PO Box 1407, 90715 Sandakan, Sabah, Malay-
sia
22 Department of Biology, York University, Toronto, Ontario, M3J 1P3, Canada
23 Centro de Modelación y Monitoreo de Ecosistemas, Universidad Mayor, Chile
24 Department of Ecology and Environmental Sciences, Faculty of Science, Palacký University,
Šlechtitelů 27, CZ-78371, Czech Republic
25 Center for Biodiversity Dynamics in a Changing World (BIOCHANGE), Department of Biology, Aar-
hus University, Ny Munkegade 114, DK-8000 Aarhus C, Denmark
26 Center for Ecological Dynamics in a Novel Biosphere (ECONOVO), Department of Biology, Aarhus
University, Ny Munkegade 114, DK-8000 Aarhus C, Denmark
27 Department of Botany, University of Otago, PO Box 56, Dunedin, 9054, New Zealand
28 CSIRO Health and Biosecurity, Centre for Environment and Life Sciences, 147 Underwood Ave, Flo-
reat, WA, 6014 Australia
29 School of Biological Sciences, The University of Western Australia, 35 Stirling Highway, Crawley,
WA 6009, Australia
30 Faculty of Science, University of Alberta, AB, T6G 2E1, Canada
31 Institute of Biodiversity and Environmental Conservation, Universiti Malaysia Sarawak, Malaysia
32 Naturalis Biodiversity Centre, PO Box 2300 RA Leiden, The Netherlands
33 Ecology and Evolutionary Biology, School of Biosciences, University of Sheffield, S10 2TN, UK
34 Royal Botanic Gardens, Kew, Wakehurst, Ardingly, Haywards Heath, West Sussex RH17 6TN, UK
35 Biology Centre of the Czech Academy of Sciences, Institute of Entomology, Branisovska 31, 370 05
Ceske Budejovice, Czech Republic
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36 Department of Wildlife Ecology and Conservation, University of Florida, 110 Newins-Ziegler Hall,
Gainesville, FL 32611, USA
37 South East Asia Rainforest Research Partnership, Danum Valley Field Centre, 91112 Lahad Datu,
Sabah, Malaysia
38 EcoHealth Alliance, 520 Eighth Ave., Ste 1200, New York, NY 10018, USA
39 Marine Resources Unit, Department of Environment, Grand Cayman, Cayman Islands
40 Department of Biology, University of Oxford, Oxford OX1 3PS, UK
41 Department of Botany, Faculty of Science, Palacký University, Šlechtitelů 27, CZ-78371, Czech Re-
public
42 Department of Mathematics, Imperial College London, South Kensington Campus, London SW7
2AZ, UK
43 Department of Forest Ecology, Faculty of Forestry and Wood Sciences, Czech University of Life Sci-
ences Prague, Czech Republic
44 Department of Sustainable Land Management, School of Agriculture, Policy and Development,
University of Reading, Berkshire, UK
45 School of Environmental and Natural Sciences, Griffith University, Brisbane, Queensland 4111,
Australia
46 Department of Animal Ecology and Tropical Biology, Biocenter, University of Wuerzburg, Am Hub-
land, 97074 Würzburg, Germany
47 Department of Wood and Forest Science, Laval University, Quebec, Quebec, G1V 0A6, Canada
48 School of Biosciences, University of Nottingham, Sutton Bonington Campus, Nr Loughborough,
Leicestershire, LE12 5RD, UK
49 School of Biological Sciences, University of East Anglia, Norwich, Norfolk, NR4 7TJ, UK
50 Forest Research Institute, University of the Sunshine Coast, Sippy Downs, Queensland, Australia
51 Environmental Change Institute, School of Geography and the Environment, University of Oxford,
Oxford OX1 3QY, UK
52 Field Programmes Department, Durrell Wildlife Conservation Trust, Les Augres Manor La Profonde
Rue Trinity Jersey JE3 5BP
53 School of Biosciences, The University of Sheffield, Western Bank, Sheffield, S10 2TN, UK
54 University of East Anglia, School of Environmental Sciences, Norwich Research Park, Norwich, Nor-
folk, NR4 7TJ, UK
55 Evolutionary Biology Group, Faculty of Biology, Adam Mickiewicz University, Poznań, Poland
56 School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, NE1
7RU, UK
57 Dyson School of Design Engineering, Imperial College London, South Kensington Campus, London
SW7 2AZ, UK
58 Natural Resources and Environmental Studies Institute, University of Northern British Columbia,
Prince George, British Columbia, Canada
59 Centre for Ecology and Conservation, School of Biosciences, University of Exeter, Penryn Campus
TR10 9FE, UK
60 Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO, USA
61 Department of Life Sciences, School of Life and Environmental Sciences, University of Lincoln, Bray-
ford Pool, Lincoln, LN6 7TS, UK
62 Department of Geography, University of Exeter, Streatham Campus, Exeter, EX4 4QJ, UK
63 Department of Plant Sciences, University of Cambridge, Cambridge, UK
64 Wildlife Biology Program, Division of Biological Sciences, University of Montana, Missoula, MT,
59812, USA
65 WWF-Malaysia, Sabah Office, 88000 Kota Kinabalu, Sabah, Malaysia
66 Conservation & Fisheries Directorate, Ascension Island Government, Georgetown, Ascension Island
67 Asian School of the Environment, Nanyang Technological University, Singapore City, Singapore
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68 Ecology and Biodiversity Research Group, Departement of Biology, Vrije Universiteit Brussel, Brus-
sels, Belgium
69 Centre for Planetary Health and Food Security, Griffith University, Brisbane, Queensland 4111,
Australia
70 Department of Forest Botany, Dendrology and Geobiocoenology, Faculty of Forestry and Wood
Technology, Mendel University in Brno, Brno, Czech Republic
71 Lee Kong Chian Natural History Museum, National University of Singapore, Singapore
72 New York Cooperative Fish and Wildlife Research Unit, Department of Natural Resources, 321 Fer-
now Hall, Cornell University, Ithaca, NY 14853-3001
73 Fauna & Flora International – Vietnam Programme, 46 Tran Kim Xuyen Lane, Yen Hoa Ward, Cau
Giay, Hanoi, Vietnam
74 Institute of Microbiology, Department of Biology, ETH Zürich, Zürich, Switzerland
75 Department of Biological Sciences, National University of Singapore, Singapore
76 Centre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environ-
ment, University College London, London, UK
77 Malaysian Nature Society, Sabah Branch, JKR 641 Jalan Kelantan, Bukit Persekutuan 50480, Kuala
Lumpur, Malaysia
78 The Nelson Institute for Environmental Studies, University of Wisconsin-Madison, Madison, WI,
USA
79 Royal Botanic Gardens, Kew, Richmond, London, TW9 3AE, UK
80 School of Physiology, Pharmacology and Neuroscience, University of Bristol, Bristol BS8 1QU, UK
81 Faculty of Forestry and Environment, Universiti Putra Malaysia, Seri Kembangan, 43400 Selangor,
Malaysia
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Abstract
The functional stability of ecosystems depends greatly on interspecific differences in responses to
environmental perturbation. However, responses to perturbation are not necessarily invariant
among populations of the same species, so intraspecific variation in responses might also contribute.
Such inter-population response diversity has recently been shown to occur spatially across species
ranges, but we lack estimates of the extent to which individual populations across an entire
community might have perturbation responses that vary through time. We assess this using 524 taxa
that have been repeatedly surveyed for the effects of tropical forest logging at a focal landscape in
Sabah, Malaysia. Just 39 % of taxa – all with non-significant responses to forest degradation – had
invariant responses. All other taxa (61 %) showed significantly different responses to the same forest
degradation gradient across surveys, with 6 % of taxa responding to forest degradation in opposite
directions across multiple surveys. Individual surveys had low power (< 80 %) to determine the
correct direction of response to forest degradation for one-fifth of all taxa. Recurrent rounds of
logging disturbance increased the prevalence of intra-population response diversity, while
uncontrollable environmental variation and/or turnover of intraspecific phenotypes generated
variable responses in at least 44 % of taxa. Our results show that the responses of individual species
to local environmental perturbations are remarkably flexible, likely providing an unrealised boost to
the stability of disturbed habitats such as logged tropical forests.
Introduction
Species differ in their traits, with the implication that they will respond differently to the same
environmental perturbation (1, 2). This interspecific “response diversity” has been identified as a key
determinant of community and ecosystem stability for several decades (3, 4). Yet there is newly
emerging evidence that perturbation responses within species are surprisingly variable, because any
given species might respond differently to the same perturbation depending on where it experiences
that perturbation (5): the population-level responses of individual species vary according to position
within their geographic range (6), climate envelope (7) and macroclimatic conditions (8).
Consequently, the “response traits” that purportedly define how a given species will respond to
environmental change (9) may not have a fixed relationship with species’ morphological traits (10),
despite this being widely assumed in many analyses (2, 11, 12). What we don’t yet know, however, is
whether the population-level responses at a single location are fixed and invariant through time, or
whether those responses might also vary. Such variation might arise through population-level
turnover in the phenotypes of individuals, and in response to the ever-changing environmental
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conditions in which local ecosystems are embedded. If it exists, any such local, intra-population
response diversity through time might be expected to boost the stability of disturbed ecosystems (3,
13).
Here, we directly quantified the degree of intra-population response diversity by comparing taxon-
specific occurrence patterns described in multiple surveys that were collected within a single
landscape: the SAFE Project in Sabah, Malaysia (14). Data were collected along a forest degradation
gradient defined by variation in logging intensity, which we quantify as the percentage of biomass
reduction. Logging in tropical rainforests is often a recurrent perturbation, and our sites are no
different. Sites were logged between zero and four times between 1970 and 2008 (15), and many
underwent an additional round of salvage logging between 2012 and 2015. Sites at SAFE therefore
extend across all levels of biomass reduction, encompassing primary forest with no (0 %) biomass
removal, areas of light and moderate selective harvesting of trees, through to salvage logged and
clear-felled sites with virtually all (100 %) above-ground biomass removed. Habitat degradation
gradients of this magnitude should generate strong, predictable impacts on the occurrence patterns
of individual taxa, and as such it represents a good system in which to quantify the degree of intra-
population response diversity.
Our analysis encompassed 119 single-year surveys that included at least one taxon that was also
sampled in at least one other survey. Individual surveys – including those conducted on the same
taxon – varied in one or more dimensions of survey method, sample sites and year (Table S1),
reflecting variation in the study design process of individual researchers. While all surveys were
conducted within a single year, we stress that our definition of single-year survey does not imply
that each survey conducted just a single site visit. Sixty-six of the 119 surveys (55 %) conducted
repeat site visits within the year, meaning the researchers had sampled more intensively than a
snapshot survey in which a site is visited just a single time. Data from multiple site visits within a
year were aggregated to represent a single survey for analysis.
There were 1,258 taxa observed in two or more of these spatially overlapping, single-year surveys, of
which 524 had high enough occurrences to be modelled in multiple surveys (n ≥ 5 occurrences in
each of ≥ 2 surveys). Sensitivity analysis demonstrated that this choice of occurrence threshold
ensured the results and conclusions we present are conservative estimates of response diversity (SI
Appendix; Fig. S1). The 524 taxa included 122 plants, 205 invertebrates, 17 fish, 2 reptiles, 17
amphibians, 100 birds and 61 mammals. We focus our analysis on patterns of taxon occurrence.
Taxon occurrence is a simple, commonly employed analysis of biodiversity patterns that should be
more robust to among-year variation in population sizes than analyses based on abundance (16, 17),
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and therefore represents a conservative test of response diversity. We fitted binomial general linear
models to presence-absence data for 1,942 taxa × survey combinations, from which we recorded
two metrics: (1) the statistical significance of the occurrence pattern, categorised into significant (p <
0.05) or non-significant (p ≥ 0.05) (sensitivity analysis demonstrated little impact of the choice of p-
value on our conclusions; SI Appendix and Fig. S1); and (2) the slope and intercept of the occurrence
pattern. These two metrics can be combined to define four types of response diversity in empirical
data (18) (Fig. 1): (A) invariant, where responses are statistically indistinguishable; (B) magnitude,
where all responses are statistically significant and have a common direction, but have variable slope
estimates; (C) uncertainty, where some responses are statistically significant but others are not; and
(D) sign changes, where statistically significant responses occur but in opposite directions.
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Figure 1: Examples of intra-population response diversity to a gradient of forest degradation. In all
panels, forest degradation is represented as a percentage reduction in aboveground biomass, where
zero represents the median biomass in unlogged forest. Each line displays a fitted model generated
to empirical data collected from a different survey of that particular taxon. Shaded polygons
represent the 95 % confidence intervals. Statistically non-significant relationships are displayed as an
intercept-only fitted model. (A) Invariant: all observed responses of that taxon to forest degradation
in different surveys are statistically indistinguishable (e.g. the tree genus Vitex). (B) Magnitude:
responses of that taxon are all statistically significant and have the same direction of effect, but have
slope and/or intercept estimates that differ (e.g. Bornean yellow muntjac Muntiacus atherodes). (C)
Uncertainty: responses of that taxon vary in their statistical significance (e.g. Yellow breasted
flowerpecker Prionochilus maculatus). (D) Sign: responses of that taxon are statistically significant,
but have response patterns in opposing directions (e.g. dung beetle Onthophagus mulleri).
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High intra-population response diversity of tropical forest taxa
Almost two-thirds (61 %) of all taxa exhibited response diversity, and our consistently conservative
approach to the analysis means this estimate represents a lower bound (Fig. S1). We found 154 taxa
(29 %) with magnitude response diversity, having response patterns that were consistent in terms of
statistical significance and directions of effect, but where the slope or intercept of the observed
effect varied significantly among surveys (Fig. 1B). Most commonly, response diversity was in the
form of uncertainty, (n = 254, 48 %; Fig. 2A), with some surveys finding non-significant response
patterns but others finding statistically significant trends (Fig. 1C). This proportion is higher than
would be expected by chance (12-41 %, SI Appendix), indicating this result is not a spurious one
emerging from the combination of Type I (false positive) and Type II (false negative) sampling errors
across multiple surveys. Finally, 6 % of taxa (n = 32) displayed the most extreme form of intra-
population response diversity – sign diversity – where repeated surveys detected statistically
significant response patterns in opposing directions (Fig. 1D). The latter three classes are not
mutually exclusive, and 23 % of taxa (n = 122) exhibited multiple forms of response diversity (Fig.
2A). For example, a taxon with three surveys might have one with a non-significant result and two
with statistically significant responses in opposite directions, demonstrating both uncertainty and
sign class. Our results provide quantitative insight into the intra-population response diversity of
individual taxa to an environmental gradient at a single location, and indicate that more than one
half of tropical forest taxa might demonstrate remarkably flexible responses to habitat degradation.
Less than half of all taxa (39 %; n = 206) had invariant responses to the forest degradation gradient
(Fig. 2A), in which all surveys gave results that were indistinguishable in terms of their statistical
significance, direction and magnitude of effect (Fig. 1A). The proportion of taxa with fully invariant
results varied across broad taxonomic groupings, varying from zero in fish to more than half for
plants (Fig. 2B). However, all of the 206 taxa (100 %) with invariant response patterns also had no
significant response to the forest degradation gradient in any survey. By contrast, there were 318
taxa that exhibited a significant response in at least one survey, and not one of those (0 %)
responded in a fully invariant manner in all surveys, suggesting intra-population response diversity is
the norm among taxa with meaningful habitat preferences.
Some of the intra-population response diversity we observed can be ascribed to life history
characteristics of the taxa. The number of taxa exhibiting each of the four response diversity classes
differed from a null expectation for all taxonomic groups (Fig. S2; Goodness of fit test,
> 15.9,
p < 0.015). Plants were more likely to have invariant responses than expected by chance, reflecting
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the fact that trees are long-lived and stationary organisms. Repeated surveys of vegetation plots are
always likely to detect the same individuals and thereby generate invariant response patterns.
However, different studies with alternative spatial designs, or that examine different life history
stages such as adults versus seedlings, still generated variable results. Mammals, by contrast, were
more likely to exhibit response diversity in the form of both the magnitude and the sign classes. This
pattern probably reflects the highly mobile nature of large mammals, which might allow them to
rapidly re-distribute in response to continuously changing local conditions, including the spatial and
temporal patchiness of fruiting events.
Figure 2. Broad taxonomic patterns in the replicability of single-year biodiversity surveys. (A) Number
of taxa exhibiting each of the four classes of intra-population response diversity (see Fig. 1 caption
for definitions). Classes are not all mutually exclusive, which is displayed with partially overlapping
bars. (B) Bootstrapped estimates of the proportion of taxa with invariant, statistically
indistinguishable response patterns across multiple surveys. Thick line represents the median, boxes
the 1st and 3rd quantiles respectively, and whiskers the range.
Causes of intra-population response diversity
Intra-population response diversity can arise through spurious or ecologically meaningful
mechanisms. First, variation in study design and field methods might generate spurious differences
in observed response patterns that have nothing to do with the ecology of the species themselves.
By contrast, ecologically meaningful response diversity might arise in one of two ways. First,
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variation in the phenotype of individuals can influence how they respond to disturbances. Any
temporal turnover in the phenotypic composition of populations, which could be driven by selection
forces linked to the forest degradation or varying environmental conditions, might contribute to the
intra-population response diversity we have observed. We have no repeated morphological or
genomic measurements that would allow us to detect or quantify this effect. And second,
meaningful response diversity that might arise from uncontrollable and unmeasured environmental
variation (18). Specific examples might include human disturbance such as hunting intensity and
logging activity, and year-to-year variation in animal population sizes and movements driven by
phenological events such as fruiting, or by climatic variation and extremes such as El Niño
oscillations. When comparing the occurrence patterns from two surveys, we can only definitively
rule out spurious response diversity, and by elimination confirm the presence of meaningful
response diversity, if a pair of surveys used identical sampling methods in identical sampling sites. In
these cases, any inconsistency in the taxon-specific response patterns between surveys must be due
to either unaccounted for environmental variation or phenotypic turnover.
We confirmed the presence of meaningful response diversity in 44 % of taxa (174 out of 397 that
had overlapping methods and sampling sites). A roughly equal number of taxa (n = 168, 42 %) had
variable response patterns that could not be definitively confirmed as being meaningful due to
variations in sampling sites and/or methods, and we therefore consider them to be confirmed
examples of spurious response diversity. Both estimates should be considered to represent the
lower bound of the actual values, however, as any survey pair could simultaneously exhibit both
meaningful and spurious response diversity and there is no statistical method that can quantify this
overlap.
We investigated three potential hypotheses that might explain the presence of meaningful intra-
population response diversity in our data. There was no effect of the number of years between
surveys on the probability of two surveys giving invariant results (Fig S3A; binomial GLM:
= 1.05,
p = 0.30), nor was there a discernible effect of El Niño events (Fig. S3B;
= 0.84, p = 0.66), despite
the latter having impacted forest growth patterns both through time and across space at our study
site (19). We did, however, find that an extended logging event that occurred in the middle of our
decade of observations, and that further reduced the biomass across large portions of the SAFE
Project study area, influenced the pattern of survey results (Fig. S3C;
= 37.4, p < 0.001). We
found increased intra-population response diversity for survey pairs occurring within the logging
events relative to survey pairs occurring in non-disturbed years, indicating that species responses are
more variable during extreme land use change events. This increased response diversity did not
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occur equally across taxonomic groups (logging x taxonomic group interaction effect:
= 41.5, p
< 0.001), but was instead driven by a reduction in the proportion of invariant responses of bird and
mammal taxa. These taxa both have relatively high mobility, so their increased response diversity
may reflect a tendency to move away from active disturbances.
Spurious response diversity, meanwhile, arose from variation in the methodology and fine details of
individual studies, with the exact effect varying almost species-by-species (SI Appendix). One
possible implication is that variation in the way sample sites are distributed along an environmental
gradient may exert considerable, undetected influence over the outcome of a biodiversity survey.
Even unconscious bias in the choices of individual researchers about exactly where in the vicinity of a
planned sample point to set a quadrat or place a trap could be affecting the outcome of surveys (20).
Generating reliable results from ecological surveys
Much of ecology and conservation relies on single-year and snapshot surveys. Close to half (44 %) of
the studies published in the journal Ecology present results based on data from a single year, and
many of the world’s largest conservation NGOs rely on rapid biodiversity assessments to prioritise
their actions (21). Drawing general inference from single-year studies on taxa with intra-population
response diversity could easily lead to misleading conclusions about biodiversity patterns (17), and
these could, in turn, lead to poor management and conservation decision-making.
We estimate that any given taxon needs to be analysed in each of three surveys to gain reliable
insight into the impacts of forest degradation (Fig. 3A). Of the four response diversity classes, getting
the direction (sign) of an effect wrong is the most immediately problematic: it could lead directly to
management decisions that are the exact opposite of what is needed. We therefore used Bayesian
hierarchical models to estimate the observed variation within and among taxon-specific surveys, and
so determine the probability that analysis of a single-year survey will return the correct sign. This
probability was less than 80 % – the assumed power of standard statistical tests – for one fifth of all
taxa (n = 109; 21 %), but varied significantly among taxonomic groups (Fig. 3B, Kruskal-Wallis:
=
78.9, p < 0.001). Birds were most likely to return correct signs from a single survey, while
invertebrates were the least likely. On average, three surveys were needed to ensure a 90 %
probability of getting the right direction of effect for 90 % of taxa (Fig. 3).
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Figure 3. The probability of an analysis returning the correct sign (direction) of a taxon’s response to
forest degradation as a function of the number of single-year surveys in which that taxon was
detected. (A) Each grey line represents an analysis for one taxon (n = 494). (B) Violin plots showing
the distribution of probabilities of a single-year survey returning the correct sign for seven taxonomic
groups. Points indicate the probability for individual taxa within each group.
Implications for ecology and conservation management
Our analyses raise the uncomfortable possibility that three out of every five taxon-specific results
published in single-year studies may be unreliable representations of biodiversity patterns, whether
that be through spurious methodological issues or due to meaningful intra-population response
diversity. Yet our results should not be a complete surprise and do not appear to be unique to our
study site. Inter-annual variation in the spatial distribution of species has been reported for taxa as
diverse as birds in Australia (17), stream invertebrates in Finland (22) and plants in China (23).
Similarly, it is becoming apparent that data spanning a decade or more are needed to obtain
consistent results in ecological field experiments (24, 25), and time lags in the responses of species
to environmental impacts (26) can mean the results of short-term studies can generate
unrepresentative results.
Given such high levels of intra-population variation in response patterns, how can we generate
conclusive insights in ecology and conservation? Intra-population response diversity means clear,
definitive answers to biodiversity and conservation questions cannot be obtained from short funding
cycles. Grants to collect new field data need to be awarded for longer durations, the continuation of
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14
multi-taxa biodiversity time series along environmental gradients should be prioritised, and studies
specifically targeted at repeating earlier work should be supported. Until then, the most efficient
way forward will likely be to find new ways that reliably transfer results among study sites. Early
indications are that such transferability may be low (5), but for predictable reasons including the
location of a site relative to species’ geographic ranges (6) and climatic tolerances (7). Clearer
understanding of how these macroecological patterns influence site- and species- specific
biodiversity patterns will be key to developing new predictive frameworks to extrapolate findings
from heavily studied sites to regions lacking equivalent data (5). Such frameworks will provide a
means to maximise the utility of the data that do exist, help contextualise the generality of
ecological conclusions arising from single-year studies, and give the empirical evidence needed to
support urgent decision making at national and regional scales.
The functional stability of ecosystems can be promoted through the diversity of relationships
between species and the environment (3), with communities containing suites of species that vary in
their responses to environmental gradients better able to stabilise ecosystem properties than
communities lacking equivalent response diversity (4). Temporal or spatial variation in the responses
of individual populations is similarly expected to contribute to the stability of ecosystems (27), but
that contribution is unlikely to be equal among the different classes of response variability: we
expect variation in the sign of a response will make the strongest contribution to stability, followed
by the uncertainty class and with variable response magnitudes having the smallest impact. Our data
demonstrate intra-population response diversity may be the norm rather than the exception, and
that year-to-year variation in ecological context might mediate, or even reverse, species responses
to environmental gradients. This flexibility in the responses of individual species to environmental
changes might ensure human-modified environments, like logged and degraded tropical rainforests,
are more stable and more resilient ecosystems than expected.
Acknowledgements
The SAFE Project was supported by the Sime Darby Foundation. Site access and research permits
were provided by the Maliau Basin Management Committee, Sabah Foundation, Benta Wawasan,
Sabah Softwoods, Innoprise Foundation, Sabah Forestry Department, and the Sabah Biodiversity
Centre. RME is supported by the NOMIS Foundation. Data collection was funded by Australian
Research Council grant DP140101541; Bat Conservation International; British Council Newton-Ungku
Omar Fund; British Ecological Society grant 3256/4035; Cambridge University Commonwealth Fund;
Cambridge Trust; Imperial College London SSCP DTP grant NE/L002515/1; Jardine Foundation;
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15
Malaysia Industry Group for High Technology; Panton Trust; Ministry of Education, Youth and Sports
of the Czech Republic grant INTER-TRANSFER LTT19018; Primate Society of Great Britain; ProForest;
Royal Society of London grant RG130793; Sime Darby Foundation; S.T. Lee Fund; Tim Whitmore
Fund; University of Kent; Universiti Malaysia Sabah; UK Natural Environment Research Council grants
NE/K016253/1, NE/K016407/1, NE/K016148/1, NE/K0106261/1, NE/K015377/1, NE/L002582/1,
NE/P00363X/1, and studentship 1122589; UK Research and Innovation; Universiti Malaysia Sabah,
University of Florida Institute of Food and Agricultural Sciences; Varley Gradwell Travelling
Fellowship; World Wildlife Fund for Nature. Data collection was supported by Saloni Barsrur, Susan
Benedick, Victoria Bignet, Stephen Brooks, Keiron Brown, Stephen Butler, Daniel Carpenter, Kristina
Graves, Herry Heroin, Alex Kendall, Darren Mann, Sol Milne, John Mumford, Derek Shapiro, Kathryn
Sieving, John Sugau, Elizabeth Telford, Bradley Udell and Bakhtiar Effendy Yahya.
Author contributions
RME designed the study, conducted the analyses and drafted the manuscript. WDP, CDLO, PA, TdL,
GR and CBL supported the data analysis, helped interpret the results and edited the manuscript. All
other authors contributed field data and checked the manuscript.
Materials and methods
Data analysis and construction of figures were conducted in the R v4.02 computing environment
(28), using the packages arm (29), dplyr (30), lme4 (31), MuMIn (32), paletteer (33), rstanarm (34,
35), safedata (36) and scales (37).
Taxa occurrence data
We compiled taxa records from 47 data sources, of which 45 were published on the SAFE Project
data repository (38) and the remaining 2 were presented in published papers (39, 40) (Table S1).
Data were collected in the eleven-year period 2010 to 2020. We accepted both presence-absence
and abundance data for the analysis, but restricted it to records where the sampling locations had
known geographic coordinates. Where data sources contained data generated by multiple sampling
methods, they were split to consider each method as a separate survey. Similarly, data sources
comprising samples collected from multiple years were also split to consider each year as a different
survey.
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Only identified taxa were analysed, although not all taxa were identified to species. We rejected any
taxa identified to less than ordinal level. Morphospecies represent a particularly difficult challenge
for analyses of replicability, because the within-survey codes used to identify them are not
consistent among surveys. This means taxa cannot be accurately matched and therefore compared
among surveys. To surmount this challenge, we aggregated morphospecies by genus within
individual surveys, which allowed us to match taxa among surveys. Genus is a commonly used level
of taxonomic resolution used in tropical analyses of diverse taxa such as trees (41) and ants (42), and
taxa aggregated in this way accounted for just 6 % (n = 32) of all taxa in our analysis.
The median number of surveys per taxon was 3.0 (range: 2-15), and the median number of pairwise
comparisons per taxon was also 3.0 (range: 1-105).
Quantifying forest degradation
Taxa at the SAFE Project were collected along a tropical rainforest degradation gradient that runs
from unlogged, old growth forest in strictly protected areas, along a gradient of logging damage
going from low-level, selective extraction of individual trees within water catchments through to
high-intensity, salvage logged forest with no restrictions on tree harvesting, and ends with oil palm
plantation with palms that ranged in age from 5 – 20 years old. We used Aboveground Carbon
Density (ACD, Mg.ha-1) derived from airborne LiDAR data (43, 44) as a base metric from which to
quantify habitat degradation. ACD values ranged from 273 Mg.ha-1 in unlogged forest through to just
1 Mg.ha-1 in deforested patches. For ease of interpretation, we converted these to a percentage
reduction in biomass density relative to the median biomass density observed in unlogged forest
(230 Mg.ha-1).
We used maps of above-ground carbon density generated from LiDAR data collected in November
2014 (43) and again in April 2016 (44). These dates approximately bracket a salvage logging round
during which forest quality was greatly reduced across much of the study area. Each survey was
assigned the forest quality metrics that were collected closest in time to the date of the taxa record.
Sampling units varied among the individual surveys, with taxa recorded either at specific point
locations (e.g. insect traps), along transects (e.g. fish censes), or within a polygon sampling area (e.g.
tree plots). We followed Pfeifer et al. (45) by implementing a 1 km buffer area around each sampling
unit and averaging the forest quality metrics over all 1-ha pixels within that buffer. We implemented
a Gaussian distance weighting that weighted pixels by distance from sampling unit, ensuring pixels
located far from the sampling units carried less weight than those immediately adjacent.
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We opted to restrict all taxa to be analysed in response to forest quality at a single spatial scale to
keep our estimates of response diversity as simple and as conservative as possible. Allowing taxa to
vary in the spatial scale of their response among surveys introduces an additional way in which they
might have a diversity of response patterns, making it more likely that they would be classified as
demonstrating intra-population response diversity. Restricting all taxa to a single spatial scale
therefore represents a conservative estimate of the proportion of taxa exhibiting response diversity.
Quantifying and summarising intra-population response diversity
Occurrence models: To combine such disparate sampling methods across such a wide range of taxa,
we standardised all taxa records to presence-absence data for analysis. We used univariate, binomial
generalised linear models (GLMs) to model taxon occurrence in response to forest degradation, with
models fitted to individual surveys. We only fitted models to taxon × survey combinations where the
taxon had n ≥ 5 presence records within that particular survey. For each taxon x survey combination,
we fitted a model containing a single linear predictor allowing for a sigmoid pattern of occurrence
along the forest quality gradient, and estimated the statistical significance of that model using a log-
likelihood ratio test comparing the fitted model to a null model.
Controlling for detectability in analyses of species occurrence patterns is often recommended, but
such analyses have substantially higher data requirements than typically exist for all taxa within
biodiversity surveys (46). In exploratory analyses we found such models routinely failed to converge.
This is commonly the case for taxa in the tropics where communities have large numbers of
predominantly rare species with low detection probabilities, which are known issues that prevent
detectability-based models from converging (46). Moreover, statistical estimates of tropical species
responses to ecological gradients do not notably vary between models that incorporate or ignore
detection probability (46), so there is no reason to expect the use of detectability-corrected
modelling approaches to influence our main conclusions.
Categorising response diversity: For the 524 taxa that were detected and modelled from ≥ 2 surveys,
we compared model results and grouped taxa according to four classes of response diversity, based
on the four classes of ecological context-dependence described by Catford et al (18).
A. Invariant: Taxa were considered to have invariant, fully replicable occurrence patterns if all
models of that taxa agreed on two criteria:
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i. the pattern of significance; binarized into significant (p < 0.05) or non-significant (p ≥
0.05); and
ii. the slope and intercept of the fitted model: determined by using the estimate and
accompanying standard error to statistically test for a difference among all pairwise
combinations of models of the same taxa (47).
B. Magnitude: Taxa were considered to vary only in the magnitude of the occurrence pattern if
they:
i. had a consistent pattern of significance, but
ii. had slope and/or intercept estimates that varied significantly among surveys.
C. Uncertainty: Taxa were considered to vary in their statistical certainty if they had an
inconsistent pattern of statistical significance, exhibiting both a statistically significant and
statistically insignificant response in one or more surveys respectively.
D. Sign: Taxa were considered to vary in sign if they had statistically significant response
patterns with opposing signs, meaning in some surveys their occurrence increased with
increasing forest degradation but in others their occurrence decreased.
To compare response diversity among broad taxonomic groupings, we categorised all taxa as
belonging to one of plant, invertebrate, fish, amphibian, reptile, bird or mammal groups. For each
taxonomic group, we took 1,000 bootstrapped samples of the binary responses representing
whether the individual taxa within that group lacked (statistical class 1) or exhibited response
diversity (one or more of statistical classes 2, 3 or 4). These bootstrapped samples were used to
generate distributions describing the proportion of taxa per group with response diversity.
Setting a null expectation for the proportion of taxa with response diversity: Statistical techniques,
such as the binomial GLM we used to classify response patterns as significant or non-significant,
have standard Type I and Type II error rates of 5 and 20 % respectively, meaning the probability of
true detection (statistical power) is 80 %. We used these values to set bounds on the proportion of
taxa that could have statistically inconsistent results (‘Uncertainty’ response diversity class; Fig. 1C)
for spurious reasons. The lower bound is estimated by assuming that all taxa have a “true” response
in which they are not impacted by habitat degradation. In this scenario, the probability of correctly
reporting a non-significant result in one survey (80 %) is multiplied by the probability of incorrectly
detecting a significant result where none exists (Type I error; 5 %), meaning 4 % of the pairwise
comparisons we analyse would be expected to spuriously report response diversity. Most taxa we
examined had > 2 pairwise comparisons, and the probability that at least one pair of surveys for a
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given taxon returns a spurious result () scales according to the number of pairwise comparisons
(), such that . The upper bound is estimated by assuming that all taxa have
a “true” response in which they are significantly impacted by the habitat degradation gradient. In
this case, we multiply the probability of one survey accurately detecting the significant pattern
(power; 80 %) by the probability of the second survey failing to detect the significant pattern that
exists (Type II error; 20 %), suggesting 16 % of pairwise comparisons might be expected to spuriously
report response diversity. Scaled up to taxon level, this gives a probability of generating a spurious
result of .
For each taxon, we estimated and , and then took the median of each of those
two probabilities across all taxa as a null expectation for the proportion of taxa that would exhibit
uncertainty response diversity from the spurious accumulation of Type I and Type II errors.
Sensitivity analyses
Minimum number of presence records: We used sensitivity analysis to examine the extent to which
our arbitrary threshold of requiring n ≥ 5 presence records per taxon per survey might influence our
key results. We progressively restricted our analysis to subsets of taxa that had at n ≥ 5, 10, 15, …
100 presence records within at least two separate surveys, and for each cut-off value calculated the
proportion of taxa assigned to each of the four response diversity classes.
Critical p-value for statistical significance: Our categorisation of results into uncertainty response
diversity (Fig. 1C) is dependent on the p-value used to denote statistical significance. We tested the
extent to which our results are sensitive to our choice of p = 0.05 by repeating the analysis for values
of 0.005 ≤ p ≤ 0.1 in increments of 0.005.
Taxonomic bias in the response diversity classes
We used goodness of fit tests to determine whether the distribution of taxa within each of the
four response diversity classes were random samples of the seven taxonomic groups. We used the
proportion of taxa within the full dataset belonging to each of the seven groups as an expected
distribution, and assessed the probability that the observed distribution deviated from this. We then
used bootstrapping to determine which taxonomic groups were under- or over-represented in each
of the four classes. For each class, we took 1000 random draws of the taxa exhibiting that class
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(sampling with replacement), from which we quantified an expected null distribution of the number
of taxa belonging to each of the seven taxonomic groups (Fig. S2). Groups where the observed
number of taxa fell below the 2.5 % or above the 97.5 % quantiles of the null distribution were
considered to be significantly under- or over-represented respectively.
Meaningful versus spurious intra-population response diversity
We conducted a secondary analysis to classify intra-population response diversity into meaningful or
spurious causes. Meaningful response diversity can only be definitively demonstrated when
response patterns vary among two surveys that share an exact sampling design and survey method,
in which case the variation in response pattern must arise due to unaccounted variation in the
environment. To identify cases of confirmed, meaningful response diversity for individual taxa, we
first identified all pairs of surveys that contained data on that particular taxon. For each survey pair,
we extracted the subset of sampling locations that were present in both surveys and repeated the
fitting of binomial GLMs and the categorisation of GLM results into the four response diversity
classes. Survey pairs that exhibited one or more of statistical classes B-D (Fig. 1) were confirmed as
having meaningful response diversity. This represents a lower bound on the true number of
meaningful response diversity cases, as surveys with different spatial designs could also exhibit
meaningful response diversity but we are unable to test for it. Taxa that were observed to have
response diversity in our main analysis, but for which we could not positively determine the
presence of meaningful response diversity in this secondary analysis, were considered to
demonstrate spurious response diversity.
We examined three potential causes of meaningful response diversity at our study site: a general
year effect; El Niño events that occurred in 2010 and again in 2015/16; and a salvage logging
operation that impacted large areas of the landscape over the years 2012-2015. The first of these
was tested by quantifying, for each year, the proportion of pairwise comparisons involving that year
that exhibited response diversity (Fig. S3A). We quantified this proportion for each of the years for
which we had survey data (2010 – 2020 inclusive), and tested for an effect of the number of years
separating the two surveys on the whether they had invariant or different response patterns. This
was tested using using a binomial GLMM, including taxon identity as a random effect. To test the
remaining two hypotheses, we divided pairwise survey comparisons into three groups for each of
the two events: (1) outside: when each of the individual surveys occurred outside of an El Niño or
logging event respectively; (2) straddling: when one of the surveys occurred within an El Niño or
logging event and the other occurred outside of such events; and (3) within: when both surveys
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occurred within an El Niño or logging event. For each group, we quantified the proportion of
pairwise comparisons that exhibited invariant responses (Fig S3B,C). If either of these events were a
strong driver of meaningful response diversity, we would expect to see a higher proportion of taxa
exhibiting response diversity in survey pairs either straddling or embedded within these disturbance
events.
To explore the potential drivers of spurious response diversity, we used generalised linear mixed
effects models to examine the effect that researcher-controlled decisions about study design exert
on response diversity. We categorised all pairwise combinations of taxon-specific comparisons as
exhibiting response diversity or not, and used that as a response variable with taxon identity as a
random variable. As fixed effect predictor variables we included the minimum number of
occurrences per survey pair (log10-transformed), the minimum number of sample sites per survey
pair (log10-transformed), the taxonomic resolution at which that taxa had been identified (species,
genus, family or ordinal level), and whether the pair of surveys used the same or different sampling
methods. We used minimums rather than the mean or maximum because analyses based on low
sample size are more prone to generating spurious and imprecise results, and therefore a pair of
studies where one or both have a particularly low sample size are more likely to return inconsistent
results. We fitted all variables in a single model and used backwards stepwise model selection using
log-likelihood ratio tests to identify significant variables. We calculated the marginal and conditional
coefficient of determination (pseudo-r2) for the fixed and random effects respectively using the
method outlined by Nakagawa and Schielzeth (48).
Single-survey data in ecology
We reviewed all papers published in Ecology, the flagship journal of the Ecological Society of
America, in 2021, recording the number of years of empirical data presented in each publication.
Out of a total of 263 papers, 246 presented empirical data, of which 100 (41 %) presented data
collected from a single-year survey only. A further nine studies (4 %) presented data from multiple
datasets, of which one or more was a single-year survey.
Estimating the probability of detecting a true response
To estimate the probability that we would correctly detect the correct sign of a taxon’s response to
the forest degradation gradient, we re-fitted all models using Bayesian hierarchical models in
rstanarm (34, 35), fitting each taxon with hierarchically-drawn slopes of biomass and intercepts. We
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22
used package defaults for model sampling (1000 warm-up iterations and 1000 subsequent sampling
iterations across each of 4 chains) with default priors apart from where specified below, and found
no evidence of model mis-specification or a lack of convergence (i.e., all values <1.1 and no
divergent transitions in the sampling steps). Specifically, our model was defined as:
(1)
(2)
(3)
(4)
where is the presence/absence (1/0) of a taxon in a given survey () at a given site (), and is
logit-transformed and is proportional to the predicted probability of presence of a species in a given
survey (i.e., is a standard Binomial Generalised Linear Model term). itself is then defined by ,
the overall mean across all years, , the contrasts (difference) in the intercept for each survey year,
and , each survey year’s estimated biomass removal effect (itself multiplied by , the biomass
removal at a given site). The terms and are themselves hierarchically drawn from distributions
centred at 0 and and with standard deviations and , respectively. The Bayesian hierarchical
formulation of our approach is central to our method since, for each taxon, it allows us to estimate
variation in responses to biomass removal across surveys and also to directly parameterise the
distribution of estimated responses in equation 3. From this, we can directly estimate the probability
of observing a consistent response across years.
To ensure that our prior specifications were not biasing model results, we repeated our model fits
across two extreme prior definitions: ‘correlated’ and ‘variable’ priors. These were fitted across 4
chains, each with 3000 warm-up iterations and 1000 sampling iterations (more samples were
needed due to the priors being extreme and therefore slowing model convergence). ‘Correlated’
priors specified regularisation parameters () of 0.5 for the hierarchical terms, biasing the model
such that slopes and intercepts should be more consistent across surveys. ‘Variable’ priors set = 5,
such that survey slopes and intercepts were more independent. These two specifications form
extremes of a continuum. Our model results were qualitatively identical to those reported in the
main text (with set to 1, the default), suggesting that our conclusions are robust to model
specification and fitting method.
SI Appendix
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23
Sensitivity analyses
Minimum number of presence records: Our results are largely robust to the choice of cut-off value of
requiring n ≥ 5 presence records per taxon per survey (Fig. S1A), and our specific cut-off value of n ≥
5 resulted in the highest proportion of taxa lacking response diversity (having fully invariant
responses). This ensures our choice of n ≥ 5 ensures we present results that emphasise the most
conservative estimate of response diversity.
Critical p-value for statistical significance: Using values of p that were lower than 0.05 resulted in a
slight reduction in the proportion of taxa demonstrating uncertainty response diversity and a
corresponding increase in the proportion of taxa lacking response diversity (Fig. S1B). This effect was
most apparent at highly conservative estimates of statistical significance (p ≤ 0.02), above which the
choice of p exerted little influence on our qualitative conclusions.
Figure S1. Sensitivity of results to variation in (A) the minimum number of occurrence records
required for a taxon to be analysed; and (B) the critical p-value used to denote statistical significance.
Values represent the proportion of taxa categorised into each of the four classes of response diversity
(Fig. 1).
Taxonomic bias in the response diversity classes
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Figure S2. Expected and observed number of taxa within seven broad taxonomic groups exhibiting
the four classes of response diversity. Distributions represent, for each taxonomic group and class, a
null expectation of the number of taxa that will exhibit that particular class. Points represent the
observed number of taxa displaying each class. Significance denotes whether the point sits outside
the 95 % quantile of the null distribution.
Spurious response diversity
Spurious response diversity is likely driven by methodological differences among surveys of the same
taxon in the same landscape, although it’s not clear from our data exactly what researchers should
do to eliminate this. The probability that a taxon-specific, pairwise comparison of responses were
invariant increased as taxonomic resolution increased ( = 24.1, df = 3, p < 0.001), and there was a
paradoxical effect where taxa that were more common were less likely to demonstrate invariant
responses ( = 15.2, df = 1, p < 0.001). This likely occurs because rare taxa were less likely to have a
significant response to forest degradation (beta regression; z = -17.1, p < 0.001), and we only found
fully invariant responses within generalist taxa that were not impacted by the degradation gradient
(e.g. Fig. 1A). Increasing our threshold of n ≥ 5 for inclusion in the analysis further reduced the
proportion of taxa with invariant responses (Fig. S1), demonstrating that excluding uncommon
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25
species from the analysis would generate even more extreme estimates of response diversity. The
probability of responses varying was also increased when pairs of surveys used different sampling
methods ( = 5.25, df = 1, p = 0.022), but was not impacted by the minimum sample size of a pair of
surveys ( = 1.67, df = 1, p = 0.20). Together, these fixed effects explained just 7 % of the variation
in probability of obtaining two invariant surveys, meaning spurious response diversity was not
strongly driven by variation in general study design parameters that researchers can exert direct
control over. By contrast, the random effect of taxon identity explained 66 % of the variation,
indicating the cause of spurious response diversity is almost species-specific. These results indicate
that the spurious response diversity we have detected is not obviously generated by high-level
design features of studies, and instead appears to be caused by the fine details of individual studies.
Figure S3. Bootstrapped estimates of the proportion of taxa with invariant response patterns with
respect to (A) year of survey, (B) El Niño events and (C) logging events. Thick line represents the
median, boxes the 1st and 3rd quantiles, and whiskers the range. In panels (B) and (C), data were
categorised into pairwise comparisons of taxon responses to forest degradation in which both
surveys were conducted in years during which the event occurred (within), when one survey occurred
during an event and the other occurred outside of the event (straddling), and when both surveys
occurred outside of the event (outside).
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environmental gradient. https://zenodo.org/record/3994260. (Zenodo, 2020).
66. J. Hardwick et al., The effects of habitat modification on the distribution and feeding ecology
of Orthoptera 2015. https://zenodo.org/record/4275386. (Zenodo, 2020).
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insectivorous bats of Sabah. https://zenodo.org/record/3247465. (Zenodo, 2019).
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95. C. Wilkinson et al., All Fish catch data at the SAFE project 2011-2017.
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Table S1. List of data sources compiled for analysis. For each data source, we present: the surname
of the first author and a data citation; a weblink to a data publication or, if that is unavailable, then a
weblink to a published paper presenting the data; the broad taxonomic grouping(s) that were the
focus of the study; the number of taxa that were shared with other surveys; the number of sampling
methods used; the number of sampling periods; and the final number of surveys we extracted from
that data source.
First author
Link
Taxon
type(s)
No.
taxa
No.
sampling
methods
No.
sample
periods
No.
surveys
Bernard (49)
https://zenodo.org/record/3908128
mammal
21
1
1
1
Bishop (50)
https://zenodo.org/record/1198839
invertebrate
22
1
1
1
Both (51)
https://zenodo.org/record/3247631
plant
133
1
1
1
Brant (52)
https://zenodo.org/record/1198846
invertebrate
21
2
2
3
Carpenter (53)
https://zenodo.org/record/5562260
invertebrate
77
6
1
6
Chapman (54)
https://zenodo.org/record/2579792
mammal
12
1
1
1
Deere (55)
https://zenodo.org/record/4010757
mammal
28
1
3
3
Döbert (56)
https://zenodo.org/record/2536270
plant
150
1
1
1
Drinkwater (57)
https://zenodo.org/record/3476542
invertebrate
2
1
2
2
Ewers (58)
https://zenodo.org/record/3975973
invertebrate
13
1
1
1
Faruk (59)
https://zenodo.org/record/1303010
amphibian
2
1
1
1
Fayle (60)
https://zenodo.org/record/3876227
invertebrate
22
1
1
1
Fraser (61)
https://zenodo.org/record/3973551
amphibian
16
1
1
1
Fraser (62)
https://zenodo.org/record/3981222
bird
76
1
1
1
Gray (63)
https://zenodo.org/record/1198302
invertebrate
46
1
1
1
Gray (64)
https://zenodo.org/record/3475406
invertebrate
4
1
1
1
Gregory (65)
https://zenodo.org/record/3994260
invertebrate
1
1
1
1
preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
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32
Hardwick (66)
https://zenodo.org/record/4275386
invertebrate
1
1
1
1
Hemprich-Bennett
(67)
https://zenodo.org/record/3247465
mammal
35
1
3
3
Heon (39)
https://doi.org/10.1890/15-1363
mammal;
bird
60
1
6
6
Heon (68)
https://zenodo.org/record/3955050
mammal;
bird
33
1
7
7
Heon (69)
https://zenodo.org/record/1304117
mammal
8
1
1
1
Jebrail (70)
https://zenodo.org/record/3475408
invertebrate
5
1
1
1
Kendall (71)
https://zenodo.org/record/1237736
invertebrate
1
1
1
1
Konopik (72)
https://zenodo.org/record/1995439
amphibian
25
1
3
3
Lane Shaw (73)
https://zenodo.org/record/1237732
invertebrate
19
1
1
1
Luke (74)
https://zenodo.org/record/5710509
invertebrate
7
1
1
2
Luke (75)
https://zenodo.org/record/1198833
invertebrate
11
2
1
2
Mackintosh (76)
https://zenodo.org/record/4630980
invertebrate
13
1
1
1
Mitchell (40)
https://doi.org/10.1016/j.ecolind.202
0.106717
bird
132
1
5
5
Mullin (77)
https://zenodo.org/record/3971012
mammal
7
1
2
3
Noble (78)
https://zenodo.org/record/3485086
amphibian
8
1
1
1
Pianzin (79)
https://zenodo.org/record/3897377
mammal
1
1
1
1
Pillay (80)
https://zenodo.org/record/3366104
bird
6
1
2
2
Qie (81)
https://zenodo.org/record/3901735
mammal;
bird;
invertebrate
47
1
1
1
Qie (82)
https://zenodo.org/record/1400564
plant
275
1
3
3
Seaman (83)
https://zenodo.org/record/5109892
mammal
1
1
1
1
preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
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Sethi (84)
https://zenodo.org/record/3997172
bird;
amphibian
227
1
2
3
Shapiro (85)
https://zenodo.org/record/1237720
invertebrate
2
1
1
1
Sharp (86)
https://zenodo.org/record/1323504
invertebrate
288
1
3
13
Slade (87-89)
https://zenodo.org/record/3247492
https://zenodo.org/record/3247494
https://zenodo.org/record/3832076
invertebrate
71
1
3
3
Slade (90, 91)
https://zenodo.org/record/3906118
https://zenodo.org/record/3906441
invertebrate
64
1
2
2
Turner (92)
https://zenodo.org/record/5729342
plant
93
1
1
1
Twining (93)
https://zenodo.org/record/1237731
mammal
2
1
1
1
Vollans (94)
https://zenodo.org/record/3929764
invertebrate
1
1
1
1
Wilkinson (95)
https://zenodo.org/record/4072959
fish
29
3
5
10
Williamson (96)
https://zenodo.org/record/1487595
invertebrate
15
1
1
1
preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
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