PNAS Biotime SI PURE.Pdf (1.998Mb)

Supporting Information

A. Site information SI Table 1: Grid references of sites SI Table 2: Sampling dates SI Figure 1: Photographs of sites

B. Detailed sampling methodology

C. Assemblage and environmental data SI Table 3: Fish assemblage SI Table 4: Benthic Invertebrate assemblage SI Table 5: Diatom assemblage SI Figure 2: Species accumulation curves for each site and assemblage SI Figure 3: Rank abundance plots for the three assemblages SI Table 6: Environmental variables by site

D. Detailed statistical methodology SI Table 7: a and b diversity metrics used in the study SI Figure 4: Illustration of cyclic shift randomization SI Figure 5: a diversity trend for each assemblage and site SI Figure 6: b diversity trend for each assemblage and site

E. Detectability and repeatability SI Table 8: Repeatability tests

F. Supplementary Results SI Figure 7: Disturbance v. biodiversity change. SI Table 9: Randomization tests: Z scores and quantiles SI Figure 8: Randomization tests: Fish species plots SI Figure 9: Randomization tests: Fish families plots SI Figure 10: Randomization tests: Benthic invertebrates plots SI Figure 11: Randomization tests: Diatom plots SI Figure 12: Biotic homogenization

References

A. Site information

Sites (Table S1) in Trinidad’s Northern Range (see Figure 3, main text) were sampled four times per year (twice in the wet season and twice in the dry season – Table S2) for five years (2010-2015). On each sampling occasion the diversity of stream fish, benthic invertebrates and diatoms was quantified. The stream fish assemblage included only finfish (vertebrates). Sampling methodology was consistent throughout. Eight of our sites were exposed to overt human pressure as a result of regular recreational use (e.g. 1). These ‘disturbed’ (d) sites were matched with ‘undisturbed’ (u) sites in the same river system. Four sites were resampled in 2011 (see E below).

SI Table 1 with grid references of sites

Site Latitude Longitude Acono d (AD) 10.712317 -61.399383 Acono u (AU) 10.713650 -61.398183 Caura d (CD) 10.689533 -61.355167 Caura u (CU) 10.702750 -61.367850 Lopinot d (LD) 10.689050 -61.319583 Lopinot u (LU) 10.705167 -61.319683 Lower Aripo d (LAD) 10.650300 -61.220683 Lower Aripo u (LAU) 10.656333 -61.222733 Maracas d (MD) 10.722150 -61.419950 Maracas u (MU) 10.717983 -61.419083 Quare d (QD) 10.665650 -61.193583 Quare u (QU) 10.673933 -61.196867 Turure d (TD) 10.656900 -61.168017 Turure u (TU) 10.680233 -61.167333 Upper Aripo d (UAD) 10.681517 -61.230450 Upper Aripo u (UAU) 10.685800 -61.232500

SI Table 2 - Sampling dates

This table gives the year and month (values in cells) of each sample session at each site (site abbreviations as in SI Table 1). Shaded cells represent wet seasons, unshaded cells dry seasons. year session AD AU CD CU LD LU LAD LAU MD MU QD QU TD TU UAD UAU 2010 omitted from 11 11 12 12 11 11 11 11 12 12 12 12 12 12 11 11 analysis 2011 1 1 1 1 1 1 1 1 1 2 2 2 2 1 1 1 1 2011 2 5 5 5 5 5 5 5 5 6 6 6 6 5 5 5 5 2011 3 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 2011 4 11 11 11 11 11 11 10 10 11 11 10 10 11 11 11 11 2012 5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2012 6 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 2012 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 2012 8 10 10 10 10 11 11 10 10 10 10 11 11 11 11 10 10 2013 9 2 2 1 1 2 2 1 1 1 1 1 1 1 1 1 1 2013 10 4 4 4 4 5 5 5 5 5 5 4 4 5 5 5 5 2013 11 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 2013 12 10 10 10 10 10 10 11 11 11 11 10 10 11 11 11 11 2014 13 1 1 1 1 2 2 1 1 1 1 2 2 1 1 1 1 2014 14 5 5 4 4 4 5 4 4 4 4 4 4 4 4 4 4 2014 15 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 2014 16 10 10 10 10 11 11 12 12 11 11 11 11 12 12 11 11 2015 17 2 2 1 1 2 2 1 1 2 2 2 2 2 2 1 1 2015 18 5 5 4 4 5 5 4 4 5 5 5 5 5 5 5 5 2015 19 8 8 8 8 7 7 7 7 8 8 7 7 7 7 7 7

SI Figure 1

Photographs of survey sites

Disturbed Undisturbed

Acono

Caura

Lopinot

Lower Aripo

Maracas

Quare

Turure

Upper Aripo

B. Detailed sampling methodology

Assemblages The same 50m stretches of stream were revisited each session. Fish, invertebrates and diatoms were sampled during each visit. Fish were exhaustively sampled (2, 3) using (first) a two-person seine net (mesh 6.4mm) and (second) by electrofishing. A dip net was used to catch any remaining fish. All stream fish were identified to species and counted. Captured fish were then returned unharmed to the river at the end of the session. Any macroinvertebrates (e.g. crayfish) sampled were also recorded but have not been analysed here. Next, a surber sampler (2) was deployed to assess benthic invertebrates. Five samples, dispersed along the stream section, were taken on gravel substratum. These samples were preserved in alcohol and returned to the laboratory where all taxa were identified to family (the best taxonomic resolution achievable based on local expertise at the University of the West Indies, Trinidad & Tobago). All individuals sampled were recorded. Data from the invertebrate samples at a given site and session were combined for analysis. Finally, in the case of diatoms three rocks of ~20cm diameter were collected from a depth of around 15 cm at intervals along the stream section, following the recommendations in (4). On return to the laboratory each sample was processed using the protocols in (4, 5). No taxonomic key is available for Trinidadian diatoms; specimens were therefore identified to morphospecies using a photographic catalogue compiled by AED (6). Diatom samples underwent a known dilution after which individuals were counted along 5 haphazard transects on a 1 ml Sedgewick-Rafter counting cell using an Olympus IX51 inverted light microscope (x60 N.A. 0.70 lens). Data from the diatom samples at a given site and session were combined for analysis. In all cases sampling methods and effort were constant throughout.

Environmental Data The following data were collected during each session at each site:

i. Water parameters -1 -1 Conductivity (µS.cm ), O2 (mg.l ), pH and temperature (˚C) were measured using a field meter. Turbidity was recorded using a scale of 1-5 (1: Completely clear, substratum clearly visible even in deeper sections; 2: Clear in shallow parts, but slight turbidity evident in deeper areas (i.e. hard to see the substratum); 3: Some turbidity evident even in shallower areas.; 4: Considerably turbid in both shallow and deep areas - water brown in colour; 5: Extremely turbid throughout, substratum completely obscured.)

ii. Recreational impact Recreational impact was assessed by counting the number of pieces of garbage in or alongside the sample site.

iii. River features Three transects (5m, 25m and 45m from the upstream margin of the site) were identified. Flow (m.s-1) was measured 9 times (3 times near the centre of each transect) and averaged. Transect width was measured, average depth calculated and these data used to estimate water volume. The canopy cover above each transect was estimated using a concave spherical densiometer. These data were used to construct an integrated canopy measure.

iv. Substratum The percentage cover of different substratum categories: silt, sand, fine gravel, coarse gravel, cobble, small boulders, large boulders and bedrock was recorded at each transect, and averaged. The percentage of leaf litter was also recorded. 6

C. Assemblage Data 1

SI Table 3: Fish assemblage

PHYLUM FAMILY SPECIES AUTHORITY COMMON NAME Chordata Callichthyidae Corydoras aeneus Gill 1858 Bronze cory/pui pui Chordata Callichthyidae Hoplosternum littorale (Hancock, 1828) Cascadura Chordata Charachidae Astyanax bimaculatus (Linnaeus, 1758) Two-spot sardine Chordata Charachidae Corynopoma riisei Gill 1858 Sword-tail sardine Chordata Charachidae Hemibrycon taeniurus (Gill, 1858) Mountain stream sardine Chordata Charachidae Hemigrammus (Gill 1858) Featherfin tetra unilineatus Chordata Charachidae Odontostilbe pulchra (Gill 1858) Chordata Charachidae Roeboides dientonito (Schultz 1944) Humpback sardine Chordata Cichlidae Andinoacara pulcher (Gill, 1858) Coscorob/blue acara (previously Aequidens pulcher) Chordata Cichlidae Cichlasoma taenia (Bennett, 1831) Brown coscorob Chordata Cichlidae Crenicichla frenata Gill, 1858 Pike cichlid (previously C. alta) Chordata Cichlidae Oreochromis (Peters, 1852) Tilapia mossambicus Chordata Curimatidae Steindachnerina (Gill, 1858) Stout sardine argentea Chordata Erythrinidae Hoplias malabaricus (Bloch 1794) Guabine/wolf fish Chordata Gobidae Awaous banana (Valenciennes 1837) Sandfish Chordata Gymnotidae Gymnotus carapo Linneaus 1758 Cutlass/knife fish Chordata Heptapteridae Rhamdia quelen (Quoy & Gaimard 1824) River catfish/cascalaw Chordata Loricariidae Ancistrus maracasae Fowler 1946 Jumbie teta Chordata Loricariidae Hypostomus robini Valenciennes 1840 Teta/armoured catfish Chordata Muglilidae Agonostomus (Bancroft, 1834) Mountain mullet monticola Chordata Poecilidae Micropoecilia picta (Regan, 1913) Swamp guppy (previously P. picta) Chordata Poecilidae Poecilia reticulata Peters, 1859 Guppy/millionsfish/ drainfish Chordata Poecilidae Poecilia sphenops Valenciennes, 1846 Molly Chordata Rivulidae Anablepsoides hartii (Boulenger, 1890) Rivulus/jumping guabine (previously Rivulus hartii) Chordata Synbranchidae Synbranchus Bloch 1795 Zangee marmoratus

SI Table 4: Benthic Invertebrate Assemblage

1 Data are available at http://dx.doi.org/10.17630/ede726cd-3ab0-41a7-a6ef-5063732af297

PHYLUM CLASS ORDER FAMILY AUTHORITY COMMON NAME Arthropoda Insecta Coleoptera Psephenidae Lacordaire, 1854 Water pennies Arthropoda Insecta Coleoptera Elmidae Curtis, 1830 Riffle beetles Arthropoda Insecta Ephemeroptera Tricorythidae Mayfly larvae Arthropoda Insecta Ephemeroptera Baetidae Mayfly larvae Arthropoda Insecta Ephemeroptera Leptophlebidae Mayfly larvae Arthropoda Insecta Ephemeroptera Euthyplocidae Mayfly larvae Arthropoda Insecta Plecoptera Perlidae Stonefly larvae Arthropoda Insecta Odonata Libellulidae Rambur, 1842 Dragonfly larvae Arthropoda Insecta Odonata Gomphidae Dragonfly larvae Arthropoda Insecta Odonata Aeshnidae Rambur, 1842 Dragonfly larvae Arthropoda Insecta Odonata Coenagrionidae Damselfly larvae Arthropoda Insecta Odonata Calopterygidae Damselfly larvae Arthropoda Insecta Trichoptera Hydropsychidae Curtis, 1835 Net-spinning caddisfly larvae Arthropoda Insecta Trichoptera Helicopsychidae Ulmer, 1912 Snail-case caddisfly larvae Arthropoda Insecta Trichoptera Hydroptilidae Stephens, 1836 Purse-case caddisfly larvae Arthropoda Insecta Trichoptera Glossosomatidae Wallengren, 1891 Saddle-case caddisfly larvae Arthropoda Insecta Trichoptera Philopotamidae Stephens, 1829 Finger-net caddisfly larvae Arthropoda Insecta Trichoptera Lepto/Odontoceri Leach in Long-horned caddisfly larvae dae Brewster, 1815 Arthropoda Insecta Trichoptera Calamoceratidae Caddisfly larvae Arthropoda Insecta Diptera Simuliidae Newman, 1834 Black fly larvae Arthropoda Insecta Diptera Chironomidae Non-biting midges Arthropoda Insecta Diptera Tipulidae Latreille, 1802 Crane fly larvae Arthropoda Insecta Lepidoptera Unknown Aquatic moth larvae Arthropoda Insecta Hemiptera Veliidae Amyot & Serville, Water striders 1843 Arthropoda Insecta Hemiptera Gerridae Leach, 1815 Water striders Arthropoda Decapoda Brachyura Pseudothelph- Pretzmann, 1971 Manicou crab usidae Arthropoda Arachn- Trombidiformes Hydracarinidiae Water mites ida Platyhelminthes Turbellar- Ehrenberg, 1831 Flat worms ia Oligochaeta Segmented worms

SI Table 5: Diatom Assemblage - see (6).

Note Invasive taxa Oreochromis mossambicus is an introduced species and Poecilia sphenops (also known as Poecilia boesemani) is also believed to have been introduced (7). To the best of our knowledge all other taxa recorded in the study are native.

SI Figure 2: Species accumulation curves for each site and assemblage, compiled using the specaccum function in vegan(8) (random option, with 100 draws). Site abbreviations as in SI Table 1 above.

SPECIES ACCUMULATION CURVES Fish spp. Fish families

AD AU CD CU AD AU CD CU 20 20 random random random random random random random random 10 10

LD LU LAD LAU LD LU LAD LAU

Sites Sites Sites Sites Sites Sites Sites Sites 20 20 random random random random random random random random 10 10

MD MU QD QU MD MU QD QU

Sites Sites Sites Sites Sites Sites Sites Sites 20 20 FISH (spp.) species accumulation FISH (spp.) FISH (families) species accumulation FISH (families) random random random random random random random random 10 10

TD TU UAD UAU TD TU UAD UAU

Sites Sites Sites Sites Sites Sites Sites Sites 20 20 random random random random random random random random 10 10

5 10 15 5 10 15 5 10 15 5 10 15 5 10 15 5 10 15 5 10 15 5 10 15 Sites Sites time Sites Sites Sites Sites time Sites Sites Benthic Invertebrates Diatoms AD AU CD CU AD AU CD CU 30 30 20 20 random random random random random random random random 10 10

LD LU LAD LAU LD LU LAD LAU

Sites Sites Sites Sites Sites Sites Sites Sites 30 30 20 20 random random random random random random random random 10 10

MD MU QD QU MD MU QD QU

Sites Sites Sites Sites Sites Sites Sites Sites 30 30 DIATOMS species accumulation DIATOMS 20 20 random random random random random random random random INVERTEBRATES species accumulation INVERTEBRATES 10 10

TD TU UAD UAU TD TU UAD UAU

Sites Sites Sites Sites Sites Sites Sites Sites 30 30 20 20 random random random random random random random random 10 10

5 10 15 5 10 15 5 10 15 5 10 15 5 10 15 5 10 15 5 10 15 5 10 15 Sites Sites time Sites Sites Sites Sites time Sites Sites

SI Figure 3: Rank abundance plots for the three assemblages. These rank abundance plots, based on overall numerical abundance, show that none of the assemblages display an excess of singletons, and thus provides evidence of good sample coverage in all cases.

SI Table 6: Environmental Variables by site. Median (Med) value (and upper (Q3) and lower (Q1) quartiles) shown for each variable. Site abbreviations as in SI Table 1 above.

D. Detailed statistical methodology

Choice of metric Assessments of the variety and abundance of species (sometimes called species diversity in the older literature) fall into two broad categories: measurement of the amount of diversity (species richness, or species relative abundance) in the assemblage of interest, and evaluation of compositional differences (or turnover) between two or more assemblages. Whittaker (9, 10) dubbed these two components of diversity a diversity and b diversity respectively. Together they determine the overall (g) diversity of the region as a whole. Commentators have repeatedly commented on many different diversity metrics available to investigators (2, 11-18). It is, however, becoming clear that meaningful assessments need to consider both a and b components, particularly when the goal is to assess biodiversity change (17, 19-21). Many of the metrics initially devised for spatial comparisons can be used to evaluate temporal shifts in diversity, providing that the characteristics of assemblage time-series (such as within-species temporal autocorrelation in population size) are taken into consideration (22, 23). It follows from the above that to meet our goal of understanding biodiversity change across assemblages within this tropical ecosystem we need to evaluate both temporal a and temporal b diversity. Given the ongoing debate about the relative merits of the different metrics (e.g. 17, 18, 21) we began by focusing on a set of measures that are widely-used and understood, and/or considered informative (see SI Table 7). We computed temporal trends in diversity (see below) for each metric and asked if these trends are correlated, within and across assemblages. This analysis informed the choice of a and b diversity metrics used in the rest of the study.

SI Table 7: a and b diversity metrics used in the study metric comments reference a diversity Species Number of observed species. A widely-used metric but heavily influenced (12, 24, 25) richness (S) by sampling effort. Chao1 Estimate of total number of species. Bias corrected form used (equation 2 (26) in http://viceroy.eeb.uconn.edu/estimates/EstimateSPages/EstSUsersGuide /EstimateSUsersGuide.htm). Computed as in (20). PIE Probability of interspecific encounter. Considered an informative measure (27, 28) of evenness and close (in terms of conceptual underpinning and results) to reciprocal form of Simpson’s index. Computed as in (20). McNaughton A dominance measure based on the proportional abundance of the two (15) Dominance most dominant species. Computed as in (20). (Dom) Shannon A popular index that combines elements of species richness and (15, 30) index, evenness. Here used in its exponential form (this gives the number of exponential species that would be present given perfect evenness in the observed form (ExpH) assemblage). ExpH’ is equivalent to one of the Hill numbers (Hill1 or q=1(29)). Computed as in (20). b diversity

Morisita-Horn An abundance based (dis)similarity measure that gives weight to the most (31, 32) (MH) abundant species. Here expressed as dissimilarity. Computed using the vegdist function in vegan (8). Bray-Curtis A widely-used abundance based (dis)similarity measure. Computed using (33) but see (BC) the vegdist function in vegan (8). also (31, 34) Chao Jaccard A qualitative (presence/absence) (dis)similarity measure that takes (35) (ChaoJ) account of unseen species. Computed using the vegdist function in vegan (8). 12

Jaccard The component of Jaccard (presence/absence) dissimilarity attributed to (37) turnover turnover. Computed using the beta.pair function in betapart (36). (Jturn) Jaccard The component of Jaccard (presence/absence) dissimilarity attributed to (37) nestedness richness change (nestedness). Computed using the beta.pair function in (Jnest) betapart (36) Jaccard Total Jaccard (presence/absence) dissimilarity. Sum of Jturn and Jnest. (37) (Jtot) Computed using the beta.pair function in betapart (36)

Quantifying trends We removed the first data point from all time series to allow for the fact that it had not been possible to electro fish sites on the initial sampling visit (see (3) for a discussion of sampling methodology). This gave us n=19 samples per site and assemblage. We then computed a diversity (metrics as above) at each time point for each site and assemblage. b diversity (metrics as above) was calculated as pairwise dissimilarity between the first data point in a time series, and each successive one (21). We fitted a linear model to each time series allowing a different slope and an intercept, by using ordinary least squares (OLS). In the case of b diversity metrics we adopted current accepted practice in the field in setting the initial dissimilarity at time 1 to zero, and plotted shifts in the composition of the assemblage at each successive time point relative to composition at time 1. The slope of this model was our measure of biodiversity change for a given metric, site and assemblage. In this we followed (20). SI Figures 5 and 6 illustrate the trend lines for the a and b metrics (PIE and Jaccard (total) respectively) that were the focus of the remainder of the analysis. The slopes produced by null model were generated in exactly the same way. As a check on the impact of our decision to constrain initial dissimilarity we reran the analyses using the slopes produced when values at time 1 were not constrained to zero. The results produced by the two methods of handling dissimilarity trends were almost identical with baseline changes in dissimilarity (as measured using the criterion in Figure 2 i.e. Z>2) as follows: fish species – 2 sites in each case; invertebrates 2 sites in each case; diatoms 4 sites (constrained) v. 5 sites (not constrained). For diatoms there were also a few instances where dissimilarity was reduced relative to expectation (i.e Z<-2): 1 site (constrained) v. 2 sites (not constrained). We also checked for systematic differences in the trends in biodiversity change between paired undisturbed and disturbed sites within a river using a Wilcoxon matched-pairs test (see SI Figure 7).

Correlation analysis We computed the pairwise correlation of slopes (Spearman index, n=16 sites) for each assemblage, to examine the relationship between the metrics, and visualized the results using the corrplot R package (38). On the basis of this analysis we concluded that PIE and Jaccard provided informative assessments of biodiversity change for a and b diversity respectively.

Null model We used a cyclic shift permutation to assess whether the temporal trends in a and b diversity exceeded the levels expected by chance. A cyclic shift permutation retains within-species temporal autocorrelation but breaks species cross correlations. It does this by randomising the start time for each species. The advantage of a cyclic shift permutation is that it preserves a key aspect of population ecology (population size at time t is not independent of population size at time t-1, due to ecological processes including density dependence (39)). These features would be lost in a free permutation. In essence, then, we are using the null model to ask whether, given ecologically realistic population dynamics, the pattern of change in the diversity of the assemblage as a whole exceeds null expectations. By making explicit the features of community structure we wish to preserve, and those we wish to randomise, the cyclic shift permutation, as implemented here, meets the criterion for a meaningful null model (40, 41). We are unaware, however, of any formal evaluation of the cyclic shift permutation in community ecology, relative to alternative models, as undertaken by (42) in the context of spatial synchrony. We ran the cyclic shift permutation 1000 times for each assemblage and site, using the cyclic_shift function in codyn (23, 43). In each case we calculated assemblage biodiversity change, exactly the same way as above, for both a (PIE) and b (Jaccard) diversity. Jaccard (total) dissimilarity was computed using the beta.pair function in betapart (36). We compared observed biodiversity change (slope of the linear 13 model of metric against time, again, as above) against the expected distribution and noted instances when it fell outside the 95% range (0.025 and 0.975 quantiles). As such our null model is tailored to the assessment of biodiversity change, as implemented here. However, we recognize that multiple testing increases the likelihood of false detection. We therefore show the distributions, and report Z scores and the actual observed quantile, for each site, metric and assemblage, in SI Table 9 and SI Figures 8-11.

SI Figure 4: Illustration of cyclic shift. In this toy example we have two observed assemblages in which 5 species were monitored over 5 time steps – A: where there is no trend in richness (S) and B: where there is a trend in richness (S). The cyclic shift permutation randomly shifts the start time for each species and wraps the time series around as required. We show one such permutation and have used the same one in both examples. In both cases the blue dots in the graph plot the observed richness, while the red dots show the pattern obtained after the randomisation. The blue and red trend lines in A, representing the observed and cyclic-shifted assemblages respectively, are indistinguishable. In contrast, the slopes of the trend lines differ in B, since the observed assemblage exhibits an increase in S, but the randomized assemblage does not.

A observed (no trend) cyclic-shift A. No trend t1 t2 t3 t4 t5 t1 t2 t3 t4 t5 Sp. A 16 19 21 24 20 Sp. A 24 20 16 19 21 6 4 S Sp. B 0 0 1 3 3 Sp. B 0 0 1 3 3 2 Sp. C 7 8 4 0 0 Sp. C 0 7 8 4 0 0 Sp. D 1 0 0 1 1 Sp. D 1 1 1 0 0 0 2 4 6 Sp. E 89 96 101 98 87 Sp. E 96 101 98 87 89 time S 4 3 4 4 4 S 3 4 5 4 3 B observed (trend) cyclic-shift t1 t2 t3 t4 t5 t1 t2 t3 t4 t5 B. With trend Sp. A 16 19 21 24 20 Sp. A 24 20 16 19 21 6 Sp. B 0 0 1 3 3 Sp. B 0 0 1 3 3 4 S Sp. C 0 0 4 7 8 Sp. C 0 7 8 4 0 2 Sp. D 0 0 1 1 1 Sp. D 1 1 1 0 0 0 0 2 4 6 Sp. E 89 96 101 98 87 Sp. E 96 101 98 87 89 time S 2 2 5 5 5 S 3 4 5 4 3

SI Figure 5: a diversity, as measured by PIE, for each assemblage and site (site abbreviations as in SI Table 1) in relation to time with trend line fitted using a linear model. PIE Fish species Fish families AD AU CD CU AD AU CD CU 1.00 1.00 0.75 0.75 0.50 0.50 obsres$PIE obsres$PIE obsres$PIE obsres$PIE obsres$PIE obsres$PIE obsres$PIE obsres$PIE 0.25 0.25 0.00 LD LU LAD LAU 0.00 LD LU LAD LAU

FISH spp. PIE FISH spp. obsres$time obsres$time obsres$time obsres$time obsres$time obsres$time obsres$time obsres$time 1.00 1.00 FISH families PIE FISH families 0.75 0.75 0.50 0.50 obsres$PIE obsres$PIE obsres$PIE obsres$PIE obsres$PIE obsres$PIE obsres$PIE obsres$PIE 0.25 0.25 0.00 TD TU UAD UAU 0.00 TD TU UAD UAU

obsres$time obsres$time obsres$time obsres$time obsres$time obsres$time obsres$time obsres$time 1.00 1.00 0.75 0.75 0.50 0.50 obsres$PIE obsres$PIE obsres$PIE obsres$PIE obsres$PIE obsres$PIE obsres$PIE obsres$PIE 0.25 0.25 0.00 0.00 5 10 15 5 10 15 5 10 15 5 10 15 5 10 15 5 10 15 5 10 15 5 10 15 obsres$time obsres$time time obsres$time obsres$time obsres$time obsres$time time obsres$time obsres$time

Benthic Invertebrates Diatoms

AD AU CD CU AD AU CD CU 1.00 1.00 0.75 0.75 0.50 0.50 obsres$PIE obsres$PIE obsres$PIE obsres$PIE obsres$PIE obsres$PIE obsres$PIE obsres$PIE 0.25 0.25

0.00 LD LU LAD LAU 0.00 LD LU LAD LAU

0.00 MD MU QD QU 0.00 MD MU QD QU

obsres$time obsres$time obsres$time obsres$time PIE DIATOMS obsres$time obsres$time obsres$time obsres$time 1.00 1.00 0.75 0.75 Benthic INVERTEBRATES PIE Benthic INVERTEBRATES 0.50 0.50 obsres$PIE obsres$PIE obsres$PIE obsres$PIE obsres$PIE obsres$PIE obsres$PIE obsres$PIE 0.25 0.25

0.00 TD TU UAD UAU 0.00 TD TU UAD UAU

SI Figure 6: b diversity, as measured by Jaccard dissimilarity, for each assemblage and site (site abbreviations as in SI Table 1) in relation to time with trend line fitted using a linear model.

JACCARD dissimilarity Fish species Fish families AD AU CD CU AD AU CD CU 0.8 0.8 0.6 0.6 0.4 obsres$Jtot obsres$Jtot obsres$Jtot obsres$Jtot 0.4 obsres$Jtot[1:19] obsres$Jtot[1:19] obsres$Jtot[1:19] obsres$Jtot[1:19] 0.2 0.2

LD LU LAD LAU LD LU LAD LAU obsres$time obsres$time obsres$time obsres$time obsres$time[1:19] obsres$time[1:19] obsres$time[1:19] obsres$time[1:19] 0.8 0.8 0.6 0.6 0.4 obsres$Jtot obsres$Jtot obsres$Jtot obsres$Jtot 0.4 obsres$Jtot[1:19] obsres$Jtot[1:19] obsres$Jtot[1:19] obsres$Jtot[1:19] 0.2 0.2

MD MU QD QU MD MU QD QU obsres$time obsres$time obsres$time obsres$time obsres$time[1:19] obsres$time[1:19] obsres$time[1:19] obsres$time[1:19] 0.8 0.8 0.6 0.6 FISH spp. Jaccard (dissimilarity) Jaccard FISH spp. FISH families Jaccard (dissimilarity) Jaccard FISH families 0.4 obsres$Jtot obsres$Jtot obsres$Jtot obsres$Jtot 0.4 obsres$Jtot[1:19] obsres$Jtot[1:19] obsres$Jtot[1:19] obsres$Jtot[1:19] 0.2 0.2

TD TU UAD UAU TD TU UAD UAU obsres$time obsres$time obsres$time obsres$time obsres$time[1:19] obsres$time[1:19] obsres$time[1:19] obsres$time[1:19] 0.8 0.8 0.6 0.6 0.4 obsres$Jtot obsres$Jtot obsres$Jtot obsres$Jtot 0.4 obsres$Jtot[1:19] obsres$Jtot[1:19] obsres$Jtot[1:19] obsres$Jtot[1:19] 0.2 0.2

5 10 15 5 10 15 5 10 15 5 10 15 5 10 15 5 10 15 5 10 15 5 10 15 obsres$time obsres$time time obsres$time obsres$time obsres$time[1:19] obsres$time[1:19]timeobsres$time[1:19] obsres$time[1:19]

Benthic Invertebrates Diatoms

AD AU CD CU AD AU CD CU 0.8 0.8 0.6 0.6 0.4 0.4 obsres$Jtot obsres$Jtot obsres$Jtot obsres$Jtot obsres$Jtot obsres$Jtot obsres$Jtot obsres$Jtot 0.2 0.2

LD LU LAD LAU LD LU LAD LAU

obsres$time obsres$time obsres$time obsres$time obsres$time obsres$time obsres$time obsres$time 0.8 0.8 0.6 0.6 0.4 0.4 obsres$Jtot obsres$Jtot obsres$Jtot obsres$Jtot obsres$Jtot obsres$Jtot obsres$Jtot obsres$Jtot 0.2 0.2

MD MU QD QU MD MU QD QU

obsres$time obsres$time obsres$time obsres$time obsres$time obsres$time obsres$time obsres$time 0.8 0.8 0.6 0.6 DIATOMS Jaccard (dissimilarity) Jaccard DIATOMS 0.4 0.4 obsres$Jtot obsres$Jtot obsres$Jtot obsres$Jtot obsres$Jtot obsres$Jtot obsres$Jtot obsres$Jtot 0.2 0.2 Benthic INVERTEBRATES Jaccard (dissimilarity) Jaccard Benthic INVERTEBRATES

TD TU UAD UAU TD TU UAD UAU

5 10 15 5 10 15 5 10 15 5 10 15 5 10 15 5 10 15 5 10 15 5 10 15 obsres$time obsres$time time obsres$time obsres$time obsres$time obsres$time time obsres$time obsres$time

E. Detectability and repeatability

Detectability Diversity metrics such as the Shannon index assume that individuals have been sampled at random from the assemblage (16, 44). This is rarely achievable in nature. There are a number of approaches to the problem including modelling occupancy, estimating the probability of detection of species (and/or individuals) through mark-recapture or distance sampling, or making the case that the data obtained by sampling are adequate for the question posed without further correction. Occupancy methods (45) typically draw on the presence/absence of the species of interest and necessitate multiple repeated (at least two, but ideally substantially more) samples at a site (assuming no underlying change in the community between samples). However, as McGill (46) explains, ignoring detection probabilities in assessments of occupancy does not necessarily produce a less meaningful result. In addition, occupancy based methods are not generally suitable when the goal is to evaluate change in metrics involving abundance (but see 47). Moreover, if repeated sampling has an adverse impact on the organisms involved (as may happen, for example, if the same individual fish are repeatedly electrofished over a short time period (48)) it could alter the structure and composition of the assemblage being studied. The new generation of occupancy models (47) may provide solutions to some of these concerns, but one of their main assumptions – that sites are closed to immigration and local extinction over replicated surveys (47) – makes their application problematic in open habitats (such as rivers), and in studies where the quantification of temporal turnover is the objective. Detectability can be estimated using mark-recapture methods and distance sampling (44). Mark- recapture (49, 50) assumes that individuals are identifiable or can be uniquely marked, and is thus not feasible in a cross-community survey such as this. Distance sampling (44, 51, 52) typically involves the investigator noting the distance of each individual from a transect or point. Although distance sampling is an effective method of accounting for detectability in contexts, such as surveys of birds or trees, where the investigator can locate and identify each individual by sight, it is not workable in a river survey such as this one that encompasses multiple taxa, many of which cannot be identified in situ. A further issue worth mentioning is that these methods fit a detection function to each species in the assemblage and use this information in the calculation of diversity statistics. However, detection functions cannot be fitted to rare species, which must either be excluded from the analysis or assumed to have the same detectability (44). As Buckland et al. (44 p34) note, ‘Ignoring detectability might not be a major problem if bias is consistent over time or space’. Our study used the same methodology throughout and investigated river sections with similar dimensions, water depth and substratum. Effort remained constant across all surveys and the same field team was involved for the 5-year duration of the study. The sites were within a restricted geographic area and subject to the same meteorological conditions. As such our investigation minimized detectability concerns linked to site and sampling variables (47). We recognize that while the results we present will be subject to biases associated with the methodologies employed (as is true for most, if not all, sampling techniques (53)) it is unlikely that there will be any systematic variation in this bias over time and space. Indeed, we separately evaluated the fishing methodology from a detectability aspect (3). The species accumulation curves (SI Figure 2) take a similar form across assemblages, and we uncovered no excess of singletons overall in the fish assemblage, or in the invertebrate and diatom assemblages (SI Figure 3). A further point is that although unseen species are a detectability issue (44) we noted a strong correlation (Figure 1, main text (54)) between the trends in community diversity obtained from observed species richness and estimated species richness (Chao 1), and between the classical Jaccard metric and Chao Jaccard (which corrects for unseen species). This provides additional reassurance that detectability issues are not obscuring the patterns we report.

Repeatability To check that our methodology was returning consistent assessments of diversity we repeated sampling at four sites Maracas (MD and MU) on 1/6/11 and 3/6/11 and Acono (AD and AU) on 12/8/11 and 15/8/11. To establish if the shape of the species abundance distribution changed between surveys we ran 17 a non-parametric comparison of RADs (rank abundance distribution), for each site and assemblage, using Kolmogorov–Smirnov tests (12). We next compared the shape of the individual based rarefaction curves using first the H0biog null model of Cayuela et al., (55, 56) and then the H0ecol null model. The H0biog test asks if sites share similar species richness and species abundance distributions, while the H0ecol null model takes composition into account alongside richness and the species abundance distribution. The results Table 8 provide a strong indication that the structure of the assemblage does not differ between resurveys. In most cases the H0ecol null model also provides support for the conclusion that the re-samples were drawn from the same assemblage although a significant result in one case each for fish (site AD), invertebrates (site MD) and diatoms (site AD) points towards compositional change attributable either to sampling effects or genuine turnover. Interestingly, these were also sites that emerged as having strong signals of change over the full survey.

SI Table 8: Repeatability tests. Evaluation of differences in assemblage structure between repeat samples of the three assemblages at four sites. Kolmorogov-Smirnov D, its associated p value, and the p values for the H0biog null model and H0ecol null model are shown. Site abbreviations as in SI Table 1.

Assemblage Site Kolmogorov- (K-S test RAD H0biog null model H0ecol null model Smirnov test (D) comparison) P P P Fish AD 0.300 0.759 0.135 0.005 AU 0.125 1.000 0.705 0.64 MD 0.273 0.808 0.8 0.47 MU 0.250 0.848 0.88 0.61 Invertebrates AD 0.200 0.699 0.63 0.35 AU 0.269 0.303 0.525 0.31 MD 0.200 0.819 0.14 0.005 MU 0.250 0.346 0.52 0.135 Diatoms AD 0.107 0.997 0.05 0.005 AU 0.250 0.346 0.195 0.13 MD 0.176 0.665 0.83 0.76 MU 0.143 0.867 0.545 0.13

F. Supplementary Results

Disturbance SI Figure 7: Disturbance v. biodiversity change. Box plots of biodiversity change (slope of relationship between metric and time, as above), by disturbance category, for a (PIE) and b (Jaccard) diversity and fish (species), invertebrate assemblages. The line plots connect the pairs of sites within a river. Wilcoxon matched-pairs tests (with continuity correction) of undisturbed and disturbed pairs of sites are as follows: Fish (species) PIE, V = 7, p-value = 0.141; Fish (species) Jaccard, V = 27, p-value = 0.25; Invertebrate PIE, V = 10, p-value = 0.31; Invertebrate Jaccard V = 10, p-value = 0.31; Diatom PIE V = 26, p-value = 0.31; Diatom Jaccard V = 15, p-value = 0.74.

Fish PIE Fish JACCARD 0.02 0.02

0.00 0.01 slope slope

−0.02 0.00

−0.04 −0.01 undisturbed disturbed undisturbed disturbed Invertebrate PIE Invertebrate JACCARD

0.012 0.005

0.008 slope slope 0.000 0.004

undisturbed disturbed undisturbed disturbed Diatom PIE Diatom JACCARD 0.02 0.015

0.01 0.010 slope slope 0.005

0.00

0.000

undisturbed disturbed undisturbed disturbed

Randomization tests

SI Table 9. Results of randomization tests in which the observed linear slope of the metric of interest is compared against the expected distribution of slopes generated by a cyclic shift permutation (see above for details). The table lists the Z score and quantile of the observed value for both a and b diversity (PIE and Jaccard dissimilarity respectively) for each assemblage. Plots of these analyses follow. Z scores are illustrated in Figure 3, main text. Site abbreviations as in SI Table 1.

PIE Fish spp Fish fam Inverts Diatoms Z quantile Z quantile Z quantile Z quantile AD -0.647 0.263 -0.665 0.261 -1.032 0.149 0.607 0.691 AU -2.737 0.001 -2.490 0.005 0.127 0.509 0.157 0.568 CD 2.414 0.991 2.195 0.987 -0.683 0.242 -0.531 0.313 CU -0.996 0.175 -0.951 0.191 0.582 0.69 1.239 0.895 LD 0.697 0.733 0.700 0.742 -0.429 0.329 -0.985 0.178 LU 0.868 0.784 0.975 0.826 -1.377 0.089 -0.918 0.185 LAD 0.674 0.76 0.574 0.718 0.661 0.754 1.395 0.912 LAU -0.506 0.318 0.009 0.518 -1.372 0.087 1.561 0.961 MD 1.443 0.926 1.553 0.947 4.088 1 -0.255 0.409 MU -0.249 0.453 -0.086 0.470 1.270 0.896 -1.221 0.129 QD -0.487 0.327 -0.580 0.304 -0.210 0.416 -0.243 0.414 QU 0.061 0.501 0.059 0.508 -0.256 0.432 0.199 0.577 TD -1.071 0.152 0.385 0.650 0.368 0.634 -0.640 0.272 TU -1.967 0.022 -1.806 0.037 -0.922 0.182 -0.073 0.488 UAD 0.607 0.707 0.517 0.671 1.021 0.837 -0.819 0.214 UAU 0.241 0.564 0.157 0.540 0.932 0.804 0.155 0.537 Jaccard Fish spp Fish fam Inverts Diatoms Z quantile Z quantile Z quantile Z quantile AD 0.502 0.686 -0.633 0.261 1.445 0.927 2.028 0.981 AU 2.187 0.984 1.939 0.972 -0.856 0.204 1.662 0.952 CD -1.580 0.06 -0.239 0.4 1.819 0.968 0.693 0.744 CU 0.880 0.822 0.443 0.659 -0.183 0.407 -1.958 0.034 LD 1.317 0.905 1.613 0.957 1.723 0.95 1.281 0.905 LU 0.318 0.612 1.152 0.874 0.753 0.777 3.333 1.000 LAD 0.475 0.686 -0.068 0.466 1.151 0.883 -0.099 0.471 LAU 1.057 0.846 1.892 0.972 -0.053 0.466 -1.120 0.132 MD 0.701 0.766 0.656 0.724 -0.552 0.299 -0.231 0.408 MU -1.304 0.094 -0.557 0.274 -0.689 0.262 0.413 0.652 QD -0.902 0.184 0.384 0.628 2.435 0.992 -0.896 0.187 QU 0.002 0.494 -0.515 0.327 1.087 0.86 1.265 0.893 TD 0.976 0.817 -1.580 0.057 -1.071 0.138 -0.206 0.415 TU 0.961 0.83 1.603 0.96 -0.527 0.304 -2.239 0.010 UAD -2.404 0.012 1.502 0.955 0.141 0.54 2.857 0.998 UAU 0.482 0.651 1.998 0.993 3.670 1 3.172 1.000

SI Figure 8 Fish species. Box plots illustrate the results of randomization tests in which the observed linear slope of the metric of interest is compared with the expected distribution of slopes generated by a cyclic shift permutation (see above for details). Each cell represents a site (abbreviations as in SI Table 1 above) and both a and b diversity (PIE and Jaccard dissimilarity respectively) are shown. The blue points indicate the 95% range of the expected distribution (0.025 and 0.975 quantiles), and the red points indicate the observed values. Z scores and the actual percentile of the observed value are given in the Table above.

AD AU CD CU 0.02 0.00 0.02 −

LD LU LAD LAU 0.02 0.00 0.02 −

MD MU QD QU slope 0.02 0.00 0.02 −

TD TU UAD UAU 0.02 0.00 0.02 −

PIE Jaccard PIE Jaccard PIE Jaccard PIE Jaccard FISH species

SI Figure 9. Fish families. Details as above.

AD AU CD CU 0.02 0.00 0.02 −

LD LU LAD LAU 0.02 0.00 0.02 −

MD MU QD QU slope 0.02 0.00 0.02 −

TD TU UAD UAU 0.02 0.00 0.02 −

PIE Jaccard PIE Jaccard PIE Jaccard PIE Jaccard FISH families

SI Figure 10. Benthic Invertebrates. Details as above.

AD AU CD CU 0.01 0.00 0.01 −

LD LU LAD LAU 0.01 0.00 0.01 −

MD MU QD QU slope 0.01 0.00 0.01 −

TD TU UAD UAU 0.01 0.00 0.01 −

PIE Jaccard PIE Jaccard PIE Jaccard PIE Jaccard Benthic INVERTEBRATES

SI Figure 11. Diatoms. Details as above.

AD AU CD CU 0.01 0.01 −

LD LU LAD LAU 0.01 0.01 −

MD MU QD QU slope 0.01 0.01 −

TD TU UAD UAU 0.01 0.01 −

PIE Jaccard PIE Jaccard PIE Jaccard PIE Jaccard DIATOMS

SI Figure 12: Biotic homogenization and heterogenization. To test for biotic spatial homogenization we computed pairwise Jaccard dissimilarities between sites, for each time step and assemblage, as in (54). The results are shown as box plots. Mean values (dotted line) and trends (dashed line) are also shown

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