THE ROLE OF HABITAT AND DISPERSAL IN SHAPING THE
BIODIVERSITY OF RIVERINE INSECT ASSEMBLAGES
by
Colin Curry
B.Sc., University of Calgacy, 2002
A Dissertation Submitted in Partial Fulfillment of the Requirei:rents for the Degree of
Doctor of Philosophy
in the Graduate Academic Unit of Biology
Supervisor: Donald J. Baird, Ph.D., Environi:rent Canada, Biology R Allen Curry, Ph.D., Canadian Rivers Institute, Biology
Examining Board: Serban Danielescu, Ph.D., Civil Engineering Richard Cuajak, Ph.D., Canadian Rivers Institute, Biology Steven Heard, Ph.D., Biology
External Examiner: Sandra Walde, Ph.D., Biology, Dafuousie University
This dissertation is accepted by the Dean of Graduate Studies
THE UNIVERSITY OF NEW BRUNSWICK
November, 2013
© Colin Curry, 2014 ABSTRACT
Given limited resources, biomonitoring progrrum are touted as a source of biodiversity information for conservation p1anning in riverine ecosyste~. However, the degree to which patterns revealed by biomonitoring are reflected in llllSampled mesohabitats and undersampled taxonomic groups has not been fully addressed. Differences in dispersal capacity among taxonomic groups, in particu1ar, may result in divergent patterns of biodiversity at landscape and regional scales. I sought to address the suitability of biological monitoring data in freshwater biodiversity assessment, and to test the prediction that the degree of spatial structuring in aquatic insect assemb1ages is inversely re1ated to their dispersal capacity. My thesis comprises four articles. 1be first addresses whether macroinvertebrate biodiversity patterns in riffles, the target mesohabitat of
Canada's national aquatic biomonitoring program, are reflective of those in riverine wetlands. The second addresses whether biodiversity in a group of insects that is abundant in biomonitoring samples (Trichoptera) reflects that of an tlllderrepresented group (Odonata). The third tests the above prediction by comparing the degree of spatial structuring in the weakJy dispersing Trichoptera and the stronger dispersing Odonata. The final article investigates regional and national aquatic insect biodiversity patterns utilizing the national biomonitoring dataset, and seeks to evaluate the influence of scale on the observation of spatial structuring aquatic insect assemb1ages. Several key findings emerged from this work: 1) Patterns of invertebrate taxon richness and beta diversity in riftles poorly reflect those in riverine wetlands. 2) Odonata and Trichoptera biodiversity were not always congruent, however, differences in abundance among groups may account for weak corre1ations. 3) Both Odonata and Trichoptera assemb1ages
ll demonstrate relatively weak spatial structuring at a Jandscape (ie. 5th order catclnrent) scale. The weak exp1amtory ability of spatial variables was also apparent at a regional scale, as just one of the Water Survey of Canada sub drainages within the Pacific drainage demonstrated a significant spatial component in aquatic insect assemblage variation These findings suggest caution in the application ofbiomonitoring data to conservation pJamring. Although landscape and regional scale structuring of aquatic insect communities may be weak, it does not preclude the existence of smaller scale spatial structuring driven by local dispersal processes.
m DEDICATION
For my colleague and wife Wendy, who always believed in me when the going got tough.
IV Table of Contents
ABS TRA.C T ...... ii DEDICATION ...... iv Table ofContents ...... v List of Tables ...... 'Viii List ofFigures ...... x 1. Introduction ...... 1 Summary ...... 14 Refure11Ces ...... 17 2. The contnbution of riffles and riverine wet1ands to benthic macroinvertebrate diversity ...... 21 Introduction ...... 23 Materials and Metoods ...... 29 Study Area and Sites ...... 29 Macroinvertebrate Sampling ...... 30 Habitat Assesslllent ...... 32 Geospatial Data ...... 32 Data Analysis ...... 33 Results ...... 36 Discussion ...... 39 Acknowledgelllents ...... 45 Refure11Ces ...... 47 3. Congruence of biodiversity llleasures among larval dragonflies and caddisflies from three Camdian rivers ...... 62 Introduction ...... 63 Metoods ...... 68 Study Orga.nisms ...... 68 Study Areas ...... 70 Larval Salll_l)ling ...... 71 Ad ult Sampling ...... 73 Data amlysis ...... 74 Results ...... 76 Discussion ...... 79
V Acknowledgelllents ...... 85 Refere11Ces ...... 87 4. Limited evide11Ce for dispersal-driven differences in the composition of caddisfly and dragonfly assemblages in At1antic Canadian streams ...... 97 Introduction ...... 99 Methods ...... 103 Study Orga.nisms ...... 103 Envirolllllental varia.bles ...... 105 Data Analysis ...... 108 Results ...... 110 Discussion ...... 111 Acknowledgelllents ...... 117 Refere11Ces ...... 119 5. Scale-re1ated patterns of biodiversity in riverine insect assemb1ages: observations from the Canadian national biomonitoringprogram ...... 131 Introduction ...... 133 Methods ...... 13 8 The CABIN Biomonitoring Protocol ...... 138 Field-collected environmental variables ...... 139 Geo spatial Variables ...... 140 Data Analysis ...... 142 Results ...... 146 Discussion ...... 150 Acknowledgelllents ...... 157 Refere11Ces ...... 159 6. General Discussion and Conclusions ...... 177 Implications for professional practice ...... 186 Refere11Ces ...... 191 7. Errata ...... 196 Clia.pter 1 ...... 196 Chapter 2 ...... 201 Appeooix 1 ...... 202 Appendix 2 ...... 208 Appendix 3 ...... 211
VI Appendix 4 ...... 216 Permissions Curriculum Vitae
vn List of Tables
Table 2-1 : Rifile environmmtal variables measured for exploratory analysis of species environment relationships ...... 52
Table 2-2: Riverine wet1and environmental variables measured for exploratory analysis of species-environment relationships ...... 54
Table 2-3: Landscape and Jand-use variables measured for exploratory analysis of species-environment relationships ...... 56
Table 2-4: Average tax.on richness per site across habitats and non-parametric estimates of overall richness based on incidence data ...... 57
Table 3-1: Taxonomic resolution of the combined Renous and Nashwaak Odonata dataset before and after DNA barcoding...... 93
Table 3-2: Pearson Product-Moment Correlations between Odonata and Trichoptera taxon richness ...... 93
Table 4-1: Main-channel environmental variables subjected to forward selection for variance partitic) ning ...... 124
Table 4-2: Riparian wetland environmental variables subjected to forward selection for varia11Ce partitic)ning ...... 126
Table 4-3: Landscape and Jand-use variables measured for subjected to forward selection for variarice partitioning ...... 127
Table 4-4: Partitioning of variance among environmental and spatial predictors for Odonata and Trichoptera at the whole dataset and watershed scales. Values for each explained fraction are adjusted r2• In the case of the full dataset, sampling date was a significant explanatory variable for caddisflies and is included as a separate partitic)n in the analysis. Fractions are (a) purely environmentai (b) spatially structured environmentai (c) purely spatial and (h) total variance explained. In cases where date is an explanatory variable, we also show (d) purely date, (e) date*environment, (t) date*spatial and (g) date*spatial*environment...... 128
Table 4-5: Taxon and spatial scale of recent studies of dispersal-driven structuring of stream assemblages. Where distances were not stated, they were estimated from the maps provided. n/a: data not available ...... 129
Table 5-1: Standard environmental variables collected for CABIN site assessments. Note that data is missing for some sites depending on study objectives, budget and changes in
Vlll protocol Details regarding units and measurement methods can be fomd in the CABIN Field Manual: wadeable streams (Environment Canada, 2012) ...... 165
Table 5-2: Pearson product-moment correlations between Simpson's Diversity or estimated richness (Chaol) and latitude, longitude, altitude, pH and wetted width...... 166
Table 5-3: Variance partitioning resuhs for the Pacific Drainage and constituent sub drainages. Fractions are [a] date (regu]ar) or year (italics) [b] purely environmental, [c] purely spatiai [d] date or year*environment, [e] environment* space, [f] date or year*space, [g] date or year*environment*space. Significance for testable fractions [a, b, c] based onpermutational ANOVA bold: p<0.05 ...... 167
Table 5-4: Results of variance partitioning analyses in recent aquatic insect surveys... 169
IX List of Figures
Figure 2-1: Sampling locations in the Nashwaak River catchment, New Bnmswick, Canada...... 58
Figure 2-2: Sample-based rarefaction curves for riffies (b1ack) and riverine wetlands (grey) ...... 59
Figure 2-3: 2-DNon-metric multidimensional scaling plot based on Bray-Curtis dissimilarities. Riffle samples, b1ack; riverine wet1and samples, grey...... 60
Figure 2-4: Principal components ordination of the macroinvertebrate coll1ll1Llllity for (a) riffle samples and (b) riverine wetland samples. Arrows represent the strength and direction of significantly correlated environmental variables ...... 61
Figure 3-1: Survey locations in New Bnmswick, Canada for Jarval Odonata and Trichoptera (circles) and adult Trichoptera (squares) ...... 94
Figure 3-2: Homogeneity of multivariate dispersions for Odonata (b1ack), Trichoptera (medium grey) and rarefied Trichoptera (light grey) for each catchment. Error bars represent standard error...... 95
Figure 3-3: Corre1ation between distance to centroid values for Odonata and Trichoptera (b1ack) and Odonata and rarefied Trichoptera (grey) for the (a) Nashwaak, (b) Renous and ( c) Little Southwest Miramichi catchment...... 96
Figure 4-1: Schematic diagrrum of the (a) Nashwaak and (b) Little Southwest Miramichi/Renous catchments. e = edge connecting site, s = site ...... 130
Figure 5-1: Distribution of CABIN sampling locations. Samples included in this study are highlighted in red ...... 170
Figure 5-2: Distribution of study sites in the Pacific Drainage Area. Sub drainages included in variance partitioning are highlighted in yellow. (1- Skeena-Coast, 2-Queen Charlotte Islands, 3- Nechako, 4- Upper Fraser, 5-Southem Coastal Waters, 6- Thompson, 7- Lower Fraser, 8- Vancouver Is1and, 9- Skagit, 10- Columbia- U.S.A.) 171
Figure 5-3: Corre1ations between Simpson Diversity and selected environmental variables for the national dataset (a-e) and the sites within the Pacific Drainage (f-j) ... 172
Figure 5-4: Corre1ation between estirmted genus riclmess (chao) and Latitude (a) and Wetted Width (b) for the Pacific drainage ...... 173
Figure 5-5: Average distance to centroid values for Water Survey of Canada sub drainages. 1- Porcupine, 2- Peel and Northwestern Beaufort Sea, 3- Central Yukon, 4-
X Upper Yukon, 5- Stewart, 6- Pelly, 7- Alsek, 8- Headwaters Yukon, 9- Lower Lia.rd, 10- Upper Lia.rd, 11- Nass Coast, 12- Skeena-Coast, 13- Queen Charlotte Is1ands, 14- Nechako, 15- Central Coastal Waters ofB.C., 16- Upper Fraser, 17- Northern Newfotn1dland, 18- Southern Newfoundland, 19- Southern Coastal Waters ofB.C., 20- Saskatchewan, 21-Thompson, 22- Lower Fraser, 23- Vancouver Is1and, 24- Skagit, 25- Bow, 26- Cape Breton Island, 27- Columbia (U.S.A.), 28- Lake Winnipegosis and Lake Manitoba, 29- Upper South Saskatchewan, 30- Assiniboine, 31- Gulf of St. Lawrence and Northern Bay ofFtn1dy (N.B.), 32-Abitib~ 33- Missinaibi-Mattagam~ 34- Bay of Fundy and Gulf of St. Lawrence (N.S.), 35- Southeastern Atlantic Ocean (N.S.), 36- Northeastern Lake Sllperior ...... 174
Figure 5-6: Average distance to centroid values for Water Survey of Canada sub-sub drainages. 1- Upper Peei 2- Central Yukon - Sixty Mile, 3- Klondike, 4- Lower Stewart, 5- Upper Stewart, 6- Lower Pelly, 7- Headwaters Yukon - Nordenskiold, 8- Dezadeash, 9- Headwaters Yukon -Lake Laberge, 10- Big Sahnon, 11- Upper Pelly, 12- Upper South Nahanni, 13- Nisutlin, 14- Tagish, 15- Hyland, 16- Headwaters Lia.rd, 17- Upper Teslin, 18- Flat, 19- Upper Skeena, 20- Babine, 21- Lower Skeena, 22- Central Skeena, 23- Bulkley, 24- Morice, 25- Morseby Island, 26- Nechako Reservoir, 27- Southern and Eastern Bonavista Bay, 28- Headwaters Fraser, 29- Chilcotin, 30- Upper Fraser - Baker, 31- Bonne Bay, 32- Clearwater (B.C.), 33- East Central Vancouver Island, 34- McNaughton Lake -Colwnbia Reach, 35- Harrison, 36- South Thompson, 37- Lower Fraser - Nahatlatch, 38- Headwaters Columbia, 39- Nicola, 40- Central Colwnbia, 41- 0kanagan, 42- Lower Kootenay, 43- Strait of Georgia - Squamish, 44- South Central Vancouver Island, 45- Lower Fraser Coast, 46- Cheticamp, 47- Wreck Cove, 48- Skagit (B.C. - Wash), 49- Kouchtbougouac, 50- Mossy, 51- Central Oldman - Belly, 52- Pointe Wolfe, 53- Annapolis, 54- Mersey ...... 175
Figure 5-7: Correlations between average distance to centroid values and the coefficient of variation for altitude, pH and (log) wetted width. Sub drainage units (a-c), sub-sub draimge units (d-f) ...... 176
Figure 6-1: The effect of resampling on observed average distance to centroid values. Error bars reflect standard error...... 195
XI 1. Introduction
Stewardship of aquatic resources is moving to the forefront of political and enviromnental agendas due to myriad stresses p1aced on freshwater ecosystems. 1he list of threats to freshwater ecosystems upon which hmnans rely includes eutrophication, organic and inorganic chemical pollution, modified hydrologic regimes and food web alteration due to overharvesting and introduction of exotic species (Vorosmarty et al
2010). In many cases these threats are amplified due to climate change. While addressing these threats is largely a political challenge, scientists p1ay key roles in identifying problems and developing and implementing solutions.
Identifying problems involves assessment and monitoring. These are activities that measure the heahh of aquatic ecosystems and attempt to diagnose the drivers behind deviations from acceptable conditions. The most widely used definition posits that heahhy ecosystems have stable and sustainable organization (e.g. food web structure and biodiversity) and vigour (ecosystem processes, e.g. primacy production and metabolism), and are resilient to additional stress (Costanza 1992). Assessment and monitoring involve the identification of indicators of ecosystem heahh and implementation of programs to evaluate ecosystems using these indicators. This can involve direct measurement of the abiotic enviromnent (e.g. chemical testing of water quality) or measurement of organisms that inhabit that environment. The use of biodiversity, organisms or organism properties to measure ecosystem heahh is known as biomonitoring or bioassessment. Increasingly, biotic approaches are being used to measure ecosystem heahh because organisms
1 integrate the impact of anthropogenic stressors over longer periods of time, rather than the snapshot provided by, for example, a water quality sample (Rosenberg & Resh 1993 ).
Parallel to the diagnosis of ecosystem health is the prevention of impairment through conservation Conservation involves protecting organisms and their habitats to ensure that ecosystem structure and fimction are maintained. Systematic conservation planning uses physical and biological data (particu1arly biodiversity information) to identify the amollllt and spatial arrangement of conservation units that will best protect structure and fimction, or provide the most efficient solution needed to achieve a given level of protection (Margules & Pressey 2000). Biodiversity assessment (as opposed to bioassessment) involves measuring and llllderstanding patterns of biodiversity
(taxonomic or genetic composition, riclmess and beta diversity), and is a cornerstone of systematic conservation planning. Somewhat surprisingly, the development of systematic conservation planning has Jargely focused on terrestrial ecosystems, while ignoring the unique properties of aquatic ecosystems (N el et al 2009). However, this trend appears to be shifting as many authors recognize that different approaches are necessary to effectively conserve biodiversity in aquatic ecosystems (Linke, Norris & Pressey 2008,
Roux et al 2008, Leathwick et al 2010).
Scientists working in :freshwater ecosystems have embraced the challenge of identifying suitable biological indicators of ecosystem health, and understanding how these indicators respond to the range of anthropogenic stresses descnbed above. Early in the development of biomonitoring methods, many freshwater ecologists recognized the
2 potential for macroinvertebrates to serve as an indicator of ecosystem heahh. As a group, these organisms are diverse, comprise a Jarge proportion of the standing biomass of aquatic ecosystems and demonstrate a range of tolerances to various anthropogenic stressors (Lenat 1993). Early biomonitoring approaches surrnnarized information on the composition and abmidance of invertebrates into metrics (e.g. taxa richness, Shannon or
Simpson diversity, % tolerant taxa, Hilsenhoff biotic index, Hilsenhoff (1988)), or indices that incorporate numerous metrics into their calcu1ation (Kerans & Karr 1994), and compared values derived from unimpacted and impacted locations.
A drawback of the metric or index approach is that it is regionally limited and muhiple metrics within an index can contain redundant information (Reynoldson et al
1997). An ahemative approach to biomonitoring is the multivariate analysis of assemb1age information Often, the multivariate analytical :framework relies on a reference condition approach (RCA). A m.nnber of nations have adopted this approach for biomonitoring, including the Canadian Aquatic Biomonitoring Network (CABIN, http://www.ec.gc.ca/rcba-cabin/). In contrast to study designs that utilize observations along a stressor gradient, upstream'downstream of a stressor, or before/after controVimpact (BACI) designs, the RCA measures the composition of the invertebrate assemb1age across a range of unimpacted or ''best available" sites considered representative of the study area. This information is used to develop a model that predicts the taxa which should occur at a site at different probability levels, or uses a discriminant model to predict test site group membership (Reynoldson et al 1997). Uhimately, both models assess the degree to which the assemb1age of invertebrates at a site suspected of
3 impairment differs from that expected in the absence of anthropogenic stress. The primary advantage of the reference condition approach is that it does not require pre impact information in order to assess a given site. In principai the RCA also attempts to account for noise in macroinvertebrate assemblages by comparing individual test sites to muhip le reference sites.
Both biomonitoring and systematic conservation planning rely upon basic biodiversity information to address their questions - that is, lists of taxa from a number of sites that have been consistently and sufficiently sampled. Collecting this information is expensive, requiring samples across a large number of sites and considerable geographic area, and in the case of macroinvertebrates, Jabour-intensive sorting and identification of specnrens in a laboratory. This has led to the suggestion that data from established biomonitoring prograim be employed in systematic conservation planning (Linke &
Norris 2003).
There are a number of reasons to suggest biomonitoring information may be unsuitable for systematic conservation planning in riverine ecosystems. First, while biomonitoring protocols such as CABIN attempt to incorporate variation among sites into predictions, they also seek to reduce noise by sampling mesohabitats that display relatively low assemblage variation In lotic systems, mesohabitats are considered discrete areas of stream exlnbiting similar physical conditions - depth, velocity, slope, substrate and cover (Tickner et al 2000). The iresohabitat of choice in biomonitoring programs has becotre riffles. Riffies are shallow areas of higher velocity and turbulence
4 in the main channel of rivers and strerum, where the substrate is composed primarily of
silicate material greater in size than sand (Gordon et al 2004). Riffies are created where there is an increase in the slope of the stream channel or by obstructions such as fallen trees, and occur at an interval of approximately 5-7 stream widths. Riffies are well oxygenated and contain abundant habitat for macroinvertebrates, particu1arly the Jarval
stages of aquatic insects. They are easy to sample, as the current can be used to carry physically dislodged organisms into a net. The combination of high taxon richness, wide
distribution of rifl1e mesohabitats in wadeable streams and rivers, and ease of sampling
has led to a focus on riffles in biomonitoring programs, ahhough attempts have been
made to develop protocols for other mesohabitat types (e.g. Australian River Assessment
System, Simpson & Norris 2000).
Sampling focused on a single habitat type raises the question of whether biodiversity patterns are indicative of other habitats. There has been extensive work comparing the composition and richness of rifiles to that of pools. Pools are low-velocity
habitats in the mainstem of rivers which form downstream of riffles. These studies have
generally found that the invertebrate assemblages of pools are a subset of those found in
rifiles (Brown & Brussock 1991, Logan & Brooker 1983). Fast-flowing, non-turbulent waters (nms), and areas of high flow and turbulence (rapids) are less well studied. It appears that the diversity and composition of the macroinvertebrate fu.tma in nm habitats
is broadly similar to that of riffles (Pridmore & Roper 198 5), and while rapids may
support a number of specially adapted taxa, they typically display low diversity compared to riffles (Englund & Mahn}vist 1996).
5 Lentic habitats associated with the main channel of wadeable rivers and streams have simi1ar ly received little attention in biomonitoring. This is due in part to the Jack of a widely accepted framework for c1assifying such habitats. While several c1assificatio ns have been proposed (Brinson 1993, Hawkins et al 1993, Rabeni et al 2002), broader application has been limited by their regional specificity. A greater impediment to the study of lentic habitats is the difficulty associated with consistently sampling the resident macroinvertebrate assemb1age. Lentic mesohabitats Jack sufficient flow to carry dislodged organistm into a net. While a more "active" sampling teclmique can overcome this problem, there is often a great deal of debris captured during sample collection, increasing the time and costs associated with sample processing.
A drawback to the use ofbiomonitoring data in conservation planning is the biased representation of taxonomic groups within samples. Certain taxa may be poorly represented due to differences in catchability (e.g. strong swimmers may evade the net), or because Jarval distnbutions exhibit patchiness re1ative to the scale of the sample. This is less of a problem for monitoring data because the extent of bias should not differ between samples. However, just as rifiles may not reflect biodiversity patterns of the wider aquatic habitat, the biodiversity patterns of groups frequently captured in a standardized kick sample may not reflect those of groups which are present but do oot occur consistently across samples. This is a major concern if poorly represented groups have particu1ar conservation significance, or have been identified as indicator taxa.
6 In addition to understanding the congruence of biodiversity patterns among habitats and taxa, both biomonitoring and conservation require an tlllderstanding of drivers that detennine taxon richness, assemblage composition and variation in assemblage composition (beta diversity). Emphasis has traditionally been p1aced on the importance of niche-driven processes, a1so known as the species sorting paradigm (e.g.
Doledec and Chessel 1994). In this view, the distributions of organisms are limited by the constraints of the abiotic environment and their interactions (e.g. predation and competition). Due to trade-offs among ecological traits, individuals can occupy onJy a subset of niche space. 1be result is that the composition and riclmess of taxa in a given locality can be predicted based on knowledge of local environmental conditions and biotic interactions. In both biomonitoring and biodiversity conservation, there is an tlllderlying assumption that assemb1age composition will track local environmental conditions.
Building upon the theory of island biogeography (MacArthur & Wilson 1967),
Hubbell (2001) recognized that spatial processes such as dispersal could a1so be a driving force behind the structuring of species assemblages. If the capacity for species in the regional pool to disperse differs, then assemblage composition can be decoupled from environmental detenninants and biotic interactions (Hurtt & Pacala 1995).
Overproduction can a1so result in the dispersal of individ ua1s into adjacent locations from sub-optimal habitats (mass effects, Shmida & Wilson 1995). Dispersal limitation and mass effects create spatial structure in assemb1ages, resulting in the spatial autocorrelation of assemb1ages and decay of assemblage similarity with increasing spatial
7 separation (Allouche et al 2008). Spatially-autocorre1ated environmental conditions can also create spatial structure in assemblages. However, if dispersal processes create independent spatial structures in freshwater invertebrate assemblages, they could have implications for both the interpretation ofbiomonitoring data and developirent conservation strategies.
Research has been directed toward the elucidation of iretacommunity concepts that incorporate both niche and dispersal processes into their models of connmmity assembly (Leibold et al 2004), an approach increasingly embraced by those who study riverine ecosystems (Brown et al 2011 ). Identifying the organisms, habitats and spatial scales where different processes dominate is necessary to address when and how to incorporate spatial infonnation into biomonitoring or conservation analyses. Separating the effects of niche and dispersal processes on assemb1age structure can be difficult due to spatial autocorre1ation of environmental conditions. Detection of dispersal structuring in riverine ecosystems is :further complicated by the branching network architecture of river drainages because the moveirent of organisms in rivers is wholly or partially restricted by the channel network. Aquatic insects may drift downstream during their
1arval life stage (Brittain & Eike1and 1988), and as adults they may disperse upstream or downstream along the river corridor (Williams & Williams 1993) or cross into the terrestrial habitat matrix (MacN eale, Peckarsky & Likens 2005). There is clearly potential for spatial structuring of aquatic biodiversity independent of environmental constraints, but the relative strength of spatial and environmental drivers of assemblage
8 composition, the scales where each dominate, and their influence on different taxonomic groups remains tmresolved.
The two primary objectives of this thesis are to better ID1derstand the suitability of invertebrate biom:mitoring tools as a wider biodiversity assessment tooi and to investigate the role of spatial drivers in structuring riverine insect assemb1ages. Two articles address the first objective. In Article 1 (Curry et al. 2012a) I compare invertebrate biodiversity patterns from a sl.llVey of riverine wet1and mesohabitats to information derived from riffle samples collected using the CABIN biomonitoring protocol In the second article (Curry et al 2012b) I compare spatial patterns of biodiversity in two conspicuous groups of aquatic insect: caddisflie s (Trichoptera) and dragonflies and damselflies (Odonata).
Articles 3 and 4 address the second objective. If dispersal limitation leads to spatial structuring of aquatic insect assemb1ages, then a group that experiences greater dispersal limitation should also demonstrate greater spatial structuring. Article 3 sought to test this prediction utilizing Trichoptera and Odonata assemb1age information while incorporating a suite of environmental variables into the analysis. Given their weaker dispersal abilities,
Trichoptera are expected to demonstrate greater spatial structuring independent of environmental structuring than Odonata. Article 4 addresses whether scale influences the degree of spatial structuring observed in aquatic insect assemblages; as spatial extent increases, the role of dispersal limitation in structuring assemblages should also increase.
As a result, the component of assemblage variation explained by purely spatial fuctors
9 should be greater in larger-scale datasets. To test this prediction, and document wider patterns of aquatic insect biodiversity in Canada, I utilized information housed within the
CABIN biomonitoring sample database.
The thesis is divided into four articles:
Article 1: The contribution of riffles and riverine wetlands to benthic macroinvertebrate biodiversity (Curry C.J., Curry RA. & Baird, D.J. 2012 Biodiversity and Conservation
21: 895-913).
If data derived from biomonitoring programs are a true representation of macroinvertebrate biodiversity in riverine ecosystems, then such data will ideally reflect patterns in tmSampled iresohabitats. While this appears broadly true for main-channel iresohabitats (see above), it is not clear how riffle macroinvertebrate diversity compares to episodically connected riverine wet1ands. Riverine wetland rresohabitats in smaller rivers or wadeable streams may sheher tmique taxa and demmstrate different patterns of biodiversity than adjacent riffles; conversely, given their strong connection to the main channei riverine wetlands may display similar biodiversity patterns as riffles.
This chapter addresses congruence of imcroinvertebrate biodiversity patterns descnbed from CABIN biom:mitoring samples and adjacent riverine wet1ands. I conducted a field study across 18 sites in the N ashwaak River, New Bnmswick, Canada.
Three aspects ofbiodiversity were investigated: assemblage composition, taxon richness,
10 and assemblage variation Though harbouring similar numbers of macroinvertebrate taxa, riverine wet1ands contained distinct and nnre variable assemblages than riffles, and patterns of taxon richness and beta diversity were mcorrelated between the two mesohabitats. I discuss the implications of the resuhs for biodiversity assessment and conservation in riverine ecosystems. 'The results serve as the basis for including riverine wetlands in the sampling design of the study upon which Articles 2 and 3 are based.
Article 2: Congruence of biodiversity measures among larval dragonflies and caddisflies from three Canadian Rivers (Curry, C.J., Zhou, X. & Baird, D.J. 2012, Freshwater
Biology 57: 628-639).
Samples of the macroinvertebrate connmmity in rifiles attempt to capture the most abmdant taxa occurring at a site; indeed, they are often subsampled, and rare taxa are often downweighted or excluded in the development of reference condition models.
Taxa that are not abundant, exhibit a large degree of patchiness relative to the scale of sampling, or are present in msamp led mesohabitats will be absent from or mderrepresented in biomonitoring samples. If macroinvertebrate assemblage data from biomonitoring progra.rm are to be utilized for biodiversity assessment, then it should reflect the biodiversity of underrepresented groups. In this chapter, I sought to detennine whether patterns of taxon richness and assemblage variation from a well-sampled group
(Trichoptera) were congruent with those of a group that is often underrepresented m biomonitoring data (Odonata).
11 Odonata and Trichoptera contrast markedly in their dispersal capacities and fimctional diversity. Odonata have longer adult lifespans and strong flight capabilities, resulting in considerable potential for displacement from their natal habitat (Corbet
1999). In contrast, Trichoptera are poor fliers and have short adult lifespans (Peterson et al 1999). If dispersal limitation leads to greater assemblage variation, then one would expect Trichoptera to demonstrate greater assemblage variation than Odonata.
I employed a multi-habitat sampling protocol to complete surveys for Trichoptera and Odonata across 34 sites in three 5th order (sensu Strahler 1952) river systems in New
Bnmswick, Canada (N ashwaak, Renous/Dungarvon and Little Southwest Miramichi). I calculated correlations between their richness and assemblage variation (distance to centroid values) across sites and compared the magnitude of their assemblage variation
(average distance to centroid values). Given the greater ablllldance of Trichoptera across sites, I also conducted these analyses with rarefied odonate assemblage data to :facilitate comparison based on equal abundance. The results are discussed in the context of biodiversity assessment and dispersal-driven assemblage structure.
Article 3: Limited evidence for dispersal-driven differences in the composition of caddisfly and dragonfly assemblages in Atlantic Canadian stream;. (Curry, C.J. & Baird,
D.J., in prep).
The extent to which local (niche) and spatial (dispersaQ processes structure assemblages could influence the design of biomonitoring or conservation initiatives,
12 particularly if the re1ative strength of these structuring forces differs among taxonomic groups. Building upon the description of re1ative biodiversity patterns in Chapter 2, I sought to test the hypothesis that dispersal would be an important structuring force in riverine insect assemb1ages, and that the strength of dispersal structuring would be inversely re1ated to dispersal capacity. If correct, Trichoptera with re1atively poor dispersal capacity should demonstrate greater spatial structuring independent of environmental variation and weaker re1ationships to environmental variables than strongly dispersing Odonata.
I evaluated the exp1anatory power of two sets of spatial predictors: a simple linear descriptor based on Jatitude and longitude, and a network-based predictor based on the distance between sites along the river network (Asymmetric Eigenvector Maps, Blanchet,
Legendre & Borcard 2008). There was little evidence that differences in dispersal capacity between Odonata and Trichoptera created differences in the level of purely spatial structuring in their assemb1ages. However, the re1atively small spatial scale covered by the sites and the broader geographic ranges of most aquatic insect species suggest that Jarger scales may be necessary to detect spatial structuring in aquatic insect assemb1ages. The need to test for spatial structuring across Jarger scales and provide a broader spatial context for the patterns of biodiversity observed in New Bnmswick
Trichoptera and Odonata assemb1ages was the pretext for the final article.
Article 4: Scale-re1ated patterns of biodiversity patterns in riverine insects: Observations from the Canadian national biomonitoring program.
13 Despite being scale-dependent phenomena, biodiversity measures such as richness and beta diversity and the processes which structure them are rarely studied across regional or larger scales (ie. > 1,000,000 km2). Obtaining sufficient replication across a large spatial area is both logistically and financially difficult.
The final article of my thesis addresses the hypothesis that the proportion of assemb1age variation in aquatic insects explained by purely spatial ractors will be greater at larger spatial scales. I used riverine insect assemblage data collected llllder the umbrella of the Canadian Aquatic Biomonitoring Network which is a national-scale biomonitoring program with corrn:non methods and quality assurance/quality control
CABIN has a dataset consisting of over 13,000 samples has been accumu1ated by network partners. I examined a subset of 1,492 samples with genus-level taxonomy and re1ated environmental data. To this information I added a number of land use, climate and physical variables to assess the level of spatial structuring in riverine insect assemblages.
Given the spatial distnbution of suitable samples in the CABIN dataset, I limited this analysis to the Pacific Drainage and ten of its constituent sub drainages.
Summary
My thesis makes three major contributions to the study of riverine insect biodiversity patterns. First, I highlight the importance of riverine wetland mesohabitat to the overall richness of invertebrate taxa in riverine ecosystems. I demonstrate that
14 nvenne wet1ands contain different and trore variable invertebrate assemblages than riffle mesohabitats; while this resuh was not unexpected, to date there are few quantitative data supporting this notion Second, I demonstrate that spatial variation in biodiversity is not necessarily consistent across mesohabitats and taxonomic groups, potentially limiting the use of biomonitoring data in conservation p1anning.
In article 2, I demonstrate that biodiversity patterns are dissimilar between corrnnon and rare taxonomic groups that are observed in biotronitoring applications. Contrary to expectations, Odom.ta demonstrated greater assemblage variation than Trichoptera in two of the three catchments studied, while their patterns of richness and beta diversity were only weakly correlated. However, accollllting for differences in ablllldance in the analyses considerably increased the strength ofthese correlations and eliminated differences in the magnitude of assemblage variation
Finally, articles 3 and 4 demonstrate the relatively weak influence of spatial factors in structuring aquatic insect assemblages. Across both intermediate scales (e.g. 5th order streams, article 3) and larger scales (e.g. WSC Pacific Drainage, article 4), spatial variables consistently explained a small atrount of variance in partitioning analyses. The resuhs also highlight the need to tie dispersal behaviolll' directly to predictions regarding the scales at which spatial structlll'ing will be observed.
Together, my contnbutions provide insights into how riverine insect biodiversity differs between habitats, taxonomic groups and spatial scales. This thesis also evaluates
15 the role of dispersal processes in structuring aquatic insect assemb1ages by incorporating spatial predictors into the analysis of assemblage data, a shortcoming which to date has not been adequately addressed in the biomonitoring literature. By addressing these problems, my thesis c1arifies the suitability ofbiomonitoring data for biodiversity assessment in riverine ecosystems, and highlights areas where supplementary information may be required.
16 References
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20 2. The contribution of riffles and riverine wetlands to benthic
macroinvertebrate diversity
Curry, C.J., Curry RA. & Baird, D.J. (2012) The contnbution of rifiles and riverine wetlands to benthic macroinvertebrate diversity. Biodiversity and Conservation, 21, 895-
913.
Abstract: Benthic macroinvertebrates collected in biomonitoring progratm are a potentially valuable solll'ce of biodiversity information for conservation p1anning in river ecosystems. Biommitoring samples often focus on rifiles; however, we have only partially assessed the extent to which riffle biodiversity patterns reflect those of other river habitats, particularly riverine wetlands. Using a standard biomonitoring protocoi we assessed the richness, composition and magnitude of variation of macroinvertebrate assemblages in riffies across 18 sites in the Nashwaak river catchment, and compared these to samples from adjacent riverine wetlands. Despite containing on average fewer ta:xa per site than riffies, riverine wetlands demonstrated similar levels of taxon richness at the catcmrent scale. There was strong assemblage separation between habitat types, and riverine wet1ands displayed significantly greater assemblage variation than riffles.
Riffles and riverine wetlands did not demonstrate significant correlations in tenns of taxon richness or assemblage variation, though this may be partially due to the scale at which we collected observations. Principal component analysis with vector fitting suggested that (log) sub-catcmrent area was an important fuctor structuring riffle assemblages, while depth was potentially important for riverine wetland assemblages. We
21 discuss the implications of these results for the use ofbiom:mitoring data in systematic conservation p1anning, and identify future research that will improve om tmderstanding of the role riverine wetlands p1ay in maintaining catchment biodiversity and ecosystem processes.
Keywords: macroinvertebrate; riffle; riverine wetland; biomonitoring; biodiversity assessment
22 Introduction
Systematic approaches to conservation platming for freshwater ecosystems are gaining increased attention from ecologists because of increasing anthropogenic pressures (Strayer and Dudgeon, 2010, Vorostmrty et al, 2010), as well as the realization that existing reserve networks have been developed from terrestrial biodiversity information, and may not adequately protect aquatic biodiversity (Herbert et al, 2010,
Nel et al, 2009). Accurate biodiversity infonnation is crucial to science-based conservation platming, and numerous sources of biodiversity information are available.
Biommitoring studies are a key source, particu1arly in river ecosystems where a number of national and sub-national biommitoring programs already exist. Increasingly, biomonitoring studies are designed armmd a reference condition approach (Reynoldson et al, 1997) where assemb1age structure in pristine or least-impacted sites is used to predict the composition of ''test" sites with similar habitat characteristics, but subject to anthropogenic disturbance. Details of individual approaches differ, but a common element is the sampling ofbenthic invertebrate assemb1age composition across several sites within a Jandscape of interest. The raw data collected in aquatic biomonitoring studies - patterns of taxon richness, assemb1age composition, and variability of assemb1age composition among sample units - are similar to required inputs for systematic conservation platming. Thus, it is not surprising that some researchers have suggested we use biomonitoring data for systematic conservation platming in river ecosystems (Linke and Norris, 2003).
23 Benthic invertebrates are well suited to both biodiversity assessment and biomonitoring in rivers because they are ecologically diverse (accounting for upwards of 85% of species in freshwater systems, (Balian et al, 2008)), and can be collected in large mnnbers with re1atively little effort. Benthic invertebrate assemb1ages respond to a wide range of stressors (Rosenberg and Resh, 1993 ), making them the focus of several national biomonitoring progrrum, including the Canadian Aquatic Biomonitoring Network
(CABIN, http://www.ec.gc.ca/rcba-cabin/). In addition to issues of scale and cross-taxon congruency of biodiversity information, a potential drawback to the use of benthic invertebrate biomonitoring information to establish conservation priorities is the limited habitat focus of some biomonitoring programs. River ecosystems are dynamic networks where the downstream movement of water and dissipation of energy, coupled with spatial and temporal variation in the supply of water (ie. floods, droughts), and other disturbances generate an array of river habitats with varying physical and chemical properties (Benda et al, 2004, Frissel et al, 1986). These habitats include, for example, pool-rifile sequences, channel margins, riverine wetland habitats, and the hyporheic zone.
While some programs, such as the Australian River Assessment System (AUSRIVAS,
Simpson and Norris, 2000), have developed reference condition models for a variety of river habitats (e.g. pools, riftles, edge habitat), financial and logistical constraints often require the collection of samples from a single habitat type at each site. For example, the
Canadian Aquatic Biomonitoring Network (CABIN, Environment Canada, 2008) focuses on riftle habitats to collect benthic invertebrates, due to ease of sampling and the repeatability of this habitat throughout different catchments. However, the choice of
24 habitat for biomonitoring has implications for the future use of that information in conservation p1anning. Using biodiversity information from riffles to formulate conservation p1ans that affect entire catchments (or greater scales) requires greater knowledge of how riffle biodiversity reflects that of other riverine habitat units, and of the river ecosystem as a whole.
To date, the freshwater biomonito ring and biodiversity assessment literature has only partially addressed this problem A number of studies comparing the re1ative biodiversity of macroinvertebrates in riffle and pool habitats of up1and rivers have found that riffles generally support greater macroinvertebrate abmdance and riclmess, while pools generally contain a subset of the fin.m fomd in riffles (Brown and Brussock, 1991,
Logan and Brooker, 1983), though the opposite pattern may occur in low1and rivers
(McCulloch, 1986). One c1ass of habitat that may differ considerably from the main channel in tentlS of taxon riclmess, assemblage composition and assemb1age variation are riverine wetlands. These habitats are riverine lentic water bodies that maintain a laterai non-permanent connection to the main channel of streams and rivers through overbank flow, and their dominant hydrodynamics are unidirectional and horizontal (Brinson
1993). Riverine wetlands can be distinguished from habitats that ''interrupt" the main channei such as lateral scour pools and damned pools created by debris, landslides, or beaver activity (sensu Hawkins 1993), though they may include backwaters and abandoned channels. They can also be distinguished from Jakes, ponds and headwater wetlands that are original nodes in a catchment, with little or no surface water inflow.
25 Floodplain lakes and riverine wetlands are well studied for large, lowland rivers where extensive floodp1ains are present. Here they influence many ecological processes, including nutrient cycling (Tockner et al, 1999), fish reproduction (Copp, 1989,
Rodriguez and Lewis, 1997) and storage/downstream transport of materials (Ward et al,
2002). Studies of community structure in floodplain water bodies have focused on the importance of connectivity and hydroperiod, and have typically observed decreasing simi1arity in composition with decreased connectivity to the main channei though relationships are complex and depend on the habitat requirements and niche breadth of different species (Amoros and Bornette, 2002). In contrast to the floodp1ains of larger rivers, the riverine wetlands of headwater streams and smaller rivers have received fur less attention, even though these strea.m5 and rivers directly drain a Jarge proportion of the total landscape (Tockner and Stanford 2002). We know little regarding their role in ecosystem processes and ecological interactions, and the benthic invertebrate fauna of riverine wetlands are poorly described when compared to main-channel habitats such as riffles and pools (Williams et al, 2003).
Knowledge of the relative biodiversity of main-channel and riverine wetland habitats is essential for the design and implementation of conservation strategies as any measures taken are likely to affect multiple habitats (Bilton et al 2006). Indeed, riverine water bodies are often the first habitats lost when rivers are channelised, their flows 1rodified or undergo shoreline alterations (Tockner and Stanford 2002). Given that riverine wetlands are not directly comected to each other by flow, and their physical structure can vary greatly from one location to another, one might expect them to support a distinct
26 invertebrate :fauna, demonstrate greater variation in taxonomic composition, and have patterns of taxon richness and assemb1age variation that do not corre1ate with adjacent rifile habitats. Ahematively, however, strong hydrological connections with the main channel may dominate riverine wetland habitats in headwaters and small rivers, thus supporting invertebrate communities with similar patterns of biodiversity.
To our knowledge, comparative studies of biodiversity in the main channel and adjacent riverine wet1ands for smaller rivers are scarce. In the Tagliamento River in northeastern Italy, Arscott et al (2005) fmmd that the invertebrate assemblages of floodplain water bodies were distinct from the main channei while Rabeni et al (2002) demonstrated in the Jacks Fork River, USA, that slackwater habitat units were compositionally distinct from :fast and slow water habitat units. However, neither study investigated cross-habitat corre1ations in richness or assemb1age similarity.
Studies of pond and ditch diversity in relation to lotic diversity in southern Eng1and folllld that while ponds supported fewer species on average than river sites, their overall richness and variability was greater than ditches (Williams et al, 2003). Agricuhural ditches supported fewer species overall than ponds or stream; but harboured many llllconnnon taxa, likely because of their ephemeral nature. In a small-scale study of macroinvertebrate and macrophyte diversity on the River Frome and an associated ditch,
Armitage et al (2003) folllld 202 taxa, 59 of which were unique to surveys of the ditch habitat. Similar results were observed in Florida by Simon and Travis (2010), and in a survey of ponds and ditches in agricuhural areas of Europe (Davies et al, 2008).
27 However, these studies focused on agricultmal areas where anthropogenic featmes
( drainage ditches) are a connnon eleirent of the Jandscape and a long history of drainage
modification (e.g. flow aherations due to irrigation, channelisation) may have had Jarge
impacts on the regional composition of the lotic benthic conmrunity. Thus, the generality
of these patterns in other regions and bioires, particu1arly forested catchtrents, has yet to
be established. 1hese studies have also focused on patterns of species richness and
composition, while the congruence of biodiversity patterns (alpha, beta diversity)
between habitats and the relative magnitude of their assemblage variation remains
llllclear. Thus, the question of whether one can exclusively use data from main-channel
habitats (riffles in particu1ar) to make catchment-wide assumptions regarding biodiversity
patterns remains unanswered.
The present study sought to expand om knowledge of relative biodiversity patterns in riflle and riverine wetlands of a 5th order catchment (based on a 1 :50,000 scale mapping)
in New Bnmswick, Canada. Riverine wet1ands such as those descnbed above are a
connnon featme of small (:S5th order) rivers and headwater strea.111.5 throughout much of
Eastern North Atrerica. These rivers have generally experienced less flow modification than low1and rivers and, as a resuh, their biodiversity may be comparatively less homogenized, which suggests these areas have high conservation significance (Freermn et al, 2007). Focusing on the benthic macroinvertebrate conmrunity, we posed fom
questions:
I - What is the degree of assemblage overlap between riffles and riverine wetlands?
28 II - Do riverine wet1ands demonstrate greater assemblage variation than riffles?
III -Are riffles and riverine wetlands correlated in terms of taxa richness and assemblage variation?
IV - Are the potential environmental drivers of assemblage variation in riffles and riverine wetlands similar?
Materials and Methods
Study Area and Sites
The Nashwaak River is a fifth order (sensu Strahler, 1952) tributary of the Saint
John River in New Bnmswick, Canada, draining an area of 1708 km2 (Figure 2-1 ). Its headwaters are located in the N orthem New Bnmswick Up1ands terrestrial ecoregion, transitioning along its length to the Maritime Lowlands ecoregion The catchment is sparsely populated except for the lower reaches close to its confluence, where there is also limited agricultural activity in the form of cattle grazing and hay cropping. The primary vegetation cover consists of closed stands of mixed hardwood and softwood forest, while alders form a large proportion of riverine vegetation in headwater areas.
Forestry activity, both historic and ongoing, is prevalent throughout the catchment. The influence of forestry and associated activities (e.g. road building) on benthic invertebrate conmnmities in the Nashwaak catclnnent has not been studied previously, however, we chose sites with intact riverine buffer zones that were free from obvious disturbances. We
29 defined a site as a contiguous section of stream channel ca. 50 meters in length encompassing both riffle and slow-flowing areas, and any riverine wet1ands within 25 meters of the main chamtei though in practice all sampled habitats occurred within 15 meters of the stream channel We chose eighteen sites to sample based on the presence of significant areas of riverine wet1ands and accessibility, and to reflect the range of stream orders in the catchment (Figure 2-1), though at one site (NA4) riverine wetland habitat was no longer present when sampling occurred. We conducted sampling between Jlllle 13 and Jme 27, 2005.
Macroinvertebrate Sampling
Benthic macroinvertebrate Jarvae are widely recognised as an ideal group for biomonitoring studies because they are diverse, abmdant, and easily collected. We collected samples of the benthic macroinvertebrate community from both ri:file and riverine wetland habitat. We collected a 3-minute sample of the riffle benthos using a 30 cm triangular frame kick net with a 400 µm mesh size and detachable collection cup.
Sample collection followed the protocol described in the CABIN Field Methods manual
(Environment Canada, 2003, 2008); however, during processing we split the sample into coarse (> 1 mm) and fine (400 µm - 1 nm) fractions using circu1ar brass sieves. We retained both fractions and preserved them in 95% ethanoi however, we only identified the coarse fraction for this study. Morin et al (2004) identify 1 mm as the minimum mesh size where unbiased descriptions of lotic benthic communities can be obtained. Riverine
30 wetlands typically lack the flow conditions that woukl pennit an identical kick net sample to be collected. Instead, we sampled riverine wetland areas using a 25 cm diameter circular frame pond net with a 400 µm iresh size and detachable collection cup. The collector disturbed the substrate to a depth of 5-10 cm while continuously sweeping the net through the phnne of disturbed materiai as close as possible to the surface of the substrate. Sampling was time-limited (3 minutes) to allow comparison with the riffle sample, however it could be interrupted if the net filled with too much material or becrure otherwise obstructed. We typically encountered large rurounts of debris in these samples; in order to reduce the volume of material requiring preservation to a practical levei we rinsed and closely inspected Jarge debris such as stones, branches or leaves for invertebrates before discarding. Otherwise, we processed riverine wetland samples in an identical fashion to riffie samples.
We counted and identified all insects to family level and the remaining invertebrates to order/class level using the taxonomic keys of Merritt and Cummins (1996). Family-level taxonomy does not typically require an extensively trained taxonomist, and in most cases requires less time than genus or species-level identification Data are cheaper to collect and greater spatial coverage can be attained as a result. Family-level data can provide valuable information regarding patterns oftaxon richness and assemb1age variation and we expected the majority of assemb1age differences rurong habitats to occur at higher levels of taxonomic organization However, :family- level taxonomy is inadequate for situations where native and non-native taxa must be distinguished, and it masks the
31 presence of rare species, both of which may be important in developing a conservation strategy (Heino and Soininen 2007).
Habitat Assessment
We measured a suite of physicai chemicai and biological variables in the riffle habitat at each site following standard protocols described in the CABIN Field Methods
Manual (Environrrent Canada, 2003, 2008). The variables we measured in riffle habitats, their average, and their range are listed in Table 2-1. Where possible we duplicated these measurements in the riverine wetland (summarized in Table 2-2). In addition, we also measured maximum length, width, and depth to descnbe the dimensions of the riverine wetland areas sampled at each site.
Geos patial Data
We derived a suite of variables descnbing catchment conditions upstream of each site, summarized in Table 2-3. We derived elevation and average slope from a 30 m resolution Shuttle Radar Topography Mission (SRTM) digital terrain IIDdel (DTM). To calculate upstream catchment area and elevation, we first processed the DTM through a combination of filling and breaching. A known stream network ]ayer provided by the
New Bnmswick Department of Natural Resources (NBDNR) was then burned into the processed SR'fM DTM. Sites were snapped to the network, allowing upstream
32 catchments to be delineated and their area calculated. All geoprocessing was performed using the ArcGIS 9.2 Watershed Delineation Toolbox.
Stream order was detennined using the Strahler irethod (Strahler, 1952) applied to a
1:50,000 scale mapping of the stream network in the N ashwaak River catchment. The distance along the stream network from each site to the confluence of the catchment was detennined using the ArcGIS 9.2 Network Analyst Toolbox.
We obtained information on land cover and land use from the trost recent New
Bnmswick Forest Developirent Survey, supplied by the NBDNR We c1assified polygons to simplify the interpretation of land-use/land-cover patterns. In totai six land-use variables were calculated (sunnnarized in Table 2-3). We derived each variable by intersecting the c1assified land-use/land-cover Jayer with the delineated sub catchment.
To obtain a standardized percentage for each variable, we summed the area of polygons for each c1ass contained within the sub catchment and divided by sub-catchment area.
Data Analysis
Unless otherwise descnbed, we perforired all analyses using fimctio ns avai1ab le in the stats and vegan packages in the statistical computing program R, version 2.9.2 (R
Developirent Core Team, 2009).
33 Assemblage Overlap: We assessed the degree of overlap in taxonomic composition between rifiles and riverine wet1and areas by non-metric muhidimensional scaling
(NMDS) based on the Bray-Curtis dissimilarity index. We selected this index because of its wide application in connnunity ecology and the greater weight it places on shared taxa; hence, it is a more conservative measure of assemblage differences between a pair of samples. We performed NMDS with the metaMDS function, and used muhiple random starts to increase the likelihood of a stable solution There were considerable difrerences in abundance within taxa, so the invertebrate data were Wisconsin standardized (species are first divided by their maxima, then sites are standardized to unit totals) prior to computing dissimi1arities (Bray and Curtis, 1957).
Assemblage variation {Beta diversity): While the beta diversity of a set of samples can be calcu1ated by averaging the pair-wise dissimilarity between samples, a major drawback is the lack of independence anxmg pairs of samples. Another method of measuring the beta diversity of a set of samples is to measure average distance between each sample and its group centroid in principal coordinate space. Anderson (2006) proposed a test for homogeneity of multivariate dispersions (H1vfVDISP) utilizing this concept. While the test is based on Euclidean distances, non-Euclidean distance measures can be used if observations are first placed into Euclidean space using Principal Coordinates Analysis
(Gower, 1967) and negative eigenvalues corrected by adding a constant value. Here we apply their approach to ask whether variation in composition for samples from riverine wetlands is greater than that observed in rifile habitats across the catchrrent, based on
Wisconsin standardized Bray-Curtis dissimilarities.
34 Corre1ation between habitats in taxon richness and assemb1age variation: We calcu1ated
Pearson Product-Moment corre1ations between riffle and riverine wet1and samples based
on both raw counts of taxa observed and a non-parametric estimator of taxon richness
(Chao 1; Chao, 1995) that uses a probabilistic framework to estimate the number of
unseen species in a sample. The use of an estimator allows us to account for differences
in abundance arrong samples. For assemb1age variation, we calcu1ated corre1ations
between habitats using the distance to centroid values obtained from the principal
coordinate analysis described above.
Environmental interpretation of composition: Detrended Correspondence Analysis of the
connnmity datasets indicated that the gradient lengths for both riffle and riverine wet1and
samples were less than three, indicating that Principal Components Analysis (PCA) was a
suitable method for corrnnunity ordination (Leps and Smilauer, 2003). We performed
PCA separately on the riffle and riverine wet1and datasets and fitted environmental
vectors to the ordinations using the fimction envfit in order to explore potential
environmental gradients influencing composition Prior to PCA, we applied the Hellinger
transformation to the corrnnunity datasets, an approach that offers a good compromise between linearity and resolution (Legendre and Gallagher, 2001). We arcsine-square root transformed environmental variables measured as proportions or percentages, and we
applied a log10 transformation to catchment area, discharge (Q), wetted width (WW) and bankfull width (BW) prior to fitting to ensure normality. The significance of corre1ations
between individual environmental variables and the ordination was assessed by randomly
35 permuting the data 999 times for each variable, with the significance level set to a=0.05.
If we obtained an equal or greater squared correlation coefficient in greater than or equal to 5 percent of the randomizations, we considered the corre1ation between the variable
and the ordination to be statistically insignificant.
Results
Table 2-4 summarizes observed patterns oftaxa richness in the Nashwaak
catchment. We observed a total of 75 insect families and 15 rxm-insect invertebrate
orders across all sites and habitats. The total number oftaxa observed in riffles and riverine wet1and areas was virtually identical, as was the number of taxa tmique to each habitat type. At the site levei however, riffles appeared to contain a greater average number oftaxa (Table 2-4; two-tailed paired-sample t-test, t=S.66, d.f =16, p<0.0001)1•
Taxa accmnu1ation curves constructed based on samples (standardized to equal abundance) do not reach an asymptote for either habitat, suggesting that rmny taxa occurring in the catchtrent were not collected during sampling (Figure 2-2). The greater rate of increase for the riverine wetland curve suggests that while these areas are less diverse at a site scale, there is greater variation in composition among sites.
1 See Errata
36 Assemblage over1ap: The NMDS analysis revealed a clear difference in composition between the riffles and riverine wet1ands (Figure 2-3), with riffles grouping closely together compared to riverine wet1ands. However, the difference in composition was not absolute, as riverine wetland samples from some sites (BLK, NA3) appeared to group more closely with the riffle samples. The ordination produced a moderate 2-D stress value (19.6), suggesting that the rank order correlation between the original dissimilarities and plotted distances is re1atively strong.
Assemblage variation {Beta diversity): The test for homogeneity of rrrultivariate dispersions indicated that the average distance to centroid value was significantly greater for samples from riverine wetlands than samples from riffle habitats (average distance to centroid values: riffles, 0.2651, riverine wetlands, 0.4216; F=36.187, d.£ =1, 33, p=0.001). Hence, samples from riverine wetlands demonstrated a greater magnitude of assemblage variation than riffle samples.
Corre1ation between habitats in taxon richness and assemblage variation: We did not observe a significant corre1ation between habitats in te~ of observed taxa riclmess
(correlation= -0.18, t= -0.72,d.£=15, p=0.48) or the non-parametric riclmess estimator
Chaol (correlation = -0.19, t = -0.76, d.£=15, p=0.46). Simi1arly, the distance to centroid values for riffle and riverine wet1and samples were tm.corre1ated across sites (corre1ation
= 0.14, d.£ =15, t=0.54, p=0.60).
37 Environmental interpretation of composition patterns: Figure 2-4(a) gives standard PCA biplots for riffies and riverine wetlands. Arrows are best-fit lines representing the strength of corre1ation (arrow length) between a variable and the ordination, and point in the direction of most rapid change in that variable. Log catchment area demonstrated the strongest corre1ation with the ordination of sites (r2=0.72, p=0.001). Several re1ated variables (stream order, mean and max. depth, canopy coverage and conductivity) also demonstrated significant corre1ations with the ordination Turbidity and total phosphorous (TP) demonstrated weak but significant corre1ations (turbidity, r2=0.47, p=0.013; TP, r2=0.42, p=0.021). We observed a weak corre1ation with percent settlement
(r2=0.37, p=0.04) though this is likely due to the higher concentration of human settlement in the lower catchment.
None of the measured variables dennnstrated a statistically significant corre1ation with the ordination of riverine wet1and samples; however, depth was only marginally insignificant (r2=0.32, p=0.053) and is included in the biplot for illustration (Figure 2-4b).
A post-hoc analysis found that riverine wet1ands do appear to demonstrate greater variation in water chemistry than main chamel sites (average distance to centroid values: riffies = 22.67, riverine wet1ands = 45.71, p=0.003). However, this difference Jargely appears to be driven by three sites (NAP, NA7, and PEN). When. we remove these sites from the analysis, a difference in the homogeneity of dispersions of water chemistry is not detectable.
38 Discussion
Despite p1aying a potentially important role in the ecosystem dynamics of small watersheds and headwater strearm, riverine wetlands and their biodiversity patterns relative to main-channel areas are poorly studied. In soire cases, conservation is proceeding based on biodiversity information derived from a limited range of river habitats, particularly where bi01ronitoring data are the source of that information (e.g.
Aroviita et al, 2010). Riffles are often a target of biomonitoring programs because they are a predictable habitat feature of most streams and rivers. In contrast, riverine wetlands share few defining features aside from Jack of flow during at least part of the year, and are more challenging to sample consistently.
We sought to document the composition ofmacroinvertebrate communities in riverine wetlands and compare their biodiversity patterns to proximate rifi1e habitats. The rarefaction analysis suggests differences in the scale at which the habitats contribute to catchment biodiversity. Riverine wet1and samples had similar observed and estimated taxon richness to riffles (Table 2-4) and have a steeper rarefaction curve (Figure 2-2), indicating that finther sampling could reveal greater taxa richness in riverine wet1ands.
Conversely, riffles appear to contribute strongly to site-scale taxon richness. This implies that there is greater assemblage variation in riverine wetlands, an observation confirmed by the test for homogeneity of multivariate dispersions; average distance to centroid values were nruch greater for samples collected from riverine wet1ands.
39 In addition to differences in tax.on richness between rifile and riverine wetlands, our
NMDS analysis revealed pronomced differences in composition (Figure 2-3). Insects were approximately four times more abmdant in rifile habitat and accounted for >95% of the individuals collected in those areas. In contrast, non-insect taxa were approximately three titres more abmdant in riverine wetland areas. Insects still accomted for aromd
66.7% of individuals collected from riverine wetlands, though this number is only 21.8% when we exclude Chironomidae. Non-insect taxa that were frequently encountered in riverine wetland areas include several crustacean groups (Cladocera, Amphipoda,
Copepoda, and Ostracoda), and two classes of molluscs (Bivalvia: Sphaeriidae, and
Gastropoda). Chironomidae were the most abundant insect fumily in either habitat, and higher resolution taxonomy in this group could potentially aher the observed degree of assemblage overlap. Many non-chironomid insect taxa (e.g. Glossosomatidae,
Heptageniidae) observed in both habitat types occurred only sporadically and at far lower abmdances in riverine wetland samples, possibly because larvae of these taxa are transported in the drift during episodic flow events, and settle in riverine wetland areas while connected to flow from the main channel (Brittain and Eikeland 1988). The riverine wetland macroinvertebrate corrnnmity from samples at certain sites (NA3, BLA) grouped closely with riflle samples in the NMDS analysis (Figure 2-3), suggesting a more frequent connection with the main channel at those sites. It is likely that riverine wetlands exhibit a gradient in hydrologic connectivity with the main channel across the catchment.
40 Our HMVDISP analysis confirmed far greater variation in the composition of macroinvertebrate connmm.ities in riverine wetlands. One exp1anation for this pattern could be catclnnent-scale habitat variability. The riverine wetland areas we sampled had diverse origins and therefore reflected a range of depths, substrate types and water chemistry. Water chemistry did appear to be more variable in riverine wetlands, but water chemistry does not appear to be driving connmm.ity structure as these variables were not correlated with the ordination of samples from either the riffle or riverine wetland samples, save for a weak correlation with turbidity and total phosphorus in riffles (Figure
2-4a). Comparisons of physical habitat complexity in riffles and riverine wetlands are difficult; riffle and main-channe 1 habitats in the N ashwaak are dominated by cobble/boulder substrates, whereas riverine wetland areas often contain large amcnmts of fine organic/inorganic seditrents, coarse woody debris and macrophytes.
Neither taxon richness nor assemblage variation was correlated between riffle and riverine wetlands. 1here are several possible explamtions for the Jack of congruence.
First, the relatively small spatial grain and extent of this study may ~an that there is re1atively low local variation in taxa richness and composition for riffies, as all sites draw upon a simi1ar regional species pool If riffles demonstrate a small gradient in richness, then detecting a corre1ation with riverine wetland areas could be more difficult, especially considering noise associated with incomplete sampling oflocal connmm.ities. Similarly, we may have had difficulty detecting corre1ations in composition between riffle and riverine wetland habitat because ofre1atively low assemb1age variation in riffles. If we expanded the extent of the study to include a wider range of river types, it is possible that
41 corre1ations in taxon richness or assemb1age variation among habitats would become more apparent. Other studies ofbenthic invertebrates in New Bnmswick rivers have revealed re1atively small differences in community composition among riffles (Anmnini et al, unpublished), suggesting that regional or greater scales could be necessary to establish a Jarger gradient in composition
Additionally, macroinvertebrate comrmmities in riffles and riverine wetlands may respond to different environmental gradients, leading to weak corre1ation in assemb1age variation An exploratory analysis of taxa-environment re1ationships suggests different gradients structure riffle and riverine wet1and comrmmities. Catclnnent area, or factors re1ating to it, appeared to be most important for rifile cormmmities (Figure 2-4a). The role of catclnnent area in determining discharge and flow regime and their effects on benthic communities is well established (Naitmn et al, 1987, Rice et al, 2001), so it is not surprising to observe longitudinal patterns in rifile composition Catclnnent area did not corre1ate with the ordination of samples from riverine wetlands; instead, depth appeared to be the most important factor influencing composition, though this re1ationship was weak (Figure 2-4b).
These patterns raise additional questions regarding the source of colonists for riverine wet1ands. If main-channel habitats were the primary source of colonists, then one would expect riverine wet1ands to be both compositionally simi1ar and structured by simi1ar environmental gradients. Given the low level of assemb1age over1ap observed in our study, and the possibility of differing environmental controls on composition, colonists
42 from the main chmmel likely have a limited impact on the composition of riverine wet1and macroinvertebrate connmmities. The source of colonists to riverine wet1ands cannot be confirmed without direct surveys of the drift or oviposition by adult insects; however, riverine wet1ands are likely of particu1ar importance to certain groups and species. For instance, Jarvae of many odonate species occur primarily in lentic habitats
(Corbet, 1999). Given their relatively low nurreric abundance in the Jandscape, the utilization of riverine wetland areas may be important to the metapopu1ation dynamics of particular odonate species, either as sink or source habitats.
This study was not exhaustive of habitat types present in smaller catchments such as the N ashwaak River. Lakes, ponds, and headwater wetlands are conspicoous features of this landscape, but we did not survey them in the present study. The inclusion of these habitats in fi.nther surveys could aid in identifying the source of colonists for riverine wetlands, as well as taxa that may be wholly reliant on riverine wet1ands. Capturing temporal variability in diversity is also an important aspect of biodiversity assessment
(Magurran, 2004 ); our survey did not include temporal replication, thus temporal changes in the composition of riffle and riverine wetland communities could influence our results.
Greater temporal variation in riffle comrm.m.ities could compensate for their lower spatial variability, resulting in levels of spatiotemporal variability similar to riverine wetland areas.
Conservation Implications
43 Biom:mitoring data have been suggested as a cost effective and readily available source of biodiversity infonnation in freshwaters (Linke and Norris, 2003). Such studies often employ a reference condition approach (Reynoldson et al, 1997). For this approach to be successfui the impact of a stressor on the benthic community must be iso1ated from noise associated with assemb1age variation lbere are two issues here; first, samples must be located in areas with environmental conditions simi1ar to the test site. Second, monitoring requires a habitat feature that is repeated throughout the Jandscape, and demonstrates re1atively little variation in composition Riffles have served very well for this purpose in many temperate river ecosystems. However, they also present two potential conflicts with the task of biodiversity assessirent for conservation purposes. Ideally, an inventory of reference sites encompassing the range of environmental conditions and microhabitats in a region should be established. In practice, limited resources dictate that reference sites are located in areas simi1ar to those where we evaluate the impact of stressors, and focus is often paid to a single habitat type (ie. riffles). While there are exceptions to this (e.g.
Linke et al, 2007, Turak and Koop, 2008), the sampling needs of biomonitoring and biodiversity assessirent are soiretimes misaligned.
Taken together, our observations indicate that riffles and riverine wet1ands support distinct macroinvertebrate assemb1ages (Figw'e 2-3), and results of the HMVDISP analysis suggest that riverine wetlands demonstrate greater assemb1age variation than riffles. When subjected to reserve selection criteria based on compleire ntarity or assemb1age variation, using riffles could resuh in the selection of a smaller network of sites to conserve a given level of regional richness. This network would be less extensive
44 than one developed based on riverine wet1ands. Second, riverine wetlands and riffles do not corre1ate in tenilS of their richness or assemb1age variation (ie. '\mique" riffle and riverine wet1and corrnnmities do not necessarily occur at the same site). Hence, even if the magnitude of their assemb1age variation were simi1ar, a minimum network based on riflle information may differ considerably from a minimum network based on riverine wet1ands.
Our knowledge of ecological processes in riverine wetlands and other s1ackwater habitats remains vague. A first step to c1assifying riverine wetlands is to produce accurate maps of their location in the catchment and re1ate their occurrence to catchment geomorphology.
While we can identify Jarger riverine wet1and water bodies from topographic maps, aerial photography, or remote sensing, smaller patches of habitat remain elusive, although recent advances are providing finer resolution information (Mertes, 2002). Accurately mapping the diversity of river habitats themselves would provide conservation practitioners and ecologists with a valuable tool for understanding and exp1aining their biotic composition
Acknowledgements
This research was fimded by an NSERC Discovery grant to D.J. Baird. 1be Environment
Canada Science Horizons Internship Program and a Graduate Research Assistantship from the University of New Bnmswick supported C.J. Curry. Nick Hawkins and Antonis
45 Gazeas provided assistance during fieldwork, and Lisa Baird provided invaluable support with invertebrate sorting in the 1aboratory.
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51 Table 2-1: Rifl1e environmental variables measured for exploratory analysis of species environment relationships2 · Category Variables measured Units Method Mean, Range (min, max" Riverine vegetation Grasses (GR) Presence/absence Visually assessed 0,1 Shrubs (SH) 0,1 Coniferous Trees (CO) 0,1 Deciduous Trees (DE) 0,1 Substrate composition Bedrock (BR) Presence/absence Visually assessed 0,1 Fines (<4mm, FS) 0,1 Pebbles ( <6.5 cm, PE) 0,1 Cobbles ( <25 cm, CB) 0,1 Boulders (>25 cm, BO) 0,1 VI N Debris (DB) 0,1 Leaf Packs (LP) 0,1 Wood(WO) 0,1 subIC Count Stnn of presence records 4.9 (3,6) Hydrologic variables Discharge (Q) ~Is 1.28 (0.01, 5.1) Wetted Width (WW) m Depth and velocity at 20.1 (3.1, 75) Bankfull Width (BW) m 2/3 depth measured at 27.4 (7.1, 100) Maximum velocity (Vmax) mis 10 locations along a 0.84 (0.33,1.17) Average velocity (Vavg) mis transect with a Marsh- 0.52 (0.13, 0.85) Maximum depth (Drnax) m McBirney flow meter. 0.31 (0.06, 0.48) Average depth (Davg) m 0.22 (0.03,0.37)
2 See errata % Riflle/Run/Poo 1 % Riflle Proportion Visually estitmted 0.41 (0.10, 0.90) %Run 0.30 (0, 0.80) %Pool 0.29 (0, 0. 70) % Macrophyte Cover ICmp Proportion Visually estitmted 0.02 (0, 0.10) % Canopy Closure ICca Proportion Handheld densiorreter 0.25 (0, 0.87) Water Chemistry Temperature (1) oc YSI 556 handheld 16.6 (10.5, 23.9) Dissolved 02 (%Sat) Percent saturation multiprobe 101 {0.86, 1.04) Dissolved 02 (DO) mglL 9.89 (8.15, 11.38) Conductivity (Cond) µSiem 33.9 (14, 62) pH (pH) [H+] 7.22 (6.55, 7.66) Cl mglL Grab sample 1.81 (0.5, 11.9) S04 mglL 2.7 (1.8, 3.5) N03 mglL 0.03 (<0.02, 0.08) NH4 mglL 0.05 (<0.01, 0.11) wUl P04 mglL 0.002 (<0.001, 0.006) Mg mglL 0.69 (0.39, 0.97) K mglL 0.34 (0.26, 0.41) Na mglL 2.04 (1.05, 7 .21) Ca mglL 4. 76 (1.26, 11.55) TOC mglL 7.6 (3.3, 11.6) DOC mglL 5.4 (3.3, 9.6) 1N mglL (<0.02, 0.2) 1P mglL 0.02 (0.006, 0.031) Alkalinity mglL 13.21 (4.41, 29.89) Hardness mglL 14.75 (4.96, 32.09) Turbidity NTU 0.4 (0.1, 0.8) Colour Hazen units 41 (16, 72) Table 2-2: Riverine wet1and enviromrental variables treasured for exploratory ana.Jysis of species-enviromrent re1ationships3 Category Variables treasured Units Method Mean, Range (min, max) Riverine vegetation Grasses ( GR) Presence/absence Visually assessed 0,1 Shrubs (SH) 0,1 Coniferous Trees (CO) 0,1 Deciduous Trees (DE) 0,1 Substrate composition Bedrock (BR) Presence/absence Visually assessed 0,1 Fine sediment (<4mm, 0,1 FS) 0,1 Pebbles ( <6.5 cm, PE) 0,1 Cobbles (<25 cm, CB) 0,1 V. Boulders (>25 cm, BO) 0,1 ~ Debris (DB) 0,1 Leaf Packs (LP) 0,1 Wood(WO) Cmmt Sum of presence 4.5 (2,7) SubOC records Riverine wet1and Length (OCx) m Measuring tape/treter 21.7 (4, 50) dimensions Width (OCy) m stick 8.5 (1, 20.6) Depth (OCz) m 0. 73 (0.25, 1.6) % Macrophyte Cover OCmp Percentage Visually estitmted 0.07 (0, 0.5) % Canopy Closure OCca Percentage Handheld densiotreter 0.40 (0, 1) Water Chemistry Temperature (1) oc YSI 556 handheld 16.4 (10.4, 28.3) Dissolved 02 (%Sat) Percent saturation multiprobe 0.72 (0.14, 1.12) Dissolved 02 (DO) mgL 7.17 (1.22, 11.3)
3 See errata Conductivity µSiem 50.0 (17.7, 118.1) pH (pH) log [H+] 7.06 (6.33, 7.53) Cl l11lefL Grab sample 2.7(0.4, 18.1) S04 l11lefL 2.3 (1, 3.8) N03 l11lefL 0.02 (<0.02, 0.07) NH4 l11lefL 0.10 (<0.05, 0.29) PQ4 l11lefL 0.007 (<0.001, 0.06) Mg l11lefL 0.83 (0.38, 1.24) K l11lefL 0.46 (0.23, 1.16) Na l11lefL 2.63 (1.21, 11.91) Ca l11lefL 5.42 (1.17, 10.03) TOC l11lefL 7.26 (2, 18.4) DOC l11lefL 4.9 (2.2, 10.9) 1N l11lefL 0.17 (0.02, 0.62) TP l11lefL 0.036 (0.01, 0.094) V'I V'I Alkalinity l11lefL 16.46 (3.38, 29.21) Hardness l11lefL 16.96 (4.82, 28.05) Turbidity NTIJ 2.03 (0.2, 14. 7) Colour, Hazen units 47 (11, 193) Table 2-3: Landscape and land-use variables measured for exploratory analysis of species-environment re1ationships4 Variable Units Method Mean, Range (min, max) Catchment Area (WA) km2 Catchment Area was calcuJated after delineating 253.5 (4.4, 1445.1) the upstream catchment for each site using ArcGIS 9.2 Watershed Delineation toolbox Stream Order (SO) Stream order assessed using the Strahler method 4 (2,5) based on a Jayer provided by the NBDNR Catchment Slope (Sip) Percentage Average slope of catchment 0.06 (0.03, 0.08) Elevation (Ev) Meters Obtained from the SR1M-D1M 134 (17, 310) Distance to confluence (Deon) Kilometers Distance between sites and the confluence of the 58 (15, 116) catchment was calculated using ArcGIS 9 .2 Network Analyst
VI Agriculture and human Percentage Percentage of catchment area with land use 0.02 (0, 0.05) °' settlement (Hu%) defined asagriculture orsettled Historic forestry (Fh%) Percentage Percentage of catchment area subject to any 0.58 (0.34, 0.76) forestry activity Recent forestry (Fr°/o) Percentage Percentage of catchment area subject to any 0.44 (0.09, 0.73) forestry activity in the preceding 3 0 years Thinning,'Se lecti ve Cutting Percentage Percentage of catchment area subject to thinning 0.25 (0.15, 0.41) (Fl%) or selective cutting activity PJanting (F2%) Percentage Percentage of catchment area designated as 0.09 (0, 0.30) p1antation forest Clearing (F3%) Percentage Percentage of catchment area subject to forest 0.14 (0.02, 0.33) clearing or clear cutting. Conifer cover (Co%) Percentage Percentage of catchment area where prirrmy 0.49 (0.17, 0.83) vegetation coverage is coniferous
4 See errata Table 2-4: Average taxon richness per site across habitats and non-pa.rarrietric estimates of overall richness based on incidence data. Habitat Average (± s.d.) Totabt,s Uniqueobs Chaol (±se) Riffle 32 (5.1) 69 21 85.3 (11. 7) Riverine wet1and 20.3 (7.0) 68 22 91.1 (14.8) Combined 41.5 (5.5) 90 43 130 (26.1)
57 ,'"';,, "--('·<~ \a~ ':,~(•""'.l;
c; ~, ; ., -;J. t :;t\,::\,} J' )! Figure 2-1: Sampling locations in the N ashwaak River catchment, New Bnmswick, Canada. 58 0 LO "'O (1) 0 C: -.:t" Q) tn ..0 0 co X 0 -m M 0 N riffle off-channel 0 2 4 6 8 10 12 14 18 samples Figure 2-2: Sample-based rarefaction curves for riflles (black) and riverine wetlands (grey) 59 OUN., -0 stress= 16.33 WIL.- 0 NAP.- 0 NA6.- 0 TA2 O WIL• • MAN 0 NA? N BLA .- e Roe_ e NA3 0 Cl) NA6e -5MI NA5 e-:- 0 NA5 0 NA1 J;U)c_o :E DUNr A'f PE"' eBLA 0 .- .-~. z •CRO •. eNAP• .- ·'Nc3r~ PEN 0 NA7 0 NA2 eNA1 .- .- NA2 0 MAN LO .- 0 • I CRO 0 .- TA1.- 0 • r iffle • off-channel SM.I- 0 I I I I -0.5 0.0 0.5 1.0 NMDS 1 Figure 2-3: 2-D Non-metric multidimensional scaling plot based on Bray-Curtis dissimilarities. Riffle samples, black; riverine wetland samples, grey. 60 (a) : ROC co l • 0 MAN• "1: PEN 0 e e OUN Turbidity 5MI N • 0 : NAP WIL• -it1 •• ~ 0 (.) ci ··················111.:A·········································· a. • qN NA7• NA5 CRO NA4.• • NA3 "1: • q co q TA2• -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 PCA1 (b) ~ WIL• CRO• U') ci ;NAP : ROC . TA2 • • :NA1 : e N~LA : NA3e• 0 ...... ···111J112·········pEN···················· 0 ~ e e NA6 NA7• • 5Ml TA1 • • DUN • o+ui ....C!. -1.0 -0.5 0.0 0.5 PCA1 Figure 2-4: Principal components ordination of the macroinvertebrate community for (a) riffle samples and (b) riverine wetland samples. Arrows represent the strength and direction of significantly correlated environmental variables. 61 3. Congruence of biodiversity measures among larval dragonflies and caddisflies from three Canadian rivers Curry, C.J., Zhou, X. & Baird, D.J. (2012) Congruence of biodiversity measures among 1arval dragonflies and caddisflies from three Canadian rivers. Freshwater Biology, 57, 628-639. Summary 1. Scientists tasked with collecting taxon richness and assemb1age variation data for conservation purposes have identified biomonitoring studies as a potential source of information This approach assUires that biodiversity patterns revealed by biom:mitoring reflect those of the wider comrmm.ity, an assmnption not thoroughly tested in riverine ecosystems. 2. We compared patterns of assemb1age variation and taxon richness in an important biomonitoring group (Trichoptera) to a group with high conservation significance (Odonata) at 34 sites across three 5th order catchments. We also explored the effect of abundance on observed patterns by rarefying the larval Trichoptera dataset. 3. Our results indicate that Trichoptera do not fully reflect site-scale taxon richness or assemb1age variation in Odonata. The magnitude of odonate assemb1age variation was much greater than Trichoptera for one of the catchments. Odonata and Trichoptera richness was moderately correlated in two catchments, while assemblage variation was 62 strongly corre1ated in another pair of catchtrents. However, comparisons based on rarefied data eliminated differences in the magnitude of assemb1age variation and strengthened corre1ations in richness and assemb1age variation, suggesting the Jack of congruence in these measures might be due to differences in ablllldance ammg groups. Further, incomplete taxonomy may mask additional assemb1age variation, particularly in Trichoptera. 4. Conservation planning in riverine ecosystems based on proxies derived from biomonitoring data should proceed cautiously lllltil we understand how well the resulting information reflects biodiversity patterns in undersampled taxa and habitats. Future studies of biodiversity congruence should consider both richness and assemb1age variation as each provides valuable information for conservation re1ated decisions. The taxonomic resolution and re1ative abundance of comparison groups can potentially impact the strength, direction and statistical significance of patterns. Researchers should employ species-level taxonomy and account for differences in ablllldance among groups through rarefaction where at all possible and DNA-based taxonomy methods can support this. Keywords: rarefaction, beta diversity, congruence, Odonata, Trichoptera lntroductio n 63 Recent reviews have postu1ated extensive biodiversity declines in freshwater ecosystems while highlighting the nascent state of freshwater conservation science (N el et al, 2009; Strayer and Dudgeon, 2010). This has stimulated discussion regarding the use of sIDTogates in biodiversity assessment and how to identify these in river ecosystems (Heino et al, 2009). Developing effective conservation strategies for any group of organisms requires an accurate description of their biodiversity patterns (alpha and beta diversities) and the processes that drive them Beta diversity integrates between local and regional patterns of species riclmess (Kolefr: Gaston & Lennon, 2003), though the term is used to descnbe several interrelated characteristics of biotic assemblages (Jurasinskt Retzer & Beierkuhnlein, 2009, Tuomisto, 2010). Loss of beta diversity, or biotic homogenization, has become a major concern for freshwater ecosystems (Lockwood and McKinney, 2001; Rahei 2002; Olden and Rooney, 2006). A useful measure of beta diversity is variability in taxonomic composition among sampling units, or assemblage variation (Anderson, Ellingsen & McArdle 2006). While evidence-based evaluation of alpha and beta diversities should ideally support conservation plarming, resources available for biodiversity assessment are limited. In addition to documenting and diagnosing biodiversity losses (Voshell et al, 1997), scientists must adequately assess landscape and regional-scale patterns of biodiversity for conservation purposes (Margules and Austin, 1991; Groves et al, 2002). These two types of information may not be mutually exclusive. For instance, Linke and Norris (2003) proposed a two-tiered assessment for freshwater ecosystems where site condition is first evaluated using a standard biomonitoring approach such as the Australian River 64 Assessment System (AUSRIVAS, Simpson and Norris, 2000). In the second tier of the assessment, those sites exhibiting a decrease in biodiversity re1ative to reference conditions are flagged for restoration and sites considered 'unimpacted' are prioritized for conservation based on the presence of less common taxa, their complementarity to other sites, and their vulnerability to future impacts (Linke et al, 2007). Several assumptions regarding the properties ofbiomonitoring data are inherent in the two-tiered approach and others where limited biodiversity infonnation is available for conservation p1anning (van Jaarsveld et al, 1998; Margules, Pressey & Williams 2002). First, one asstnnes that the scale at which biomonitoring data are collected is relevant to the scale of conservation efforts. Second, as biomonitoring regimes often focus sampling on single microhabitats, their biodiversity is assumed reflective of unsampled microhabitats. Finally, there is an assumption that patterns of biodiversity in the subset of taxa sampled by biomonitoring programs are reflective of the wider community, especially important groups that may be missed or underrepresented during sampling (Fleishman et al, 2005; Rodrigues and Brooks, 2007). This is a major issue for invertebrates and insects; they accmmt for 85% and 60% of all species recorded in freshwater systems (Balian et al, 2008), and thus are increasingly the focus of conservation and monitoring efforts (Strayer, 2006). It is important to detennine the extent to which these assumptions are satisfied, as conservation decisions will affect habitats and taxa not covered by biomonitoring samples (Bihon et al, 2006). Moreover, as the two-tiered approach focuses on species 65 richness patterns, it is llllClear to what extent conservation decisions will reflect other aspects of biodiversity such as assemb1age variation, especially within the context of a reference condition approach. Hence, it is important to consider both richness and assemb1age variation in assessirents of smrogacy or biodiversity concordance (Su et al, 2004; Heino et al, 2009). Freshwater biomonitoring prograim putatively aim to sample the complete macroinvertebrate connrrunity, but certain taxa are invariably mcomrmn or absent in samples because of micro habitat and/or irethodological bias. If two or more subsets of the corrnmmity differ considerably in their patterns of biodiversity, then different conservation strategies may be necessary to achieve protection of the whole. Scientists will rarely have the opportunity to adequately sample every macroinvertebrate subgroup within the wider commmity on a regu]ar basis, so establishing the extent of cross-taxon congruence in biodiversity patterns is crucial to fomrulating comprehensive conservation strategies. Cross-taxon congruence of riclmess in major freshwater taxa has received re1atively little attention (Heino et al, 2003). The resuhs of existing studies are inconsistent. Several have fotmd weak congruence in species riclmess among taxonomic groups (Allen et al, 1999; Paavo1a et al, 2003; Heino et al, 2005; Heino et al, 2009), while Bilton et al (2006) fotmd that the strength of congruence among taxa was dependent on the region and groupings considered. Other studies (Briers and Biggs, 2003; Sanchez-Fernandez et al, 2006) observed strong congruence in richness amongst major groups of aquatic insects, and Heino (2002) fomd general congruence in species richness among macrophytes, dragonflies, aquatic beetles and fishes in northern Europe. 66 To our knowledge, congruence in assemblage variation and its magnitude across taxa has not been studied in freshwater systems. However, investigations of cross-taxon congruence in assemblage simi1arity, a related aspect of beta diversity, have produced conflicting patterns. Bihon et al (2006) report strong correlations in conununity simi1arity among pond taxa, while Grenouillet et al (2008) found fish assemblage simi1arity was correlated both with macroinvertebrate and diatom assemblage similarity, yet macroinvertebrate and diatom assemblage similarity were not significantly associated. Conversely, Heino et al (2003, 2009) found weak correlations in assemblage similarity amongst several invertebrate taxonomic groups. Hence, finther research is required to llllderstand the scales, habitats and taxonomic groups where patterns of species richness and assemb1age variation are congruent. 1bis research is part of a Jarger program aimed at developing methods for the assessment of freshwater invertebrate biodiversity. Here we address whether the third assumption descnbed above, congruence of biodiversity among taxonomic groups, holds for two conspicuous groups of aquatic insects - dragonflies and damselflies (Insecta: Odonata) and caddisflies (Insecta: Trichoptera). We considered the following questions: (i) Are Trichoptera and Odonata taxon richness correlated at the site scale? (it) Does the magnitude of assemblage variation differ between Trichoptera and Odonata, and is assemblage variation in these groups correlated across the catchrrent? (iii) To what extent do differences in abundance between groups influence observed magnitude of assemblage variation and cross-taxon congruence of biodiversity measures? While the 67 literature surrounding freshwater invertebrate biodiversity patterns is growing, these studies have yet to account for differences in abundance when comparing assemblage variation among groups. Understanding the biological factors that detennine the strength of cross-taxon congruence in richness and assemblage variation is equally if not more important than quantifying these patterns. Recent studies have hypothesized that dispersal limitation contributes to greater spatial autocorrelation (Landeiro et al, 2011) and stronger distance decay relationships (Brown and Swan, 2010) in freshwater organism;. Given their longer adult life stage and stronger flying abilities, one might expect Odonata to demonstrate relatively low assemblage variation due to weaker dispersal limitation Ftnther, if Odonata demonstrate relatively low assemblage variation, then it may be difficult to discern correlations in assemblage variation with Trichoptera. Methods Study Organisms We chose to focus on dragonflies and drumelflies (Odonata) and caddisfl:ies (Trichoptera) because of the relatively high species richness of each group, their contrasting dispersal abilit:ie s, and their prevalence in riverine habitats in eastern North America. We measured patterns of larval biodiversity in each group because this life stage is the focus ofbiomonitoring studies, the same methods could be used to sample 68 each group and the presence of Jarvae is trore indicative of reproductive success m a location Odonates are a charisimtic group of insects with high conservation significance, nominated both as potential indicators of ecological impairment and as an umbrella group for freshwater biodiversity conservation (C1ausnitz.er, 2003; Bried, Herimn & Ervin, 2007; Simaika and Samways, 2009), though they generally occm in low numbers in biotronitoring samples. They are predators during both the Jarval and adult stages and ID1dergo hemimetabolous development. Aquatic Jarvae occm in flowing and standing water habitats, and are univo hine or semivo hine at cool temperate Jatitudes, though partivohinism is also possible (Corbet, 1999; Corbet, Suhling, & Soendgerath 2006). The adult life stage of rmst odonate species ranges from about one week to over one tronth, typically emerging from mid-May to early September. Musemn records exist for at least 130 species in New Bnmswick and for counties over1apping the catchments studied here. Trichoptera are among the richest and trost ecologically diverse orders of aquatic insects, occupying a range of trophic niches including herbivory, carnivory and detritivory. They occm at high densities across a range of freshwater microhabitats and in benthic samples collected for biormnitoring programs such as Environment Canada's Canadian Aquatic Bi01mnitoring Network (CABIN). They are holometabolous and generally univohine at the Jatitude covered by this study, with an adult life stage Jasting well llllder one tronth in trost species (Wiggins, 1996). Trichoptera are considered key indicator taxa because they detronstrate a wide range of tolerances to anthropogenic distlll'bance (Lenat 1993, Morse 69 et al 2007). A comprehensive list of species for New Bnmswick does not exist; however, a list for the adjacent state of Maine in the United States includes over 350 species (PEARL database, University of Maine, 2010). Study Areas We conducted surveys of larval Odonata and Trichoptera for three fifth-order catchments (sensu Strahler, 1952) in New Bnmswick, Canada. The Nashwaak River is a tnbutary of the Saint John River and drains an approximate area of 1708 km2 (Figure 3-1 ). The catchment lies within the Northern New Bnmswick Uplands and the Maritime Low1ands terrestrial ecoregions, with primary vegetation cover of mixed hardwood and softwood forest. There is significant forestry activity throughout the catchment, with some limited agricultural activity and human settlement in the lower reaches. We surveyed sixteen sites on the Nashwaak River between September 3 and October 20, 2007. The Renous and Little Southwest Miramichi rivers are tributaries of the Southwest and Northwest Miramichi rivers (Figure 3-1), draining areas of 1429 km2 and 1345 km2 respectively. Mixed hardwood/softwood forest covers both catchments, which span the Northern New Bnmswick Uplands and New Bnmswick Highlands ecoregions. There is significant forestry activity throughout each catchment, but there is little agricultural activity or human settlement. We surveyed eleven sites on the Renous River between July 17 and August 20, 2007, and seven sites on the Little Southwest Miramichi River between August 17 and September 9, 2008. We defined a site as a section of stream channel 70 approximately 50 meters in length, including any riparian water bodies within 20 meters of the main channel Larval Sampling Since 1arval Odonata and Trichoptera utiliz.e a variety of microhabitats, a multi-habitat sampling approach was necessary to characterize diversity for both groups. We collected three samples from main-channel (lotic) microhabitats and two from off-channel (lentic) microhabitats at each site. Main-channel samples included a kick net collected from a riftle, a kick/sweep sample collected from stream i:mrgins (e.g. exposed root masses, snags, macrophytes) and a sample collected from sediment/debris deposits using a modified pool skinnner that was rapidly passed through the top 3 centimeters of sediment for a distance of about 1 meter, then emptied into a bucket. We repeated the latter two samples in off-channel areas. To standardize effort across samples and sites, the duration of collection was 3 minutes, an effort comparable to standard CABIN biomonitoring samples which consist of a 3-minute-kick sample collected from a riflle microhabitat only (Environment Canada, 2008). Following collection, we rinsed samples through 1- nnn and 400-µm sieves and visually sorted the retained material to remove all Trichoptera and Odonata 1arvae. At least two people sorted each sample for at least 30 minutes; thus, sorting effort was no less than one person-hour per sample and could Jast up to four person hours. Material from each sample was processed in white, flat bottomed plastic sorting trays in small batches, and trays were exchanged between crew members 71 to double check sorting quality. We preserved all specimens in 95% ethanol and transferred them to the Jaboratory for imrphological identification To estimate sorting efficiency and taxonomic bias we preserved the residue from each sample at every fourth site. Lowest taxonomic level identifications for odonates were performed using the keys of Westfall and May (1996) for sub-order Zygoptera, and Needham, Westfall, & May (2000) for sub-order Anisoptera. We identified Trichoptera to genus level using the key of Wiggins (1996). Of the total individuals in a sample, we recovered an average of 94% and 88% of Odonata and Trichoptera Jarvae respectively, though sorting completeness ranged between 88% and 100% for Odonata and 72% and 100% for Trichoptera. Though biased toward Jarger instars, live sorting did not systematically bias the taxonomic composition of samples. In addition to morphological identification, we sent tissue samples from all Nashwaak and Renous odonate specimens to the Canadian Centre for DNA Barcoding, Biodiversity Institute of Ontario, University of Guelph, Canada for sequencing of the mitochondrial cytochrome c oxidase subunit I (COi) barcode region Primer sets LC01490/HC02198 (Fohner et al 1994) and LepFl/LepRl (Hebert et al 2004) were used to obtain barcodes, but where these failed, the mini-primers MLepFl and MLepRl (Hajibabei et al, 2006), paired with LepRl and LepF 1, respectively, were used. Extraction, amplification, sequencing and alignment followed standard DNA barcoding protocols (Ivanova et al, 2006). We obtained DNA barcodes for 1376 of the 1617 Odonata specimens submitted for analysis. Table 3-1 provides a sumrmry of taxonomic resolution in the Renous/Nashwaak Odonata datasets before and after DNA barcoding. Resolution 72 improved for 48.8% of individuals where we obtained DNA barcodes, and we were able to confirm species level identifications for 44.5% of individuals. Barcoding also flagged 20 specimens that we misidentified based on morphological characters. Taxa lists for each site and catchment are available in Appendix 15, and COi sequence data and associated voucher information are publicly available in the Barcode of Life Data System (BOLD Systems; http://www.barcodinglife.org): Assemb1age Variation ofNB Odonata (NBODO). COi sequences are also publicly available through GenBank under accession mnnbers GU712949-GU713115, HM:400540-HM400606, HM900483-HM900497 and JN419251 - JN420321. Adult Sampling A potential confounding factor in our analysis is the differing level of taxonomic resolution for the Trichoptera and Odonata datasets. One might expect caddisflies to demonstrate greater assemblage variation based on their relatively high species and ecological diversity; however, this variation could be masked by genus-level taxonomy. A separate light-trapping survey for adult Trichoptera conducted in Jlllle 2008 at five locations in the Miramichi catchment (Figure 3-1) provides insight into species-level assemblage patterns for many of the Trichoptera genera. lhese specimens were collected to develop a reference library of COi barcode sequences, so were identified to species 5 Listed asAppendix2 (Odonata) andAppendix3 (Trichoptera) in thethesis. 73 and sequenced following procedmes described above. CO I sequence data and associated voucher information are publicly available through BOLD System5: Caddisflies ofNew Bnmswick (NBCAD) and through GenBank tmder accession numbers JN657710- JN659403. Data analysis We perfonned separate analyses for each catchment due to differences in sampling period. We used packages stats and vegan in the statistical computing program R, version 2.10.1 (R Development Core Team, 2009) for all analyses. Since the number of individuals sampled can strongly influence measmes of species richness as well as beta diversity (Cao et al, 2002; Plotkin and Muller-Landau, 2002; Cardoso, Borges & Veech 2009), we rarefied the Trichoptera dataset to the same number of individuals sampled in the odonate dataset by randomly subsampling an equivalent number of Trichoptera for each site. We used this procedme to generate 99 rarefied Trichoptera datasets that we subsequently used to evaluate the influence of abtmdance on observed biodiversity patterns. Analyses were perfonned using both lowest taxonomic level and genus level data for Odonata. Concordance in taxon richness: We calcu1ated Pearson's product-moment corre1ation between the observed numbers of odonate and trichopteran taxa for each catchment. To evaluate the influence of abundance on the strength of corre1ation, we averaged the 74 number ofTrichoptera taxa observed for each site across the 99 rarefied datasets and compared these values to the Odonata dataset. Assemb1age variation: Following the approach of Anderson et al (2006), we assessed assemb1age variation by calculating the average dissimilarity of samples from their group centroid in principal coordinate space. Using this approach, we analysed differences in assemb1age variation using a distance-based test for homogeneity of nrultivariate dispersions (HMVDISP) (Anderson, 2006, Anderson et al, 2006). We used the S0rensen dissimilarity index because it emphasizes differences in assemb1age composition and has a straightforward interpretation We conducted the HMVDISP analysis using the betadisper fimction and assessed statistical significance using a pair-wise permutation of distance to centroid values atrong groups. We also compared Odona.ta and Trichoptera assemb1age variation for the 99 rarefied datasets for each catchment using the averaged distance to centroid value across all randomizations for each site. Concordance of assemb1age variation: To assess whether assemb1age variation in Odonata and Trichoptera was congruent, we calcu1ated the Pearson product-moment correlation coefficient between site-specific distance to centroid values obtained from the HMVDISP analysis above. For the rarefied dataset, we averaged the distance to centroid value for each site across the 99 randomizations and used these values to obtain a correlation coefficient. 75 Adult Trichoptera Survey: To evaluate the impact of species-leve 1 taxonomy on assemb1age variation, we perfonred the HMVDISP analysis based on the S0rensen dissimilarity index for both genus and species data. We also measured the corre1ation between genus and species richness, and genus and species distance to centroid values. Results Concordance of Jarval taxon richness: During the larval survey we collected 3 8 Odonata taxa over 1951 individuals and 49 Trichoptera taxa over 10,905 individuals. We observed an average of 8.4±4.5 odonate taxa per site, ranging between a minimum of one and a maximum of 18, and an average of 16.4±4.1 Trichoptera taxa per site, ranging between a minimum of 9 and a maximum of 266 • We observed rroderate, positive corre1ations between Odonata and Trichoptera taxon richness in the NAS and LSW catchments and a weak, positive corre.lation in the REN catchment. However, the corre1ation was statistically significant only in the NAS catchment (Table 3-2). Insufficient power may have affected our ability to detect corre1ations in the REN and LS W catchments. Following rarefaction, the strength of corre1ation between Odonata and Trichoptera taxon richness increased for each catchment and we observed statistically significant corre1ations in the NAS and LSW catchments (Table 3-2). 6 See Errata 76 Larval assemb1age variation: In contrast to Trichoptera, the observed magnitude of Odonata assemb1age variation did not differ considerably rurong catchments (Figure 3- 2). Differences in the magnitude of Trichoptera assemb1age variation among catchments may reflect the timing of sampling; we sampled Renous sites during midstnnmer when larval Trichoptera abundance is lowest. The magnitude of Odonata assemblage variation was considerably greater than Trichoptera assemblage variation in both the NAS and LSW catchments, but was nearly identical in the REN catchment (Figure 3-2). This difference was statistically significant in the NAS catchment (F=l 7.55, df=l, 30, p<0.001), marginally insignificant in the LSW catchment (F=3.88, df=l, 12, p=0.06), and non-significant in the REN catchment (F=0.23, df=l, 20, p=0.64). Rarefaction had a strong influence on the magnitude of assemblage variation In the case of the NAS and LSW catclnrents, there were no statistically significant differences in assemblage variation observed between the Odonata and rarefied Trichoptera datasets, eliminating the pattern observed with the raw dataset (Figure 3-2). None of the 99 randomizations for- any of the catchments produced a significant difference in multivariate dispersions rurong groups. Analyses based solely on genus-level data yielded quantitatively similar resuhs for corre1ations in richness and correlations in assemblage variation for each catchment. The magnitude of rarefied genus-level assemblage variation for Odona.ta was lower than for Trichoptera in each catchment, however, these differences were not statistically significant (p>O .1 ). 77 Concordance in assemb1age variation: Positive corre1ations between Odonata and Trichoptera distance to centroid values were observed for each catclnnent, however, the strength of the correlation varied widely, with the weakest correlation (0.44) being observed in NAS and stronger corre1ations in REN (0. 73) and LSW (0.90) (Figure 3-3). Corre1ations were statistically significant in the REN and LSW catclnnents (REN: t=3 .22, df=9, p=0.01; LSW: t=4.59, df=5, p=0.006) but not in the NAS catclnnent (NAS: t=l.83, df=14, p=0.09). Rarefaction considerably ahered correlations between Trichoptera and Odonata distance to centroid values. Correlations remained positive in each catclnnent, but became considerably stronger in the NAS (0.77) and REN (0.82) catclnnents (Figure 3-3 a, b). The corre1ation became weaker in the LSW catclnnent (0.79) (Figure 3-3 c). However, the corre1ation among distance to centroid values was significant for each catclnnent (NAS: t=4.58, df=14, p=0.0004; REN: t=4.28, df=9, p=0.002; LSW: t=0.79, t=2.88, df=5, p=0.03). Aduh Trichoptera Survey: We encountered 33 genera and 73 species of Trichoptera during our collection of aduh specimens in the Mirarnichi basin, 29 genera of which were also encotmtered in our larval sampling. The average distance to centroid value across these 5 samples was greater at the species level (0.3103) than genus level (0.2106). However, taxon riclmess and distance to centroid values were highly corre1ated between 78 the genus and species level analysis (Pearson product iroment correlations 0.81 and 0.91 respectively). Discussion Bioironitoring data have been proposed as a source of infonnation for conservation prioritisation of river ecosystems based on complementarity and the presence of rare/infrequent species (Linke and Norris, 2003). A critical assumption of such an approach is that biodiversity of focal groups reflects that of groups that are underrepresented in samples. A principal aim of our study was to assess the degree of congruence in biodiversity patterns (taxon riclmess and assemblage variation) between Trichoptera and Odonata, two groups with contrasting biological characteristics and conservation values. We observed neither strong nor consistent cross-taxon congruence in taxon riclmess between Odonata and Trichoptera. 1hese observations are in line with those of Allen et al (1999), Paavola et al (2003) and Heino et al (2005), who fotn1d overall weak but positive corre1ations in species richness airong aquatic invertebrate groups. Cross-taxon congruence in assemb1age variation was also inconsistent; strongly corre1ated in two of the catchments (LSW and REN), but weak in the third (NAS). Taken together, the resuhs of our larval study support the assertions of Bilton et al (2006) and Heino (2009) that 79 patterns of congruence in biodiversity may vary considerably depending on geographic location, scale and the taxa being compared. While the use of rarefaction to compare taxon richness between sets of samples is well established (Gotelli and Colwell, 2001), rarely is it applied to questions of cross-taxon congruence in biodiversity measures, or in the calcu1ation of beta diversity measures such as assemb1age variation We attempted to address the influence of ablllldance on cross taxon congruence in riclmess and assemb1age variation by rarefying the Trichoptera dataset (descnbed in Methods). Our resuhs suggest that differences in abundance between Odom.ta and Trichoptera can have a consilerable impact on cross-taxon congruence in their riclmess (Table 3-2), the re1ative magnitude of their assemblage variation (Figure 3-2), and cross-taxon congruence in their assemblage variation (Figure 3-3 a-c). Lack of accounting for differences in abundance may exp1ain the weak correlations in taxon riclmess and assemblage similarity observed by other studies (e.g. Allen et al, 1999; Heino et al, 2003, 2009), and suggests that congruence of biodiversity patterns between Trichoptera and Odom.ta may be stronger than observations based on raw data alone. We could not find any studies of cross-taxon congruence in :freshwater invertebrate richness or assemb1age variation that accounted for differences in abundance among groups, although Cao et al (2002) studied the influence of sampling effort on Jaccard and Bray-Curtis indices calcu1ated for an Oregon benthic macroinvertebrate dataset. They folllld that similarity between samples increased as the number of individuals included in a sample increased. Fmther, the simi1arity of samples collected 80 from the same site increased at a greater rate than similarity between sites or groups of sites, leading to improved c1assificatio n strength. Given longer adult life spans and greater flight abilities, one might expect Odonata to experience weaker dispersal limitation than Trichoptera. As a result, Odonata should demmstrate lower assemb1age variation However, we fomd little evidence to suggest that Trichoptera assemb1age variation exceeds that of Odonata in New Bnmswick. This may reflect the re1atively small spatial scale of our study, as it is possible that dispersal limitation operates at regional or Jarger scales. However, it is also possible that flight ability does not correspond directly to dispersal limitation For instance, male territoriality and site fidelity, factors that may increase dispersal limitation, are observed in several odonate taxa (Corbet, 1999). Ahernatively, wind-mediated dispersal could result in longer dispersal distances and lower dispersal limitation in taxa with weaker flight abilities (Kovats et al, 1996). Recently g1aciated Jandscapes also tend to have fewer species, possibly due to colonization by only proximate or stronger dispersing species (Lehmkuhl 1980). This may contribute to low levels of assemb1age variation in New Bnmswick where recolonisation of freshwater habitats by aquatic insects would have required dispersal from southern refugia; hence, it is possible that the New Bnmswick fauna represents a subset of cosmopolitan taxa with re1atively strong dispersal abilities. This could result in lower levels of assemb1age variation compared to southern Jatitudes. 81 Conversely, one might expect Trichoptera to demmstrate greater assemb1age variation than Odonata given their re1atively high functional-feeding group diversity. In Trichoptera, this diversity exists primarily at the genus level (Wiggins and Mackay, 1978). Though rarefied genus-level assemb1age variation was slightly greater for Trichoptera, these differences were not statistically significant (p>0.2 for each catchment). This suggests that simi1ar functional niches occur across the sites we surveyed, reducing the impact of functional diversity on the magnitude of Trichoptera assemb1age variation A potential criticism of our study is that differences in taxonomic resolution between the Odonata and Trichoptera datasets may affect our resuhs. It is reasonable to expect that species-resolution taxonomy would increase the magnitude of assemb1age variation observed in our Jarval Trichoptera dataset, particu1arly with respect to abundant and speciose genera such as Hydropsyche, Cheumatopsyche, Polycentropus and Rhyacophila. Unfortunately, comprehensive taxonomic keys for the Jarvae of these groups and others are incomplete or unavai1able, and resources did not pennit us to obtain DNA barcodes for the 10,000+ Jarval specimens we collected. However, information obtained from our adult Trichoptera survey suggests that analysis based on genus level taxonomy llllderestimates the magnitude of Trichoptera assemb1age variation For Jarval Trichoptera, rarefuction increased the estimate of assemb1age variation in each catclnnent and it is likely that we would observe this effect if species-level data were available. Hence, it is possible that, when compared at similar levels of abundance and taxonomic 82 resolution, the magnitude of assemb1age variation in Trichoptera would exceed that of Odonata. Another exp1anation for weak corre1ations in taxon riclmess and assemblage variation among freshwater organisms may be spatial scale. Sites, and the composite sample collected from them, represent a small area in comparison to overall catchment area. Freshwater invertebrates have patchy Jarval distributions (Downes, Lake & Schreiber, 1993; Wright and L~ 2002) and the density of individuals can vary widely from site to site due to factors such as productivity (Power, 1992) and the amount and stability of available habitat (Death and Winterbourn, 1995). If a sample does not encompass a Jarge enough area re1ative to the scale of patchiness for a given species, then individuals of that species will not be encmmtered consistently, even when present at most sites. This could lead to greater between-site variation in the observed richness or assemblage variation of taxa in a particu1ar group, variation that might not be correlated across groups being compared. Increased sampling effort (ie. collecting more individuals per sample) and a Jarger sampling grain should reduce the influence of patchiness on taxon lists. Similarly, the spatial extent of a study may also influence whether we observe correlations in biodiversity. A study that encompasses a greater geographic area will retmn a greater range of local taxon richness, and relatively small site-to-site variations due to patchiness may have less influence on observed correlations in richness between taxonomic groups. This may be why Heino (2002) observed strong correlations in richness among macrophytes, dragonflies, aquatic beetles and fishes in N orthem Europe, 83 while studies that were limited to comparatively small geographic areas (e.g. Allen et al, 1999; Heino et al, 2003, 2009) tend to show weaker cross-tax.on corre1ations. Studies regarding the cross-tax.on congruence of richness and beta diversity patterns in freshwater ecosystem; are increasing, however, none of these studies has investigated the re1ative magnitude of assemb1age variation rumng taxonomic groups. If a smrogate group is used to establish a conservation strategy that is dependent on the magnitude of assemb1age variation (e.g. minimum reserve networks, Pressey et al, 1993 ), then it is important that assemb1age variation in the smrogate group equal or exceed that of other groups likely to be affected by proposed interventions. Our study fmmd that the assemb1age variation of Jarval Odonata and Trichoptera could differ considerably, ahhough this pattern was not consistent across the catchments investigated. This implies that a conservation p1an based upon Trichoptera as a smrogate for biodiversity may not adequately conserve overall biodiversity in Odonata, a group that demonstrates greater assemb1age variation in so~ catchments. However, our results must be viewed cautiously. Trichoptera abundance in our Jarval samples fur exceeds that of Odom.ta and, as a resuh, Trichoptera may demonstrate lower apparent assemb1age variation Our rarefaction analysis lends support to this possibility; when rarefied, Jarval Trichoptera assemb1age variation did not differ significantly from that of Odonata in any of the catchments. Likewise, if the average mnnber of odonate individuals collected from a site were greater, it is possible that observed assemb1age variation would be lower as odonate species with broad but locally patchy distributions would be encmmtered more frequently. 84 Further complicating our observations is the issue of taxonomic resolution Adult Trichoptera assemb1age variation was much greater at the species versus genus levei a pattern that we would likely observe with our 1arval assemb1age dataset. If complete species-level identifications were practicable for both datasets, it is possible that Trichoptera assemb1age variation would exceed that of Odonata. Future studies of surrogacy and concordance of biodiversity measures should account for differences m abundance ammg groups and carefully consider the impact of taxonomic resolution on results. The decreasing cost of traditional DNA barcoding methods and the development of next generation sequencing methods (Hajibabaei et al 2011), along with constructions of comprehensive barcode reference libraries for a range of key taxonomic groups, can greatly :facilitate comprehensive biodiversity surveys in speciose groups such as aquatic insects. Acknowledgements This research was fimded by an NSERC Discovery grant to D.J. Baird and a Graduate Research Assistantship to C. Curry from the University of New Bnmswick. Funding for the BioBlitz and DNA sequencing work was provided by Environment Canada through its Competitiveness and Environmental Sustainability Initiative (CESI) and a Grants and Contnbutions agreement with the Biodiversity Institute of Ontario, University of Guelph The authors acknowledge the help of N. Hawkins, R Bedford, K. Bowser, A. Martens 85 and J. 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Canadian Journal ofFisheries and Aquatic Sciences, 59, 1404-1417. 92 Table 3-1: Taxonomic resolution of the combined Renous and Nashwaak Odonata dataset before and after DNA barcoding. Taxonomic level Morphological Barcoding No Barcode Family 166 0 36 Genus 694 135 98 Species 757 1241 107 Total 1617 1376 241 Table 3-2: Pearson Product-Moment Corre1ations between Odonata and Trichoptera taxon riclmess. Original Rarefied Catchment df cor t P-value cor t P-value Nashwaak 14 0.64 3.0907 0.008** 0.75 4.2527 0.0008*** Renous 9 0.35 1.1377 0.28 0.42 1.3888 0.20 LSWM 5 0.54 1.4228 0.21 0.84 3.5084 0.02* 93 • Adult survey locations • Larval survey locations (a) Saint .John R: Nashwaak ___ (b) Miramichi R: Renous - (c) Miramichi R: LSWM 47°0'0"N 46°0'0"N 67°0'0"W 66°0'0"W 65°0'0"W Figure 3-1: Survey locations in New Bnmswick, Canada for 1arval Odonata and Trichoptera (circles) and adult Trichoptera (squares). 94 c.o 0 L(') 0 "q" ""O ·5 0 .....'- C Q) (.) 0 CV) ..... 0 Q) (.) C ro .....en ""O N 0 . 0 0 0 Nashwaak (n=16) Renous (n=11) LSWM (n=?) Figure 3-2: Homogeneity of muhivariate dispersions for Odonata (black), Trichoptera (medium grey) and rarefied Trichoptera (light grey) for each catchment. Error bars represent standard error. 95 ~- (a) ~- • r=0.77 • a- • • • • • • r=0.44 • .... • • • ~- •• • • • • • • • • • C!_ • 0 I I I I I 0.0 0.2 0.4 0.6 0.8 ~ ~ CC!_ C. 0 (b) 0 .c .g ~- (c) • r=0.82 (C!_ 0 -.:i:_ • r=0.73 0 • ••• • • • C\!_ • • • 0 • • ~- I I I I I 0.0 0.2 0.4 0.6 0.8 Distance to centroid (Odonata) Figure 3-3: Correlation between distance to centroid values for Odonata and Trichoptera (b1ack) and Odonata and rarefied Trichoptera (grey) for the (a) Nashwaak, (b) Renous and (c) Little Southwest Miramichi catchtrent. 96 4. Limited evidence for dispersal-driven differences in the composition of caddisfly and dragonfly assemblages in Atlantic Canadian streams. Curry, C.J. & Baird, D.J. (2013) Limited evidence for dispersal-driven differences in the composition of caddis:fly and dragonfly assemb1ages in At1antic Canadian streams. To be submitted to Freshwater Biology Summary 1. The re1ative influence of local and spatial processes on the assembly of biological communities may be a key factor in the development of regional monitoring and conservation programs if patterns are inconsistent across taxonomic groups. In riverine ecosyste~, the branching network architecture of aquatic habitat may modify spatial processes such as dispersal 2. We sought to test the hypothesis that a group of insects with re1atively poor active dispersal capacity (caddis:flies) would demonstrate greater spatial structuring in their local assemb1ages than a group with good active dispersal capacity (dragonflies), and to test whether this structuring was tmre strongly re1ated to linear spatial predictors, or those that incorporate directional spatial processes, the Jatter assessed using asynnnetric eigenvector maps. We conducted multi-habitat surveys for caddis:flies and dragonflies at 34 sites in New Bnmswick, Canada. 97 3. Contrary to our prediction, caddisfly assemblages did not appear to demonstrate greater levels of purely spatial structuring than dragonflies. Variance partitioning analysis demonstrated that environmental factors, particularly sub-catchment area, had the greatest exp1anatory power for both groups, although variance explained by AEMs was greater than for latitude/longitude in most cases. 4. Together with other recently published work, our resuhs suggest that dispersal- driven structuring of riverine insect assemblages may be more apparent at larger spatial scales. Monitoring and conservation of aquatic insects at smaller spatial scales is less likely to be influenced by differences in dispersal capacity among taxa. 98 Introduction Ecologists acknowledge that both local and spatial processes govern patterns of species composition in biological comrmmities (Hubbell, 2001). Local processes generally full into two categories: environmental fihering, and biological interactions such as predation, competition, and mutualism These processes limit connmmity membership to organism; with traits suited to the abiotic and biotic constraints of the local environment, together referred to as the niche-driven or species-sorting paradigm (Chase and Leibold, 2003). An emergent feature of this view is that composition and richness are a result of processes occurring at re1atively small spatial scales, and spatial variation in composition will closely track variation in environmental conditions. The neutral view fonnu1ated by MacArthur and Wilson (1967) to exp1ain patterns of biodiversity on is1ands, and Jater generalized by Bell (2001) and Hubbell (2001), emphasizes the role of spatial processes in structuring comnrunities. Spatial processes are generally synonymous with dispersai but may include other biological processes such as speciation and extinction (Dormann et al, 2007). Here connnunity membership is limited by the probability of a species dispersing to a locality. Since dispersal involves the movement of organisms between locations, it is a structuring process that operates at Jarger spatial scales than niche-based processes (Legendre, Borcard and Peres-Neto, 2005). Attempts to integrate the niche and dispersal-driven paradigrm have led to the emergence of the metacorrnnunity concept (Leibold et al, 2004). They define the 99 metacommunity as "A set of local communities that are linked by dispersal of multiple interacting species." The conceptual and analytical tools used in metaconnnunity research were 1argely developed from work in lentic and terrestrial ecosystems, but have been embraced by researchers working in river ecosystems (Brown et al, 2011 ). Here, the branching architecture of river networks has been incorporated into predictions regarding the influence of dispersal and species-sorting processes on comnnmity assembly and composition In particular, descriptions of spatial re1ationships among sampling units are viewed in terms of network distances and network structure, as opposed to Euclidean or Great Circle distances (Brown and Swan, 2010, Landeiro et al, 2011). This description of connectivity in rivers is intuitive; the trovement of organisms without a terrestrial life stage (e.g. fish, bivalves) is dominated by flow and chamel bomdaries, and is interrupted by structures such as waterfalls and dams. For aquatic insects, dispersal during the winged aduh life stage is presumed to occur primarily along the riparian corridor of rivers and streams (Smith and Collier, 2001, Winterbourn and Crowe, 2001 ), though the role of dispersal across catchment bomdaries is also recognized (Petersen et al, 1999, Macneale, Peckarsky and Likens, 2005). In concert with spatial structuring of environmental conditions, dispersal generates the widely observed phenomenon of decreasing assemb1age simi1arity with increasing spatial separation between samples or :bcations, known as distance decay (Neko1a and White, 1999). Environmental conditions that create the adaptive Jandscape upon which niches are constructed tend to be spatially autocorre1ated (Legendre, 1993) ie. sites located closely in space tend to have similar environmental conditions, and sites 100 with similar environmental conditions should support simi1ar assemb1ages of species. Dispersal can also produce spatial autocorre1ation in assemb1age composition because the probability of successful 100vement between locations is inversely related to the physical distance between them Even in the absence of environmental variation, dispersal limitation should generate spatial autocorre1ation in assemb1age composition with increasing geographic separation, and the strength of this re1ationship should be proportional to the strength of dispersal limitation in a given group. The extent of inter-group variation in dispersal limitation (and therefore spatial autocorre1ation) may have important implications for biodiversity assessment and bio100nitoring of freshwater invertebrates. Taxonomic groups that do not track environmental conditions due to the influence of dispersal-driven processes may be poor indicators of environmental perturbations (Brown et al, 2011 ), 100re patchily distributed in the Jandscape (Palmer, A11an and Butman, 1996), and their patterns of alpha and beta diversity may not reflect those of strongly dispersing groups (Steinitz et al, 2006). Understanding how dispersal limitation and spatial autocorrelation vary a100ng aquatic insect groups woukl allow scientists to fine tune reference condition rmdels for bio100nitoring (Mykra, Heino and Muotka, 2007), and could allow p1anners to de100nstrate how reserve requirements differ a100ng groups in systematic conservation p1atming. While a number of studies have addressed cross-taxon congruence in patterns of richness and assemblage simi1arity for aquatic organism;, the re1ative influence of dispersal and niche-based processes on composition in different aquatic taxa remains largely uninvestigated. In the 100st comprehensive review to date, Shurin, Cottenie and 101 Hillebrand (2009) fomd evidence for significant spatial autocorre1ation in 28 of 36 surveys of aquatic organisms ranging from diatoms to fish, and fomd that the strength of dispersal limitation was greatest for fish. However, they did not directly measure the re1ative importance of spatial (dispersal) and environmental contributions. Landeiro et al (2011) compared the re1ative strength of spatial and environmental predictors to fish and caddis:fly assemb1age composition patterns in the Ducke Reserve near Mana.us, Brazil; however, the extent of their sample area was relatively small (ca. 100 km2). Brown and Swan (2010) analysed patterns of distance decay in a macroinvertebrate dataset from Maryland, USA, but did not decompose their analysis by taxonomic group. Identifying the re1ative strength of dispersal limitation and spatial autocorrelation ammg groups requires further attention We sought to investigate the relative influence of niche and dispersal driven processes on the composition of riverine insect assemb1ages, and to test the prediction that differing leve1s of dispersal limitation will lead to differing leve1s of spatial autocorrelation and assemblage variation exp1ained by spatial predictors. We predicted that the proportion of assemb1age variation exp1ained by spatial predictors would be greater for insect groups with low dispersal ability. In addition to this, we investigated the explanatory power of latitude/longitude re1ative to network-based spatial predictors generated using Asymmetric Eigenvector Maps (AEMs, Blanchet, Legendre and Borcard, 2008). 102 Our surveys focused on two diverse but contrasting groups of aquatic insect: dragonflies and damselflies (Odonata) and caddisflies (Trichoptera). Previous studies of aquatic insects have revealed positive relationships between wing length, dispersal ability, and range size (Rundle et al, 1997, Mahn}vist, 2000). As a larger bodied and stronger dispersing group, odonate assemblages should track variation in environmental conditions more closely than Trichoptera, and demonstrate lower levels of spatially structured assemb1age variation Given the importance of within-network dispersal for aquatic organisms, we also expected our network based spatial predictor (AEMs) to explain a greater portion of assemblage variation than linear spatial predictors. 1he re1ative importance of spatial :factors should increase with scale for both Trichoptera and Odonata. Methods Study Organisms We chose to focus on Odonata and Trichoptera because these insect groups are species rich, occur in similar habitats, and contrast markedly in their overall potential for dispersal Two dispersal characteristics distinguish these groups: body size and length of the adult life stage. Odonates are Jarge-bodied and considered to be strong fliers, while some species are capable of long-distance migratory behaviour (e.g. Anaxjunius, Wikelski et al 2006). In contrast the re1atively small-bodied caddisflies are considered 103 weak fliers (Petersen et al 1999, Sanderson, Eyre and Rushton 2005), though movements up to 16 km have been recorded (Coutant, 1982). Jenkins et al (2007) suggest a positive relationship between body size and dispersal distance among active dispersers, and Rundle et al (1997) found positive re1ationships between range size and wing size in damselflies, a pattern which they attribute to dispersal ability. Hoflsten (2004) found that site occupancy and body size, thorax mass, and wing loading were positively related across 17 species of caddis:tlies. The length of the adult life stage is also greater in odonates, providing adults with a longer time period in which to move between locations. We conducted intensive multi-habitat surveys for Jarval Odonata and Trichoptera at 34 sites across the Nashwaak (NAS), Renous (REN), and Little Southwest Miramichi (LSW) rivers in New Bnmswick, Canada from July 25, 2007 - September 18, 2008. These are forested 5th order catchments with relatively low human habitation and agricultural activity. While there is considerable forestry activity in each catchment, the selected sites retained intact riparian buffer zones with no obvious anthropogenic disturbance or point sources of pollution Sites were selected to reflect the range of habitat conditions present in the catchments. Five microhabitats were sampled at each site using standardized methods; samples were extensively live sorted in the field to remove all odonate and trichopteran larvae and identified to lowest taxonomic level in the Jaboratory. More detailed descriptions of the watersheds and larval collection/identification methods and lists of taxa can be found in Curry, Zhou and Baird (2012). Additional data on a range of enviromrental variables were derived from field measurements or geospatial data Jayers. 104 Environmental variables Field-measured environmental variables: We measured a suite of physicai chemicai and biological variables at each site in order to characterize local habitat conditions. A summary of variables, their units, and range of observed values is available in Table 4-1 (main channel) and Table 4-2 (riparian wetland). For both habitats, we visually estimated the presence of riparian vegetation in four classes (GR-grasses, SH shrubs, CO-coniferous trees, DE-deciduous trees). We visually estimated the presence of substrate within the area where we collected benthic samples in each of eight c1asses (BR-bedrock, FS-fine sediment, PE-pebbles, CB-cobbles, BO-boulders, DB-debris, LP leaf packs, WO-wood) and noted which substrate c1ass was dominant. We then stnnmed the number of substrate types present at a site to obtain a single measure of substrate variety for each habitat, as well as the entire site (main channel (SMC), riparian wetland (SRW), and total habitat (STOn). We measured discharge (Q) in the main channel using a Sontek Flowtracker® Acoustic Doppler Velocimeter (SonTek Inc., San Diego CA) attached to an adjustable ruled wading rod that allowed simuhaneous measurements of depth and flow velocity at 2/3 depth. Flow measurements were taken along a transect fixed perpendicular to the wetted edge in a section of the sampling reach where flow was re1atively uninterrupted by Jarge boulders or other obstructions. 'The first measurement was taken as close as possible 105 to the stream edge and at approximately equal intervals along the transect thereafter. A final measurement was taken as close as possible to the fur bank. We recorded depth, velocity and distance from the starting edge for each measurement and made at least 10 measurements per site, with a maximum distance of one meter between measurements. We also recorded wetted width (WW, total distance between wetted edges), maximum and average flow velocity 0/max, Vavg), and maximum and average depth (Dmax, Davg). We measured bank.full width (BW) by adding the distance between the high-water mark and the wetted edge on each bank to WW. We visually estimated the re1ative proportions of riffle, nm, and pool habitat for each site. For riparian wetland habitat, we measured the maximum dimensions of the water body sampled (length, OCx, width, OCy, and depth, OCz). In both habitats, we visually estimated the percentage of substrate covered by macrophytes and measured canopy closure using a hand-held densiometer containing a convex mirror with an embedded 24-cell grid. The densiometer was held level at ,..., 1.2 m height and the number of cells reflecting overhanging vegetation was cmmted and converted into a percentage as per the imnufacturer' s directions. We also took measurements of temperature (T, °C), dissolved oxygen (mg!L, DO, and% saturation, DO%), conductivity (Cond, µSiem), and pH using a YSI® 556MPS (YSI Inc., Yellow Springs OH) handheld muhiprobe. Catclnnent and Jand-use variables: We derived a suite of variables describing watershed conditions upstream of each site, summarized in Table 4-3. We obtained elevation and 106 average slope from a 30 m resolution Shuttle Radar Topography Mission (SR1M) digital terrain model (DlM). To calculate upstream watershed area and elevation, we first processed the DlM through a combination of filling and breaching. We then burned a known stream network Jayer provided by the New Bnmswick Department of Natural Resources (NBDNR) into the SR1M DTM and snapped sites to the burned network, allowing delineation of sub catchments and calcu1ation of sub-catchment area (logCA) upstream of each site. We performed all tasks using the Watershed Delineation Toolbox for ArcGIS 9 .2 (ESRI Incorporated). Stream order was determined using the Strahler method (Strahler, 1952) applied to a 1 :50,000 scale mapping of the stream network in each watershed. The distance along the stream network from each site to the confluence of the watershed, as well as network distance between sites, was determined using the ArcGIS 9 .2 Network Analyst Toolbox. We obtained information on Jand cover and 1and use from the most recent New Bnmswick Forest Development Survey, supplied by the NBDNR, and c1assified all polygons to simplify the interpretation of 1and-use/1and-cover patterns. In totai four 1and use/cover variables were calculated (summarized in Table 4-3). We derived each variable by intersecting the c1assified 1and-use/land-cover Jayer with the delineated sub catchment. To obtain a standardized percentage for each variable, we sllll1lred the area of polygons for each c1ass contained within the sub catchment and divided by sub-catchment area. 107 Data Analysis Following Legendre et al (2005), we partitioned the variance of Odonata and Trichoptera assembJage variation into components explained by purely enviromnentai purely spatiai and spatially structured environmental components. The significance of the first two components of variation can be assessed via pennutational ANOVA. The statistical computing program R version 2.14.2 was used for all analyses (R Development Core Team, 2011). Variance partitioning of the presence/absence transformed sites x species matrix was conducted using the varpart fimction in the R package vegan. This analysis was conducted at two scales: all sites combined (n=34) and in separate catchments, NAS (n=I6) and LSW+REN (n=I8). The LSW and REN sites are combined because they are contiguous sub catchments of the Miramichi River Basin, and are designated below as MIR The reJative exp1anatocy power of two different sets of spatial predictors was evaluated for each analysis. Geographic coordinates (latitude and longitude) are the simplest representation of space and were incorporated directly into analyses as x and y coordinates. Given the importance of within-network rmveirents to aquatic insect dispersai we also sought to incorporate network distance (ND) into our predictions. While ND can be represented in a distance matrix, this information cannot be incorporated directly as an exp1anatory variable into variance partitioning. Instead, we utilized an asymnetric eigenvector map framework (AEMs, Blanchet et al 2008) to model the directional spatial process of dispersal within the network. AEMs are 108 constructed by specifying a connection diagram and a hypothesized direction for the spatial process. From this we derived a binary sites-by-links matrix. If a link· affects a site it is represented by a 1, otherwise, 0. A schematic representation of sites and links for our watersheds is given in Figure 4-1. The sites-by-links matrix is multiplied by a vector of weights representing the inverse of the length of the edge (ie. longer edges are given less weight). This matrix is then utilized in Principal Components Analysis (PCA) to obtain a tmtrix of site scores that can be used as spatial predictors in variance partitioning analysis. Since network dispersal can occur in two directions (upstream or downstream), we calcu1ated two sets of AEMs for each analysis. This approach contrasts with that of 1bompson and Townsend (2006) who utilize regression on distance rmtrices to analyze relationships, and Landeiro et al (2011) who utilize principal coordinates of neighbourhood matrices (PCNM) to generate network distance spatial predictors for use in a variance partitioning framework. Environmental and geospatial variables were transformed as necessary to reflect normality or convert from proportional values (see Tables 4-1 and 4-2 for details). The transfonred variables were then subjected to forward selection using the fimction forward.sel of the R package pack/or for each analysis; given the srmll size of our dataset we retained a maximum of two predictors for each analysis. We used the same irethod to choose AEMs for variance partitioning. 109 Results Table 4-4 sumrmrizes the variables retained by forward selection and the results of the variance partitioning analysis for both the combined and N AS/MIR dataset. Odonata vs. Trichoptera, all sites: Forward selection identified log sub-catchment area (logCA) and total substrate complexity (Sror) as predictors for Odonata, and Date, logCA and coniferous vegetation cover (Csooo) for Trichoptera. Date was included as a separate partitioning variable for Trichoptera, and exp1aitied a significant proportion of their assemblage variation The portion of assemblage variation explained by environmental descriptors was greater for Odonata, while a significant :fraction of Trichoptera variation was explained by sampling date. In contrast to latitude/longitude spatial predictors, the selected ABMs explained a small but statistically significant portion of assemblage variation for Trichoptera and Odonata. In the case of Odonata, there is also a considerable shared space/environment component. Odonata vs. Trichoptera, Nashwaak: logCA (Odonata) and logCA and local slope (Trichoptera) were identified for inclusion in variance partitioning analysis by forward selection Environmental predictors explained a relatively large component of assemb1age variation for both Odonata and Trichoptera. In contrast to latitude/longitude spatial predictors, ABMs exp1ained a re1atively large component of assemb1age variation, though this component was not statistically significant (p>0.05) for Odonata. 110 Odonata vs. Trichoptera, Miramichi: logCA and elevation (Odonata) and logCA (Trichoptera) were identified for inclusion in variance partitioning analysis by forward selection The component of assemb1age variation exp1ained by environmental predictors was :larger for Odona.ta, while a :large component ofTrichoptera assemb1age variation was exp1ained by sampling date. For Trichoptera, Jatitude/longitude exp1ained a greater proportion of variance compared to AEMs, while the reverse was true for Odona.ta. There was also a considerable component of Odonata assemb1age variation jointly exp1ained by space and enviromnmt. Discussion Meas-wing the re1ative influence of enviromrental and dispersal-driven effects on the composition of lotic assemb1age s is gaining increased attention from :freshwater ecologists (Brown et al, 2011 ). If dispersal-driven processes are important in determining the composition oflotic assemb1ages, two patterns should emerge. First, there should be an inverse relationship between the average dispersal ability of a taxonomic group and the proportion of their assemb1age variation exp1ained by spatial variables. Conversely, species within a strongly dispersing group should detronstrate a stronger re1ationship to environmental gradients because they can be trore discerning in their choice of habitat and are less subject to spatial mass effects (Slnnida & Wilson, 1985). In addition to this, the exp1ana.tory power of spatial variables should increase with the extent of a study due to the increased influence of dispersal limitation over Jarger distances. 111 Given weaker flight capabilities and shorter adult life spans, Trichoptera were expected to experience greater dispersal limitation and therefore greater spatial structming in their assemb1ages. Contrary to this prediction, similar levels of assemblage variation were exp1ained by spatial predictors for both Odonata and Trichoptera, save for the MIR dataset where latitude/longitude explained a greater portion of Trichoptera assemblage variation (Table 4-4). The resuhs also contradicted the prediction that spatial variables would have greater exp1anatory power at Jarger scales. In :fact, the proportion of both Odonata and Trichoptera assemblage variation explained by spatial predictors (latitude/longitude or AEMs) was smaller in the combined Jarger-scale dataset. The overall exp1anatory ability of our spatial predictors was quite limited. Using latitude/longitude as our spatial predictors, we only observed a statistically significant spatial component in the MIR-Trichoptera dataset. For all other cases, AEMs explained a greater annunt of assemb1age variation than latitude/longitude, however, this component was usually small re1ative to that explained by purely environmental :factors. One exception is the Nashwaak catchment, where ,..,g % of assemblage variation in Odonata and Trichoptera was explained by AEM spatial variables, though this result was not statistically significant (Table 4-4). This result lends support to Shurin et al (2009) who found aquatic macroinvertebrate groups demmstrate roughly equivalent levels of spatial autocorrelation in assemb1age composition, and the tmre important transition in dispersal ability (and spatial autocorrelation) is between fish and aquatic insects. In contrast, 112 Bonada, Doledec and Statmer (2012) and Astorga et al (201 lb) folllld that the ammmt of spatial autocorre1ation in stream invertebrates was inversely re1ated to dispersal potential 1he N ashwaak was the onJy catclunent where the purely environmental component of variation was greater for Trichoptera than Odonata, though this likely reflects the influence of a single environmental variable being retained for partitioning odonate assemb1age variance. 1he Jarge proportion of residual variation that we could not exp1ain is likely a resuh of microhabitat variation not assessed at our sampling scale. Interestingly, (log) sub-catchment area was identified by forward selection for both Odonata and Trichoptera in each catclunent, as well as the combined dataset. This suggests some ammnt of spatial structuring of Odonate and Trichoptera assemb1ages between headwaters and lower reaches, as sub-catchment area must decrease as one moves upstream from the finthest downstream site. A number of variables known to influence the composition of aquatic commmities vary across this gradient, including discharge, primary productivity, and patterns of disturbance. Given the branching, sinuous nature of the river networks we studied, simple Jatitude/longitude variables may be inadequate for capturing spatially structured environmental variance in assemb1age composition along the gradient from headwaters to the main stem Though the statistical significance of this variance component cannot be tested, the spatially structured environmental component was consistently small for both Trichoptera and Odonata, save for the MIR-Odonata dataset. 113 Our failure to detect a Jarge amnmt of purely spatial variance in assemblage composition is consistent with the resuhs ofrecent investigations of assemb1age structure in riverine invertebrate communities. Landeiro et al (2011) found that spatial variables explained a maximum of 2.4% of assemblage variation in Trichoptera assemblages of the Ducke Reserve in Brazil, Astorga et al (201 la) found relatively low levels of purely spatial variance in macroinvertebrate assemblages in 6 of the 7 Finnish drainage system; they studied, Heino and Mykra (2008) found that variance exp1ained by purely spatial factors was low for Ephemeroptera, Plecoptera, Trichoptera and midges in eastern Finland, and Galbraith, Vaughn and Meier (2008) found that just 7 .2% of Trichoptera assemblage variation in Ouachita Mountain rivers was attributable to purely spatial variation These results contrast with those of Thompson and Townsend (2006) who fmmd that models incorporating spatial structure and environmental variables were the best descriptor of stream invertebrate assemb1ages in the Taieri River (New Zealand) and Brown and Swan (2010) who found that the signature of dispersal-driven processes was greater in river mainstems than headwaters, suggesting that the importance of dispersal to assemblage dynamics varies depending on position in the river network. Comparisons among studies are somewhat difficult because of the differing, often complex approaches employed by different studies (e.g. Mantel tests, regression on distance matrices, variance partitioning) and a Jack of consistency in the spatial predictors studied (latitude/longitude coordinates, linear vs. network distance, PCNMs vs. AEMs ). As analytical methods in this area are evolving rapidly, it is crucial that scientists develop an understanding of how resuhs from different approaches can be re1ated. This process could be facilitated through the sharing of taxonomic, spatial and environmental data used in any analysis. 114 The majority of studies investigating the relative influence of niche and dispersal driven processes on stream invertebrate assemblages have been conducted at catclnnent scales and smaller (Table 4-5). This may account for the failure of our study and others to detect significant spatial structuring unrelated to enviromrental gradients as the strength of dispersal limitation increases with the separation distance among sites, although Gombeer, Knapen & Bervoets (2011) found that spatial structuring of Trichoptera assemblages was weak relative to environmental controls across a relatively large study area in Belgium. Other spatial processes such as speciation will also become trore influential at regional and continental scales (Donnann et al 2007). The importance of historical processes such as post-glacial changes in the organization of river networks (Curry, 2007) and biotic hotrogenization (Olden et al 2004) will also have a considerable impact on the strength of spatial structuring at both landscape and regional extents. Ahhough spatial eigenfunction predictors such as AEMs often outperform linear spatial predictors in terms of variance explained in real datasets (Blanchet et al, 2011 ), the relationship of selected spatial variables to actual dispersal processes remains unclear, and opens the possibility that spatial structure attnbuted to dispersal processes may be due to unmeasured but spatially structured enviromrental variation (Landeiro et al, 2011 ). One clear advantage of spatial eigenfunction methods is that they are amenable to trodeling directional spatial processes that occur in networks such as river systems. 115 Without eigenvector approaches such as AEMs, it would be impossible to utilize network distance in variance partitioning analyses. It is tempting to view aquatic insect dispersal as a simple within- network process. However, out of network dispersal of aquatic insects has been observed in many instances (Petersen et al, 1999; Petersen et al, 2004; Briers et al 2004; Macneale et al, 2005). Even if out-of.network dispersal is infrequent re1ative to within-network dispersai it may uhimately have greater consequences for the distribution of aquatic insect species (Bihon, Freeland and Okamura, 2001, Mahnqvist, 2002; Bohonak and Jenkins, 2003) as they colonize and spread through previously uninhabited catchments. The fact that many insect species occur in catchments that do not share a network connection is clear evidence that historical out-of.network dispersal should be considered as a key factor in interpreting contemporary dis1nbution patterns. Within- network dispersal is itself a complex process. Adult insects may fly upstream or downstream in search of mates and oviposition sites (e.g. Winterbourn and Crowe 2001 ), and the phenoirenon of downstream drift in Jarval insects is well documented (Brittain and Eike1and, 1988). Larval insects can also attain upstream disp1aceirent (Bishop and Hynes, 1969, Bunn and Hughes, 1997). Specific behavioural traits such as Jarva-adult site fidelity and territoriality (e.g. Perithemis tenera; Switzer, 1997) further complicate our understanding of dispersal in adult aquatic insects. It is likely that simple descriptions of dispersal ability aliased from body size or wing hading 116 will be inadequate for ascribing dispersal processes to groups of organisms in landscape or regional scale studies such as ours. While numerous taxon-specific studies of dispersal have been conducted, aquatic ecologists Jack a clear understanding of the relative importance of these processes and how they vary across taxa, habitats and spatiotemporal scales. Consideration of why individuals disperse and the costs and benefits of different dispersal strategies would allow us to develop better hypotheses regarding spatial structure in aquatic insect assemblages. It tray be necessary to include several different c1asses of spatial predictor when explaining aquatic insect assemblage variance to account for the variety of directions in which they can disperse. The Jack of consistent differences in the degree of spatial structuring between Odonata and Trichoptera, and the low overall level of purely spatial variation in their assemb1age structure suggests that dispersal-driven processes will minimally affect reference condition imdels for bioimnitoring at catchment scales in om study area, though longitudinal structuring of aquatic insect comrmm.ities along river networks may require additional consideration Acknowledgements This research was fimded by an NSERC Discovery grant to D.J. Baird and a Graduate Research Assistantship to C. Cmry from the University ofNew Bnmswick. The authors acknowledge the help of N. Hawkins, F. Baird, R Bedford, K. Bowser, A. Martens and J. 117 McLaughlin in the field and Jaboratory, logistic support from Harry Collins and the Miramichi River Environmental Assessment Connnittee and 1arval collecting advice from Paul Bnmelle of the Atlantic Dragonfly Inventory Program. We are a1so grateful for the help ofXin Zhou, Paul Hebert, Jermifer Semotok, Elpidio Remigio and the staff at the Canadian Centre for DNA Barcoding at the University of Guelph who perfonned DNA sequencing for our odonate specimens. 118 References Astorga, A., Heino, J., Luoto, M. & Muotka, T. (2011) Freshwater biodiversity at regional extent: detenninants of macroinvertebrate taxonomic richness in headwater streams. Ecography, 34, 705-713. Astorga, A., Oksanen, J., Luoto, M., Soininen, J., Virtanen, R & Muotka, T. 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(2006) Simple rules guide dragonfly migration Biology Letters, 2, 325-329. Winterbourn, M.J. & Crowe, AL. (2001) Flight activity of insects along a trountain stream: is directional flight adaptive? Freshwater Biology, 46, 1479-148. 123 tmx, 1.76) 0.13) 0.08) 0.22) 0.20) 12.3) 18.0) 0.28) 0.22) 0.17) 0.22) (min, 0.50, 0.89, 0.60, 0.79, 5.93, 100, 70, 0.29) 73, 1.2) 0.80, 0. 0.90, 0.90, 1, Range (0, (0, (0, (0.17, (0, (0.11, (3.4, (0, (9.3, (0.05, (0.01, (0.01, (3,8, 15.8 18.4 1.35 0.41 0.17 0.08 0.28 0.25 0.42 0.42 0.33 0.41 5.9 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1 stddev 0,1 Mean, records ireter densioireter partitioning estitmted estitmted assessed assessed Fk>w presence of variance Handheld SonTek Visually Sum Visually Visually Method Visually for selection forward to Proportion Proportion Proportion Collllt m's m's m m rrf/s m m Presence/absence Presence/absence Units FS) max) subjected avg) (V BO) PE) CB) (CO) (V (DE) {Dmax) (BW) <4mm, (Davg) cm, (WW) ( cm, cm, (LP) Trees variables Trees depth vek>city (Q) treasured (>25 <25 Width <6.5 GR) depth vek>city (BR) ( ( ( Width (SH) (DB) Packs sediment Rllll Riflle %Pool ICmp Wood(WO) Maximum % Shrubs Wetted Boulders Bankfull Debris subIC Maximum Average Leaf % !Cea Coniferous Average Pebbles Deciduous Cobbles Bedrock Fine Discharge Variables Grasses enviromrental Cover variables Closure composition Main-channel vegetation 4-1: Canopy Macrophyte Riflle/Run/Pool % % Substrate % Category Hydrologic Riparian Table ~ N ~ Chemistry Temperature (1) oc YSI 556 handheld 14.0 (4.7, 22.7, 5.6) Dissolved 02 (%Sat) Percent saturation multiprobe 0.94 (0.77, 1.12, 0.097) Dissolved 02 (DO) lllVL 9.76 (7.19, 11.80, 1.34) Conductivity (Cond) µSiem 41.2 (15, 171, 26.23) pH (pH) [H+] 6.32 (5.83, 7.51, 1.01) ...... N U't Table 4-2: Riparian wetland environmental variables subjected to forward selection for variance partitioning. Category Variables treasured Units Method Mean, Range (min, max, std. dev.) Riparian vegetation Grasses ( GR) Presence/absence Visually assessed 0,1 Slnubs (SH) 0,1 Coniferous Trees (CO) 0,1. Deciduous Trees (DE) 0,1 Substrate composition Bedrock (BR) Presence/absence Visually assessed 0,1 Fine sediment (<4mm, FS) 0,1 Pebbles (<6.5 cm, PE) 0,1 Cobbles ( <25 cm, CB) 0,1 Boulders (> 25 cm, BO) 0,1 Debris (DB) 0,1 Leaf Packs (LP) 0,1 N -0\ Wood(WO) 0,1 SubOC Count Sum of presence records 5.6 (1,8, 1.8) OCH dimensions Length (OCx) m Measuring tape/ireter 23.6 (2, 100, 31) Width (OCy) m stick 5.81 (0.5, 20, 5.47) Depth (OCz) m 0.5 (0.1, 1.5, 0.38) % Macrophyte Cover OCmp Percentage Visually estimated 0.12 (0, 0.80, 0.29) % Canopy Closure OCca Percentage Handheld densioireter 0.48 (0,1, 0.38) Chemistry Temperature (1) oc YSI 556 handheld 14.1 (5.0, 23.1, 5.4) Dissolved 02 (%Sat) Percent saturation multiprobe 0.80 (0.11, 0.84, 0.27) Dissolved 02 (DO) mglL 5.88 (1.19, 10.01, 2.54) Conductivity µSiem 43.3 (17, 105, 20.1) pH (pH) [H+] 6.20 (4.35, 7.57, 0.82) Table 4-3: Landscape and Jand-use variables subjected to forward selection for variance partitioning. Variable Units Method Mean (min, imx, std. dev.) Sub-catchment area (hgCA) km2 Sub-catchment area was calcu1ated after delineating 194.7 (9.7, 1445.1, 331.08) the upstream catchment for each site using ArcGIS 9.2 Watershed Delineation toolbox Stream Order (SO) Stream order assessed using the Strahler method 3.8(2,5, 1.1) based on a Jayer provided by the NBDNR Sub-catchment Shpe (Sip) Proportion Average slope of sub-catchment 0.06 (0.03, 0.09, 0.02) Elevation (Ev) m Obtained from the SR1M-D1M 152 (17,422, 114) Distance to confluence {Deon) km Distance between sites and the confluence of the 53.4 (12.7, 115.9, 27.3) watershed was calcu1ated using ArcGIS 9 .2 Network Analyst N Agriculture and hwnan Proportion Percentage of sub-catchment area with 1and use 0.01 (0, 0.1, 0.02) --.) settlement (Hu%) defined as agriculture or settled. Historic forestry (Fh%) Proportion Percentage of sub-catchment area subject to any 0.59 (0.12, 0.90, 0.15) forestry activity. Recent forestry (F r°/o) Proportion Percentage of sub-catchment area subject to any 0.49 (0.10, 0.87, 0.16) forestry activity in the preceding 3 0 years. Conifer cover (Csooo) Proportion Percentage of catchment area in a 5000 m buffer 0.65 (0.40, 0.99, 0.13) upstream of the site where primary vegetation covera~e is conifuro us. Table 4-4: Partitioning of variance armng enviromrental and spatial predictors for Odonata and Trichoptera at the whole dataset and 2 watershed scales. Values for each explained fraction are adjusted r • In the case of the full dataset, sampling date was a significant explanatory variable for caddisflies and is included as a separate partition in the analysis. Fractions are (a) purely environmental, (b) spatially structured environmental, ( c) purely spatial and (h) total variance explained. In cases where date is an explanatory variable, we also show (d) purely date, (e) date*enviromrent, (f) date*spatial and (g) date*spatial*environment. Fraction Environment Spatial Selected Dataset variables predictor variables (a) (b) (c) (d) (e) (f) (g) (h-total) Full - Odonata logCA, SroT Lat/Long x, y 0.169 0.001 0.01 ---- 0.180 AEM D4,D3 0.116 0.054 0.044 ---- 0.214 Full - Trichoptera logCA, Csooo Lat/Long x, y 0.067 0.015 0.006 0.048 0.007 0.049 -0.012 0.180 AEM U2, U28 0.071 0.011 0.029 0.090 -0.010 0.007 0.004 0.202 NAS - Odonata logCA Lat/Long x, y 0.127 -0.007 0.024 ---- 0.143 -N 00 AEM Ul, D13 0.069 0.050 0.081 --- - 0.200 NAS - Trichoptera logCA, SlpL Lat/Long x, y 0.142 -0.004 -0.001 -- -- 0.137 AEM D14 0.127 0.011 0.079 ---- 0.217 MIR - Odonata logCA, Ev Lat/Long x, y 0.133 0.057 -0.015 --- - 0.175 AEM D2 0.132 0.058 0.041 - - -- 0.231 MIR - Trichoptera logCA Lat/Long x, y 0.054 0.007 0.063 0.111 -0.021 -0.029 0.016 0.202 AEM U16, D6 0.066 -0.005 0.026 0.072 -0.004 0.010 -0.0004 0.165 Significance for testable fractions (a), (c) and (d): bold (p<0.05), italic (p<0.10) Table 4-5: Ta.xon and spatial scale of recent studies of dispersal-driven structuring of stream assemb1ages. Where distances were not stated, they were estimated from the maps provided. n/a: data not available. Ta.xon Study Location Max. distance Study area Reference between sites Fish Ducke Reserve, 11.3 km (est.) 115 km2 Landeiro et al. Trichoptera Manaus, Brazil 2011 Invertebrates Taieri River, 100 km (est.) 4,385 km2 Thompson and New Zea1and Townsend 2006 Diatoms East-central 19km 150 km2 Heino et al Bcyophytes Fin1and 58 km (est.) 2150 km2 2012, Invertebrates Heino & Mykrii 2008 Diatoms River Viaur, 73 km (est.) 1,530 km2 Grenouillet et Fish southwestern al. 2007 Invertebrates France Invertebrates Garrett County, 75 km (est.) 4,100 km2 (est.) Brown and Macy1and USA Swan 2010 Trichoptera Southeastern 112km 6,610 km2 (est.) Galbraith et al Ok1ahoma, 2008 USA Odonata, New 119 km 4,482 km2 This study Trichoptera Bnmswick, Canada Trichoptera FJanders, 220 km (est.) 13,521 km2 Gombeer et al Belgium 2011 Invertebrates Fin1and -800km n/a Astorga et al 2011b PJankton Various studies >1,000 km n/a Shurin et al Fish reviewed between trost 2009 Invertebrates distant sites. Invertebrates Mediterranean -4,000 km - 1,100,000 km2 Bonada etal Basin (est.) 2011 129 46°30'0"N (a) 46°15'0"N 67°0'0''W 66°30'0"W (b) 47°0'0''N 46°45'0"N 66°30'0"W 66°0'0"W Figure 4-1: Schematic diagrams of the (a) Nashwaak and (b) Little Southwest Miramichi/Renous catchments. e = edge cormecting site, s = site 130 5. Scale-related patterns of biodiversity in riverine insect assemblages: observations from the Canadian national biomonitoring program Curry, C.J., Monk, W.A. & Baird, D.J. (2013) Scale-re1ated patterns of biodiversity in riverine insect assemb1ages: observations from the Canadian national biomonitoring program. To be submitted to Diversity and Distributions or P LoS One. Abstract With a database of over 13,000 samples, the Canadian Aquatic Biomonitoring Network presents an opportunity to docurrent patterns of riverine insect biodiversity in Canada re1ative to spatial and environmental gradients, and test the hypothesis that dispersal driven structuring of riverine insect assemblages will be stronger at Jarger spatial scales. We re1ated genus richness and beta diversity (average distance to centroid) across the full dataset to Jatitude, longitude, ahitude, pH and stream width to identify broad-scale patterns of biodiversity. We analyzed a subset of samples in the Water Survey of Canada Pacific Drainage to quantify the portion of assemb1age variance attnbutab le to purely spatial (ie. dispersal driven), purely envirorurental and spatially structured environmental variables. Local genus richness was weakly re1ated to each gradient, ahhough there is some indication that stream size establishes a lower bomdacy on richness. A stronger, statistically significant corre1ation was observed between beta diversity and variation in stream width. Contrary to our hypothesis, the purely spatial 131 component of variance was small in both the Pacific Drainage and its constituent sub drainages, suggesting that dispersal p1ays a re1atively small role in structuring aquatic insect assemb1ages at Jarger scales. However, species-level information may be required to fully test this hypothesis. Sub-catchment area consistently exp1ained a Jarge proportion of variance. Together with the positive corre1ation between stream width and beta diversity, it appears that stream insect assemb1ages may exhibit a Jarge degree of structuring from headwaters to the mainstem. However, our observations do not preclude the existence of dispersal-driven structuring at smaller spatial scales. Keywords: CABIN, aquatic insects, beta diversity, variance partitioning, rivers 132 Introduction The analysis of biodiversity patterns in rivers has traditionally focused on exp1aining the response of assemb1age composition to environmental variables (e.g. Doledec and Chessel 1994). Ecologists studying these patterns have noted that dispersal processes can p1ay an important role in determining the composition and variability of local assemb1ages (McKnight et al 2007). Evidence from numerous studies suggests that the role of dispersal varies across taxa (Qian 2009), ecosystems (Soininen, Lemon & Hillebrand, 2007) and spatial scales (Gabriel et al 2006). Understanding how spatial processes such as dispersal can influence assemb1age composition in rivers is important for both conservation and the interpretation ofbiomonitoring data. For instance, diagnosing local impacts of anthropogenic distmbance re1ative to environmentally simi1ar but unimpacted reference sites could be difficult if the biotic assemb1age targeted by sampling exhibits high spatial variability in composition unre1ated to enviromrental conditions. Similarly, the application ofbiodiversity conservation strategies may depend on the extent to which assemb1ages of indicator taxa are spatially structmed by processes such as dispersal Inevitably, given the limited budgets and short timescales of modem river science, research into the processes that determine the composition and variability of aquatic assemb1ages (beta diversity) has been conducted on smaller, individual systems (ie. ::;5th order rivers). These studies often lack a broader quantitative context within which to p1ace their observations. Given that dispersal can operate at Jarge spatial scales 133 (Pahner, Allen & Buttmn 1996) and patterns of species richness and beta diversity are highly scale dependent (Gering and Crist 2002), the local to intermediate scale focus of most research limits our ability to test predictions regarding the influence of processes such as dispersal Infonnation compiled from heal studies could be used to fill the information gap, however, methodological differences between studies limit the comparability of data. A potential solution is to utilize national-scale biomonitoring data. The Canadian Aquatic Biomonitoring Network (CABIN) is a nationwide network ofmmicipai provincial/territorial and federal governments, NGOs, industry and academic partners maintained by Environment Canada to implement a standard approach to the biological monitoring of freshwater ecosystem heahh. Members of the network use standardized methods for collecting biological and environmental data, and training and data quality standards are strictly maintained. The CABIN dataset cmrently comprises over 13,000 samples across Canada (Figure 5-1 ). Altoough there are spatial gaps in this dataset, the present level of coverage provides an excellent opportunity to evaluate spatial and regional variation in the composition ofbenthic insect assemb1ages in Canadian rivers. We present a national- and regional-scale analysis of aquatic insect biodiversity patterns based on information derived from the CABIN biomonitoring sample database. We sought to address 3 objectives: 134 1- Document patterns of tax.on richness in Canadian aquatic insect assemblages, and test the relationship between local tax.on richness and five hypothesized drivers: Jatitude, longitude, altitude, pH and stream width; 2- Document regional differences in aquatic insect beta diversity (assemb1age variation), and test the re1ationship between assemb1age variation and variation in three hypothesized drivers: altitude, pH and stream width; 3- Quantify the degree of purely spatial structuring in aquatic insect assemblages observed at Jarger spatial scales. If the importance of dispersal processes increases with scale, then the variance explained by purely spatial predictors should be greater at rmjor-drainage scales vs. sub-drainage scales. Patterns of tax.on richness in stream corrnnmities have been extensively studied at srmller scales, but there have been relatively few analyses at Jarger scales (but see Stout & Vandermeer 1975, Vinson & Hawkins 2003). These studies have suffered from a number of limitations, particularly inconsistent observation, poor taxonomic resolution and inadequate replication However, they have attempted to measure patterns of richness and diversity in aquatic organisms in relationship to a number of fuctors, including latitude, stream size and productivity. Latitudinal patterns of richness are well documented for rmny organisms, with a general pattern of decreasing tax.on richness with increasing latitude (Hillebrand 2004). However, it is llllclear whether this pattern holds true for lotic insect taxa, whose larvae dominate Canadian river rmcroinvertebrate 135 assemblages, as other authors have fmmd the riclmess of these groups peaks at mid Jatitudes (e.g. Vinson & Hawkins 2003). Stream size has been hypothesised as a key factor driving the composition and diversity of aquatic assemblages, as progressively larger streams contain greater substrate area and habitat complexity, resulting in a more diverse and species-rich assemb1ages of taxa, but decreasing in very large rivers with low substrate complexity (Minshall et al 1985). Productivity has also been evaluated as a driver of taxa richness in streams (Dudley et al 1986), as a larger resource base provides more opportl.mities for niche partitioning, therefore supporting a more diverse corrnmm.ity of consmrers. In the context of this study we were able to evaluate relationships between taxon riclmess/diversity and Jatitude, longitude, ahitude, pH and stream size. Productivity was not evaluated as information relating to this factor (e.g. nutrients, algal standing crop) was not consistently observed across all sites. Geographical variation in beta diversity has been poorly studied in general, let alone for larval aquatic insects. In the context of this study we investigated two aspects of beta diversity. Assemb1age variability, following the definition of Anderson et al (2006) is the average distance between a sample or site and its group centroid in muhivariate space. This is a non-directional measure of beta diversity; that is, it is does not measure turnover in assemblage composition across a geographic or environmental gradient (Anderson et al 2011). However, it can be used to compare local variability in composition between different landscape tmits. In this case, we evaluated regional differences in beta diversity among drainage tmits, and evaluated the degree to which this variability was associated with variability in stream width, ahitude and pH among units. 136 Beta diversity can also be viewed as the variance of a site x taxa data table (Legendre, Borcard & Peres-Neto 2005). This variation can be partitioned ammg matrices of explanatory variables, allowing the re1ative contribution of competing hypotheses to be assessed. As dispersal processes are believed to generate spatial structure in lotic insect assemb1ages (Heino & Mykrli 2008) we sought to investigate the re1ative explanatory power of environmental and spatial variables utilizing the variance partitioning approach suggested by Legendre et al (2005), and refined by Peres-Neto et al (2006). While this approach has been applied to questions of spatial structuring in aquatic corrnmmities at heal and Jandscape scales, there are few datasets with sufficient spatial coverage, consistent observation and sample replication to pennit the investigation of spatial structure at Jarger scales. The Water Survey of Canada (WSC) Pacific Drainage of western Canada (Figure 5-2) with re1atively few spatial gaps in the available data, was well suited to addressing this question Here we conducted variance partitioning analyses at the WSC sub-drainage and major-drainage scales; if dispersal is a key structuring force for aquatic insect assemb1ages, then its influence should be easier to detect at Jarger spatial scales. Together, these analyses represent the first national scale analysis of aquatic insect biodiversity information conducted in Canada, and provide a baseline against which future studies can be evaluated. 137 Methods The CABIN Biomonitoring Protocol The CABIN biommitoring protocol was developed in the early 1990s during an environmental assessment of the Fraser River Basin in British Columbia, Canada based on simi1ar progra.im in Australia, ie., Australian River Assessment System or AUSRIVAS (Smith et al 1999) and the United Kingdom, ie., River Prediction and Classification System or RIVP ACS (Wright et al 1998). It is a reference condition approach where the benthic invertebrate assemblage at a test site is predicted from a reference condition :rrodel developed from data collected at environmentally similar, but unimpacted sites. 1he benthic macroinvertebrate sample is collected from the riffie area of wadeable streams and rivers using a travelling kick net with a standard effort of 3 minutes and a maximum mesh siz.e of 400 µm Samples are typically collected in late smnmer or early autmnn, when streams are generally accessible and benthic macroinvertebrate diversity is greatest. In the laboratory, the sample is divided into 100 subsamples using a Marchant Box (Marchant 1989) and random cells are sorted until a minimum of 300 individuals are identified; the resulting counts are used to estimate whole sample abundance. Presently the CABIN dataset consists of over 13,000 samples. These samples are not evenly distributed across Canada as much of the sampling effort has occtll"fed in 138 British Columbia where methods were initially developed and adopted by the provincial government. A1so, many sites have been sampled multiple times. However, there is sufficient data coverage in enough drainages to pennit some cross-Canada comparisons. Taxonomic resolution arrong studies and samples varies considerably; while imst reference condition imdels are constructed based on fumily-level data, taxonomic identification at reference sites is nonnally performed to the lowest feasible taxonomic level We chose to conduct our analyses at the genus-levei so we limited the dataset to samples where at least 75% of insect individuals were identified to genus and a minimum of 100 individuals were identified, retaining 1461 samples for the national-scale analysis (Figure 5-1 ). This criterion reduces analytical problems associated with uncertain taxonomic assignment while retaining a broad sample coverage. In the samples we retained, an average of 1003 individuals were identified to genus, representing an average of 90% of the total individuals in the sample. Field-collected environmental variables In addition to the benthic macroinvertebrate sample, biological, chemical and physical habitat data are collected as part of a CABIN site assessment. Although the specific parameters collected vary somewhat among studies based on budget, suspected anthropogenic impacts and study objectives, a number of parameters known to influence benthic invertebrate assemb1ages are collected at each site (Table 5-1). Details on the 139 methods used to collect this data are descnbed in fi.nther detail by the CABIN Field Methods Manual (Environment Canada 2012). Geospatial Variables We delineated the upstream catchment for each CABIN site in the Pacific Drainage Area from the 30 m resolution AS1ER (Advance Spaceborne Thermal Emission and Reflection Radiometer) digital elevation model (DEM) using the Hydrology Toolbox in ArcGIS 10.1 (ESRI 2012). We mosaicked the raw DEM tiles and clipped them to the outer bomdaries of each WSC sub drainage. We processed the clipped DEM by :filling, and used the filled DEM to generate a flow direction raster and subsequent flow acclllllU1atio n raster from which we delineated the upstream catchments for each site using the Watershed Delineation toolbox. We derived several geospatial variables directly from the delineated catchments and filled DEM. First, we recalculated sub-catchment area (km2) for all delineated sub catchments. We detennined local elevation (m) from the filled DEM using the Extract Values to Point tool in the Extraction toolbox. We converted the filled DEM into slope and aspect rasters using the Slope and Aspect tools in the Surfuce Toolbox. We determined local slope (%) and local aspect (degrees) for each site using the Extract Values to Point tool However, a number of sites over1apped empty slope/aspect raster cells. In this case, we used the raster cell nearest the site to obtain the local slope/aspect 140 value. We derived average slope and average aspect for the upstream catclnrents in Geospatial Modeling Environment (GME) 0.7.2.1 (Beyer, 2012) using the isectpolyrst command. This command calcu1ates the average value of all raster squares contained by an over1aying polygon and calcu1ates separate values for over1apping polygons. Computation was difficult for a number of Jarger catclnrents, so we calcu1ated values individually for these catchments using the wnal statistics tool in ArcGIS. Simi1ar to slope and aspect, we calcu1ated average maximum temperature (°C), average minimum temperature (°C) and ~an annual precipitation (nm) for each catchment for each catchment using the isectpolyrst fimction in GME 0.7.2.1 from Meteorological Survey of Canada climate rasters. We derived land cover variables from Land Cover Circa 2000 vector data (http://www.geobase.ca/geobase/en/data/landcover/csc2000v/descriptionhtmQ. This information is based on the vectorization of raster thematic data from c1assified Landsat-5 and Landsat-7 ortho-images. While mnnerous land-cover c1asses are descnbed by the data, we sumrrmized information to derive three variables (percent urban, percent agriculture and percent forest cover). We calcu1ated these variables by intersecting the delineated sub-catchments with the Jand-cover polygon Jayer, summing area by land cover type and dividing this by catclnrent area for each sub catchment. 141 Data Analysis Unless indicated, we performed all anaJyses using the statistical computing software R version 2.15.2 (R Core Team, 2012). We used various packages to conduct analyses including vegan version 2.05 (Oksanen et al 2012), sampling version 2.5 (Tille and Matei 2012), PCNMverskm 2.1-2/r106 (Legendre et al 2012) andpackforversion 0.0-8/rlOO (Dray et al 2011). Genus richness and Simpson's Diversity: While all CABIN macroinvertebrate samples are collected using a standard technique, differences in abmdance among sites and regions and the requirements of individual studies dictate varying levels of subsampling in the laboratory. This inlubits direct comparison of genus riclmess among samples (ie. via rarefuction, Simberloff 1978). Instead, we calcu1ated Simpson diversity for each site as this measure also accomts for the relative abundance of taxa in a sample. We performed separate analyses on the nationwide dataset, within the WSC Pacific Drainage and on the subset of samples that were completely identified. To investigate nationwide patterns (n=1461 sites), we correlated Simpson diversity with 5 variables for which we have complete coverage: Jatitude, longitude, elevation, pH and wetted width. The availability of genus-level CABIN data at higher latitudes and elevations is mostly restricted to British Columbia and the Yukon It is possible that biogeographic 142 differences amongst regions (Lelnnkuhl 1980) could obscure latitudinal or altitudinal patterns; for instance, certain orders of insect (e.g. Plecoptera) are less diverse in central and eastern Canada. We conducted the same analysis as above, but restricted our scope to the Water Survey of Canada's Pacific Drainage definition Because a considerable number of samples were completely identified, direct comparisons between genus richness and environmental gradients were a1so possible. This analysis was conducted for both the full dataset and the Pacific Drainage. Here we evaluated both raw data (taxa observed) and estimated genus richness (Chaol index, Chao 2005) as the abmdance of individuals in a sample varied greatly between samples. Samples which are subsampled typically contain more individuals, so are also expected to contain more taxa. While on average these samples contained more taxa, the difference was relatively small (21.5 genera/sample in subsampled vs. 19.9 genera/sample in fully identified samples). Assemb1age variation: We conducted our analysis of assemblage variation at two spatial extents based on the sub and sub-sub drainages defined by the Water Survey of Canada. For each drainage unit we calcu1ated a measure of assemblage variability based on the definition of Anderson et al (2006), which uses the average distance between the sampled assemb1ages and their group centroid or spatial median in principal coordinates space. A distance matrix is first calcu1ated from a sample x taxa table, and this matrix is used in principal coordinates analysis. There was a Jarge range in abmdance in the 143 samples and in some cases only a subsample was identified. Thus, raw abundance data could not be employed in the analysis. To address this we utilized Hellinger distance in our analysis, which is the Euclidean distance calcu1ated from square-root transfonned relative abundance data. Hellinger distance provides a ba1ance of linearity and resolution in multivariate analyses (Legendre & Gallagher 2001 ). Given the lllleven distribution of samples among drainages within our dataset, we took a random subsample of 12 sites for each drainage unit. Drainage units with fewer than 12 sites were excluded from the analysis. This procedure was carried out 99 titres to provide an "average" average distance to spatial median (DSM) value for each drainage unit. We interpreted assemb1age variation in re1ation to variation in altitude, pH and stream width. For each variable we calcu1ated the coefficient of variation within each drainage unit and corre1ated these values with average distance to spatial iredian values. Variance partitioning: We employed variance partitioning by partial redundancy analysis (RDA) in order to address the relative explanatory power of spatial and environirental forces in structuring riffle insect assemblages. (Legendre et al 2005, Peres-Neto et al 2006). Variance partitioning allows the independent contributions of spatial and environirental control to be identified, rermving the effect of spatially-structured environirental variation The purely spatial and purely environirental fractions can be tested for statistical significance using permutational ANOVA. For our analysis we only included sites from Pacific Drainage as sample coverage was too sparse in other regions. We also conducted the saire analysis for 10 sub-drainages with sufficient replication and 144 sample coverage (Figure 5-2). In all, 622 sites with sufficiently detailed taxonomy and detailed envirorurental variables were used in the analysis. For each analysis we used Hellinger transformed abmdance data (Legendre & Gallagher 2001 ). This region is well suited to address the relative role of environmental and spatial structuring as high topographic variation creates large envirorurental and spatial gradients. Envirorurental variables used in the variance partitioning analysis were identical to those calcu1ated for our analysis of assemblage variability, though we used values for individual sites, rather than the CV of sites in a given drainage unit. Prior to forward selection, variables were inspected for nonnality and transformed if necessary. We also included :field-measured CABIN variables in the analysis, however, several of these variables were excluded if they were not recorded for a Jarge mnnber of sites. We also excluded binary variables descnbing riparian vegetation and substrate characteristics from the analysis. Variables included in the final analysis for the Pacific Drainage and its sub drainages, as well as any transformations used are described in the results. We limited the number of variables selected for each catchment by inspecting the cumu1ative adjusted r2 values following forward selection Variables were excluded if they added less than 0.02 to the cumu1ative adjusted r2, the permuted p-values were greater than 0.05, or if they exceeded a threshold of# of sites/IO variables. While conservative, this approach avoids overfitting the final solution and simplifies interpretation of results. We described spatial structure in two ways. First, we mapped Jatitude and longitude coordinates for each site onto a two dimensional plane that provides an x and y 145 variable. Following Borcard et al (1992) we then subjected these variables to a cubic trend surfu.ce regression, the terms of which can serve as separate exp1anatory variables m the analysis. We trodelled a second set of spatial structures to generate a set of spatial predictors using Principal Coordinates of Neighbourhood Matrices (PCNMs, Borcard & Legendre 2002, Dray et al 2006). PCNMs map a triangu1ar Euclidean distance matrix (ie. linear distance between sites) into a rectangular matrix whose columns can be used as exp1anatory variables in tmivariate regression or constrained ordination This is accomplished by subjecting a Euclidean distance matrix to Principal Coordinates analysis, using a tnmcation threshold for longer distances. For our analysis we used the defuult threshold, which is the longest distance that will keep sites connected. PCNMs have detronstrated greater exp1anatory power than xy coordinates or polynomial transformations in a number of study syste~ (e.g. Brind'Amour et al 2005, Beisner et al 2006), thus we sought to compare their performance in our study. As with environmental variables, we subjected spatial variables to a forward selection procedure prior to inclusion in the analysis. Results Taxon diversity and richness patterns: A total of 576 insect genera were observed in the 1461 sample subset used to calcu1ate measures of Simpson's Diversity (S) at the national scale. Despite a wide range of Sin the samples that have been collected, there were only weak corre1ations with latitude, longitude and pH (Figure 5-3 a-d). There is some 146 indication that the lower limit of S increases with increasing wetted width (Figure 5-3 e), however, the correlation is not significant (Table 5-2). Limiting the dataset to sites contained within the Pacific Drainage reveal somewhat different patterns: The correlation between Sand latitude became positive, and a significant positive corre1ation was observed with ahitude (Figure 5-3 a, c; Table 5-2). There was a simi1ar indication that the lower boundary of S increased with stream siz.e (Figure 5-3 j). A total of 598 samples in the subset were completely identified; analysis was restricted to the 300 samples contained within the Pacific Drainage. Both estimated richness (Chao 1) and taxa observed (T obs) were virtually lll1Corre1ated with longitude, altitude, pH and wetted width (Table 5-2), however, Jatitude demmstrated a weak negative correJation with chao 1 (Figure 5-4 a). Assemb1age variation: A total of 1638 samples distributed across 36 sub drainages and 56 sub-sub drainages were retained for this portion of the analysis. Average DSM values ranged from a minimum of0.51 in the Skagit sub drainage to a maximum of0.82 in the Assiniboine sub drainage (Figure 5-5), while the overall average was 0.71. At the sub-sub drainage scale, values ranged from 0.51 in the Skagit (B.C.-Wash) to 0.83 in the Lower Fraser-NahatJatch, while the overall average of all sub-sub drainages was 0.68 (Figure 5- 6). There were no obvious latitudinal or longitudinal trends; indeed, adjacent sub-sub drainages often demonstrated considerably different levels of assemblage variation, while some northern sub-sub drainages (Upper White and Upper Peel) had re1atively high DSM values. 147 At the sub-drainage levei assemb1age variation dermnstrated a significant positive corre1ation with variation in (log) wetted width (Figure 5-7 c, r=0.54, t=3.71, df:=34, p=0.0007). Corre1ation with variation in altitude was marginally insignificant (r=- 0.29, t=-1.74, df:=34, p=0.09), and there was no re1ationship between variation in pH and assemb1age variation (r=0.07, t=0.42, df:=34, p=0.68). Similar results were observed at the sub-sub-drainage levei although the corre1ation between assemblage variation and variation in (log) wetted width was somewhat weaker (Figure 5-7~ r=0.47, t=3.96, df:=54, p=0.0002). Variance partitioning: Forward selection resuhed in the retention of three environmental variables for the Pacific Drainage: average slope, (log) sub-catchment area and average maximum temperature. Given the degree ofreplication and Jarge spatial extent of the dataset these variables were weakly corre1ated. Three te~ (xy, x2y and xy2) were retained from the cubic trend stnface regression Together these variables explained 13.1 % of the variance in the Hellinger transformed site*taxa table. However, most of this variation was attnbutable to the purely environmental component (9.8%, df:=3, 615, F=24.20, p<0.005), while the purely spatial and space*environment components of variation accounted for 0.9% and 2.4% respectively (Table 5-3). Similar results were observed when PCNM variables were used as spatial predictors; the overall variance explained by the selected variables was 12.6%, most of which was attnbutable to the purely environmental component (9.7%, df:=3, 615, F=23.79, p<0.005). 148 Results for the ten sub drainages are presented in Table 5-3. Gaps in the environmental dataset meant that the :full set of variables subjected to forward selection differed between sub drainages. For seven of the ten sub drainages, a significant sampling date or sample year component was identified. For most drainages the amount of variance purely attributable to this component was re1atively small compared to overall variance exp1ained (ie. <4.0%). Two exceptions were the Skagit sub drainage, which had a very Jarge sample year component (16.5%) and the Haida Gwaii sub drainage, which had a considerable sampling date component (5.9%). In generai geospatial variables were identified by forward selection before field collected variables. The specific variables identified by forward selection differed by sub drainage, however, certain variables were consistently selected. Sub-catchment area was identified for 7 of the 10 sub drainages we investigated, while average slope was selected for 5 of the sub drainages. An exception to this was the Haida Gwaii sub drainage, where macrophyte coverage, local slope and dominant substrate type were identified by forward selection Total variance exp1ained was greater in each sub drainage than it was for the Pacific Drainage, ranging from a minimum of 14.0% for the Vancouver Is1and sub drainage, to 32.1 % in the Southern Coastal Waters sub drainage. The component of variance attnbutable to purely environmental factors was significant in each catchment (Table 5-3), and generally greater than purely temporal and/or spatial components. One exception is the Southern Coastal Waters drainage, where a significant purely spatial component of variation was identified (10.0%; df=2, 23, F=2.3896, p<0.005). This drainage also demonstrated a Jarge spatially structured environmental component of 149 variance (11.1 %). The Lower Fraser and Thompson catchments also demonstrated a relatively Jarge spatially-structured environmental component {Table 5-3). Appendix 4 provides resuhs for variance partitioning using PCNM variables. The outcome of these ana]yses did not differ considerably from those conducted using tenm from the cubic trend-surface regression; henceforth we will restrict our discussion to resuhs from the latter set of analyses. Discussion We sought to docurrent genus-level patterns of aquatic insect alpha and beta diversity in Canada, with a particu1ar focus on the Pacific drainage where sample coverage and replication were best. Additionally, we wanted to evaluate relationships between biodiversity patterns and a set of hypothesised external drivers, namely ahitude, stream size and latitudinal/longitudinal patterns. Finally, we sought to understand the degree to which spatial structuring varied among areas and test the hypothesis that the variance explained by purely spatial predictors would increase with scale. Genus-level Simpson diversity exhibited a positive, albeit weak relationship with wetted width (Figme 5-3 e,j). While the number of1arge rivers (>100 m wetted width) in the dataset is too srmll to draw firm conclusions, it appears that stream size rmy establish a lower boundary on diversity. When the dataset was limited to samples which had been completely identified, a different trend became apparent, where the upper bound for 150 estimated richness was greatest in smaller streams (Figure 5-4 b). However, there was a very wide range of genus richness observed at nearly every stream size, and with greater replication similar variation may be observed at Jarge river sites. While there were no clear Jatitudinal trends in Simpson diversity within the national dataset (Figure 5-3 a), there is som! evidence that genus richness peaks at lower Jatitudes within the Pacific Drainage (Figure 5-4). However, given considerable variation within the dataset, Jatitude would appear to p1ace a limit on regional genus richness levels rather than strictly dictate genus richness at the site scale. Trends may be weak due to a number offuctors. I-taxonomy: Genera have wider geographic distnbutions than individual species; 2- sample size: It is possible that too few individuals have been collected to obtain a reasonable estimate of local richness, given patchiness and differences in abllll.dance among sites; 3-scale: The scale of a kick net sample is so small that, if richness can vary greatly from riffle to riffle, then wildly different estimates of richness could be obtained from a single site (e.g. see Boyero 2003). Finally, Jatitudinal variation in richness may be obscured by altitudinal variability, though we did not observe a significant corre1ation between altitude and estimated richness in the Pacific Drainage (Table 5-). The observed degree of assemb1age variation within sub drainages and sub-sub drainages varied considerably between units (Figure 5-5, Figure 5-6). However, assemb1age variation was strongly re1ated to variation in wetted width at both scale (Figure 5-7 c, f), suggesting that observed differences in assemb1age variation among 151 drainage units may reflect properties of the dataset as opposed to Jarge-scale geographic differences. Another variable expected to demonstrate a strong re1ationship to assemb1age variation was variation in altitude, however, no corre1ation was observed (Figure 5-7 a, d). The influence of variability in stream size on variability in composition implies that, as catclnnent size increases, so too does beta diversity within a catclnnent. A positive re1ationship between beta diversity and study area has been noted previously (Magurran & McGill 2011 ). However, the re1ationship tmderscores the importance of scale dependency in biodiversity management and conservation planning. Conservation measures (e.g. protected areas, riparian buffers and pollution control) are often easier to implement in headwaters (Lowe & Likens 2005). Even if a considerable area is protected it may be a less effective strategy compared to one that distnbutes protection along the length of a river. A positive corre1ation between assemb1age variation and variation in stream size also implies that network architecture plays an important role in generating beta diversity within a catclnnent. A catclnnent with relatively low stream density and fewer nodes should disp1ay lower beta diversity than an equally-sized catclnnent with high stream density and more nodes, an idea that has previously been suggested by Benda et al 2004. It points to an important question for riverine biodiversity research: Is variation between streams of a similar size in different catclnnents greater than variation between streams of different sizes within a catchment? C1arke et al (2008) suggest that headwaters 152 demonstrate greater beta diversity than mainstetm, while Brown and Swan (2010) fmmd that beta diversity peaks at mid-reaches. Few authors have focused on quantifying the re1ative strength of within- and between- catchm!nt beta diversity, but Olll' observations suggest that headwater-mainstem gradients are a key driver of assemblage variation in lotic systerm. Results from the variance partitioning anaJysis contradicted Olll' initial prediction that spatial structlll'ing would be stronger at the major drainage scale, and cast some doubt as to whether dispersal-driven structlll'ing is of broad importance. However, this conclusion must be interpreted with caution Olll' ability to detect spatial structlll'e depends upon sampling grain (Fortin & Dale 2005). The average distance between nearest neighbo ID'S in the dataset used for variance partitioning was re1ative ly Jarge (7. 9 km), and while some samples were more closely spaced, the dataset is better suited to capturing large-scale spatial structlll'ing in assemblage composition Recent studies of dispersal and spatial autocorre1ation in stream insects have revealed small-scale spatial structlll'ing (Downes & Reich 2008) that would go undetected with the ctnTent dataset. This may also explain why rrruhip le studies of spatial structlll'ing in aquatic insect assemblages have encountered re1atively low levels of plll'ely spatial variation in composition (Table 5-4). Some studies have detected significant spatial structlll'ing in aquatic macroinvertebrate comrmmities. For instance, Thompson & Townsend (2006) found that models which incorporated spatial distance better explained patterns of macroinvertebrate 153 dissimilarity rumng sites than mode1s which included ecological fuctors alone. However, trophic grouping was important, as they found grazers demonstrated greater spatial structuring than predators. Brown and Swan (2010) found that dispersal-driven structuring was important for mainstem macroinvertebrate assemb1ages, but not headwater assemb1ages. 1hese studies utilized a regression on distance matrices (RDM) approach to test their hypotheses, as opposed to variance partitioning. RDM first converts the site*taxa composition table into a distance matrix, then re1ates this to an environmental and spatial distance matrix. However, Legendre et al (2005) reconnnend that variance partitioning be used to study the role of dispersal in structuring assemb1ages, as it exp1ains more of the variation in comrrrunity composition compared to RDM or Mantel approaches. Our fuilure to detect large-scale spatial structuring may reflect the spatial predictors we selected. Both the PCNM variables and cubic trend-surfuce regression tenm are derived from Euclidean (ie. over1and) distances, while ignoring network patterns. Given that networks are a major dispersal route for many aquatic insects, either during flight or drift (Williams & Williams 1993) predictors that incorporate network distance such as PCNM based on network distance (Landeiro et al 2011) or asynnnetric eigenvector maps (AEMs, BJanchet et al 2008) may better model spatial structure in riverine insect assemb1ages. Landeiro et al (2011) found that PCNMs based on network distance exp1ained a greater proportion of caddisfly and fish assemb1age variation than traditional PCNMs or xy variables. However, as we observed in Article 3, AEMs perfonred no better than xy variables in exp1aining assemb1age variation in New 154 Bnmswick caddisfly and dragonfly assemb1ages. The spatial variables we employed may emphasize out-of.network dispersal A combination of network-based and Euclidean spatial variables may be necessary to exp1ain spatial patterns of riverine insect assemb1age composition A clear limitation of the CABIN dataset is the level of taxonomic resolution Species-level information would increase the overall variance ofthe site*taxa table, and may change the structure of this variance in such a way that greater spatial structuring could be detected. For instance, if two or tmre congeneric species exlnbit different Jatitudinal distnbutions, our anaJytical approach would be tmable to detect this spatial structuring. Even if we possessed species-level infonnation, it may be easier to attnbute such variation to historic biogeographic forces as opposed to dispersal-driven structuring (Mykra et al 2007). A review of stoneflies in British Cohnnbia and the Yukon detmnstrates near complete over1ap in genus lists, while mnrerous species are absent from the Yukon species list (Stewart & Oswood 2006). Similar patterns also exist for caddisflies (Trichoptera) and mayflies (Ephemeroptera) (Curry & Baird, unpublished data). Ahernatively, the idea that dispersal limitation creates Jarge-scale spatial structuring in aquatic insect assemb1ages may be incorrect. In a meta-analysis of dispersal limitation in aquatic organisms, Shurin et al (2009) found that macroinvertebrates detmnstrated weak spatial autocorre1ation re1ative to fish, and were statistically indistinguishable from zoop1ankton, which are believed to experience ve:ry little dispersal 155 limitation (De Meester et al 2002, Havel & Shurin 2004). The results of our variance partitioning analysis support their inference that dispersal rates in benthic invertebrates are sufficiently high as to minimize spatial structuring and allow niche-driven structuring to dominate. Understanding how species riclmess and assemb1age composition are structured across 1andscapes and regions is crucial for both monitoring and conservation applications. The existence of dispersal-driven assemblage structure in particu1ar could complicate the analysis of aquatic biomonitoring data, as the composition of local assemb1ages may be decoupled from niche constraints (Brown et al 2011 ). While the results of this study indicate that spatial structuring of genus-level assemb1age patterns for aquatic insects is relatively weak, there remains a considerable amount of mexp1ained variation both at the major drainage and sub drainage scale. While it is possible that this variation could be attributable to mnneasured environmental variables, there is also a possibility that spatial structuring at smaller scales exists. Nevertheless, spatial variables should be considered during the development of reference condition models to accomt for any spatial structure that is detectable. Addressing the shortcomings of current approaches will ultimately require tmre explie it hypotheses regarding insect dispersal to be developed and used to predict spatial structures. While eigenvector based methods (ie. PCNM, AEM) may provide greater exp1anatory power in some instances, these variables are rarely linked with actual dispersal behaviour or other spatial processes, particu1arly when the spatial distnbution of 156 samples is irregu1ar and the interpretation of scale is less straightforward (Borcard et al 2004). In cases where they do exp1ain a significant component of assemb1age variation, eigenvector predictors may be a useful tool for hypothesis generation Analyses that combine explicit hypotheses regarding dispersal-driven structuring with different fimctional and taxonomic groups and different network architectures could provide greater insight into the extent and scale dependency of spatial structuring in assemb1ages of riverine organisms. Acknowledgements The following CABIN partners provided/arranged access to data: Lesley Carter, Daniel Pouliot, Eric Tremb1ay, Dan Mazerolle, Rosie Smith, Darroch Whitaker, Dave Cote, Levi Cliche, Noah Pond, Ky1a Brake, John Bailey, Robert Thomson, Nathalie Lowcy, Shelley Humphries, Garry Scrimgeour, Leon Gaber, Deb Epps, Danny St. Hi1aire, Rosie Bar1ak, Dennis Einarson, James Jacklin, Kirsten Heslop, Chris Swan, Kym Keogh, AJ Downie, Greg Tamblyn, Lisa Tonmski, Jolene Raggett, Michael Sokai Stephanie Strachan, Basia Wojtaszek, Nancy Glozier, Tim Pascoe and Christine Drake. Canadian National Parks: Kejimkl.tj ik, Kouclnbouguac, Fundy, Cape Breton High1ands, Gros Mome, Terra Nova, Tomgat Mountains, Mountain Parks, N ahanni, Gwaii Haanas, Bruce Peninsu1a, Pukaskwa and Boreal P1ains. Provincial and territorial govermrents: Newfoundland and Labrador, Yukon, British Colwnbia 157 Environment Canada: Atlantic, Prairie and Northern, Pacific and Yukon regions. 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Note that data are missing for sotre sites depending on study objectives, budget and changes in protocol Details regarding units and ireasureirent trethods can be futmd in the CABIN Field Manual: wadeable stream.5 (Environment Canada, 2012) Category Parameters Primary site data latitude, longitude, elevation, ecoregion, stream order, surrounding 1and use, Reach Characteristics habitat types (riffle, rapid, run, pooQ, dominant habitat type, canopy coverage, macrophyte coverage, riparian vegetation (rems/grasses, slnubs, deciduous trees, coniferous trees), dominant vegetation, periphyton coverage Water Chemistry temperature, dissolved oxygen, pH, specific conductance, turbidity, total suspended solids, Nitrogen (IN, N03, NOs, NH4), Phosphorus (1P, DP, P04), hardness, alkalinity, major anions/cations Substrate Characteristics Wohnan Dso, Wohnan Dg, dominant substrate class, second dominant substrate class, embeddedness, surrounding material size (Wentworth scale) ~ Channel Characteristics bank.full width, wetted width, bank.full-wetted depth, velocity (avg./max.), depth (avg./max), discharge, V'I slope Table 5-2: Pearson product-moment corre1ations between Simpson's Diversity or estimated richness (Chao 1) and Jatitude, longitude, altitude, pH and wetted width. Area/Measure Df t R p National (S): 1459 Latitude -2.99 -0.08 0.003 Longitude 5.99 0.15 <0.0001 Altitude 1.93 0.05 0.054 pH -4.00 -0.10 <0.0001 wetted width 0.22 0.006 0.83 Pacific Drainage (S): 625 latitude 3.71 0.15 0.0002 0\ -0\ longitude -1.10 -0.04 0.27 altitude 4.22 0.17 <0.0001 pH 1.41 .06 0.16 wetted width -0.45 -0.02 0.66 Pacific Drainage (Chaol): 300 latitude -3.16 -0.18 0.002 longitude 1.95 0.11 0.052 altitude 0.30 0.02 0.76 pH -0.056 -0.003 0.95 wetted width 1.10 0.06 0.27 Table 5-3: Variance partitioning results for the Pacific Drainage and constituent sub drainages. Fractions are [a] date (regular) or year (italics) [b] purely environmental, [c] purely spatiai [d] date or year*environment, [e] environment*space, [t] date or year*space, [g] date oryear*environment*space. Significance for testable fractions [a, b, c] based onpennutational ANOVA bold: p<0.05. Drainage Environment Space Sites [a] [b] [c] [d] [e] [f] [g] residual explained AVG_SWPE PACIFIC SC AREA xy, }(-y, xy2 622 0.098 0.0095 0.025 0.87 0.13 AVG- MAX- TEMP Skeena- AVG SWPE 'lt'y 79 0.040 0.060 0.0039 O.Oll 0.014 -0.0018 0.0007 0.87 0.13 Coast [l] WC ELEV Queen MACROPHYfE Charlotte WC SWPE 'lf • }(- 40 0.018 0.082 0.021 -0.0035 0.0098 0.016 0.0019 0.85 0.15 Islands [2] SUBCATI SC AREA Nechako WC_ELEV 'lt'y 45 0.0088 0.055 0.018 0.023 -0.003 0.035 0.037 0.83 0.17 [3] lo-" AVG- MAX- TEMP °'...... :J Upper SC_AREA xy3, y2 35 0.030 0.075 0.018 0.010 0.021 -0.0003 0.015 0.83 0.17 Fraser [4] AVG_SWPE Southern SC AREA Coastal y,y3 28 0.11 0.10 0.11 0.68 0.32 AVG SWPE Waters [5] WC ELEV Thompson AVG-SWPE y2, y, y3 61 0.010 0.096 0.014 -0.0027 0.029 0.015 0.036 0.80 0.20 [6] PCT-AGR AVG- MAX- TEMP Lower SC AREA xy2' 'lt'y, y, 89 0.079 0.035 0.11 0.77 0.23 Fraser [7] PCT URBAN xy AVG PRECIP SC AREA Vancouver AVG SWPE X 39 0.11 0.015 0.016 0.86 0.14 Island [8] PCT -AGR Skagit [9] SC_AREA }f 20 0.16 0.11 -0.0038 -0.023 -0.0092 0.048 -0.0070 0.73 0.27 Columbia AVG_MAX_TEMP, xy, ~y 147 0.026 0.063 0.015 0.016 0.038 0.0003 0.038 0.80 0.20 [IO] SC_AREA '""'"' °'00 Table 5-: Results of variance partitioning analyses in recent aquatic insect surveys. Taxa Location Approximate Sites Habitat Space*Habitat Space Total Author Area (km2) Trichoptera NB, 4,400 34 0.067 0.015 0.006 0.18* Cuny et al Canada 2012 in prep. Odonata 0.17 0.001 0.01 0.18 Macroinvertebrates Finland 170 48 0.092-0.21 0.018-0.098 0.0055-0.0056 0.12-0.34 Heino 2012 Macroinvertebrates Finland 60,000 172 0.163 0.272 0.027 0.46 Astorga et al 2011 Trichoptera East-central 6,500 25 0.32 0.24 0.043 0.60 Galbraith et Ok1ahoma, al 2008 USA °'"'"'"' Ephetreroptera Finland 2,173 34 0.23-0.32 n/a n/a 0.23-0.32 Heino & "° Mykra 2008 Plecoptera 0.165 0.027 0.032 .224 Trichoptera 0.13-0.22 n/a n/a 0.13-0.22 Chironomidae 0.103 0.022 0.029 .154 Trichoptera Brazil 100 27 0.073 0.014 0.024 0.111 Landeiro et al 2011 Trichoptera Belgium 13,500 55 0.37 0.087 0.125 0.572 Gombeer et al 2011 Trichoptera Brazil 10,000 89 0.11 0.095 0.038 0.243 Landeiro et al 2012 l} _;·--: ( ~~ __j, ..,··\. /)\t_ \ 0 1,000 2,000 km Figure 5-1: Distnbution of CABIN sampling locations. Samples included m this study are highlighted in red. 170 / / / / ______· .. I ,' / I _.,..:r--·;: ...... __ --- t- - - .. I ~\ ... ~. . Figure 5-2: Distnbution of study sites in the Pacific Drainage Area. Sub drainages included in variance partitioning are highlighted in yellow. (1- Skeena-Coast, 2-Queen Charlotte Islands, 3- Nechako, 4- Upper Fraser, 5-Southem Coastal Waters, 6- Thompson, 7- Lower Fraser, 8- Vancouver Island, 9- Skagit, 10- Columbia - U.S.A.) 171 (a) (f} 0 ~ a) 0 ci flD I0 0 0 0 co, ci 0 v c:i N ci r=0.15 45 50 5:5 eo 65 70 50 52 54 58 eo Latitude {degrees) Latitude {degrees) (b) (g) ID ~ c:i 0 ~ 0 0 00 Oo (I) 0 0 0 N N 00 0 0 r=-0.004 c:i O r=0.15 ci i------.:8~--....----,-----' -140 -120 -100 -80 -135 -130 -125 -120 -115 Longitude {degrees} Longitude {degrees} 8 0§0 g (d) ogP~ (i) ID a) 0 099 c:i 00'0~ c:i 0 Q) 0 00° {§' ~ 0 a 0 0 Cl) 0 ~ ~ 0 0 a 3 4 5 6 7 8 9 10 5 6 7 8 9 pH pH (e) 0 0 {j) 0 0 ID CD 0 c:i 00 ci 0 0 0 flD ci 0 0 v 0 0 0 0 ci 0 0 N ~ r=-0.006 r=-0.02 0 ci '--,-'"'----,..--....-----~--.----,,---..,.. 0 50 100 150 200 250 300 350 0 50 100 150 200 250 300 350 Wetted Width {m) Wetted.Width Figure 5-3: Correlations between Simpson Diversity and selected environmental variables for the national dataset (a-e) and the sites within the Pacific Drainage (f-j). 172 (a) ;2- 0 ~- 0 0 0 0 ~- 0 O 0 0 0 0 0 0 0 0 0 0 ~- 0 O 0 oO O ~ 0 ~- 080 0 ~ o~ oo),;o ~~,~ ~ ~~~ -~ ~~o O r=-0.18 :! .....1-----i----1.------' :~ .__i~r~__ ...,.1 _____ 1____ 1.----- 50 ~ M ~ ~ ~ Latitude (degrees) (b) f2 0 0 (0 0 0 0 0 0 lD 0 (§) 0 0 0 0 ('I') 0 0 Oo 0 0 0 0 0 0 N c° 8o 0 0 0 0 0 0 0 0 0 0 r=0.06 0 50 100 150 200 250 300 350 Wetted Width {m) Figure 5-4: Corre1ation between estimated genus riclmess (chao) and Latitude (a) and Wetted Width (b) for the Pacific drainage. 173 Avg. DSM LJ <0.55 0.55 - 0.60 • 0.60 - 0.65 - 0.65-0.70 - 0.70-0.75 - 0.75 - 0.80 - >0.80 13 0 Figure 5-5: Average distance to centroid values for Water Survey of Canada sub drainages. 1- Porcupine, 2- Peel and Northwestern Beaufort Sea, 3- Central Yukon, 4- Upper Yukon, 5- Stewart, 6- Pelly, 7- Alsek, 8- Headwaters Yukon, 9- Lower Liard, 10- Upper Liard, 11- Nass Coast, 12- Skeena-Coast, 13- Queen Charlotte Islands, 14- Nechako, 15- Central Coastal Waters ofB.C., 16- Upper Fraser, 17- Northern Newfoundland, 18- Southern Newfoundland, 19- Southern Coastal Waters ofB.C., 20- Saskatchewan, 21- Thompson, 22- Lower Fraser, 23- Vancouver Island, 24- Skagit, 25- Bow, 26- Cape Breton Island, 27- Columbia (U.S.A.), 28- Lake Winnipegosis and Lake Manitoba, 29- Upper South Saskatchewan, 30- Assimboine, 31- Gulf of St. Lawrence and Northern Bay of Fundy (N.B.), 32- Abitibt 33- Missinaibi-Mattagamt 34- Bay of Fundy and Gulf of St. Lawrence (N.S.), 35- Southeastern Atlantic Ocean (N.S.), 36- N ortheastern Lake Superior 174 (i) ...... • v•• ·~llt> Avg. dsm - "Ci.""'O J'IO - u/111$ <0.55 - Q.UUO - Q.H1¥J 0.55 - 0.60 - 0.60 - 0.65 - 0.65 - 0.70 - 0.70-0.75 - 0.75 - 0.80 - 0.80 - 0.85 J 27 ,· 52 500 ' -"'V ':)~ Avg. dsm (ii) <0.55 - 0.55 - 0.60 • 0.60-0.65 - 0.65 - 0.70 - 0.70 - 0.75 - 0.75-0.80 - 0.80 - 0.85 ) ;;;.I'~ -~- ---- f • I ,( f ,/ 250 500 km I Figure 5-6: Average distance to centroid values for Water Survey of Canada sub-sub drainages. 1- Upper Peel, 2-Central Yukon- Sixty Mile, 3-Klondike,4- Lo'M!r SteW 175 (a)~- (d) ~- 0 0 0 0 0 ~- 0 0 0 0~- 0 0 0 0 O OS oi 0 0 0 6)(o 0 0 Oo 0 rP 0 oO ~oO oo 0 "'=- 0 0 "'=- 0 0 0 0 0 0 0 0 fl/J ooo 0 0 0 0 0 0 0 0 (j) 0 0 0 0 fl/J 0 ~- 0~- 0 0 0 0 0 0 0 oOO 0 0 0 IQ_ 0 r=0.29 IQ_ 0 r=0.21 0 0 I I I I I I I I I I I I I 0.2 0.4 0.6 0.8 1.0 1.2 0.2 0.4 0.6 0.8 1.0 1.2 1.4 CV Altitude CV Altitude (b} ~- (e) ~- 0 0 0 0 0 ~- 0 0 0 0~- 0 0 0 0 0 0 0 o 00 0 0 olJ> 0 0 0 0 0 0 0 0 0 0 0 0 o ~o 0 0 0 0 0 0 ":_ 00 "'=- 0 0 0 0 0 0 00 0 0 0 0 00 0 0 0 0 0 o ~o 0 0 0 0 00 ~- 0~- or:P 0 0 0 0 0 IJ) 0 0 0 0 IQ_ 0 r=0.07 IQ_ 0 r=0.13 0 0 I I I I I I I I I I I 0.05 0.10 0.15 0.20 0.02 0.04 0.06 0.08 0.10 0.12 0.14 CVpH CV pH (c) ~ {f) ~- 0 IX) 0 0 0 0 0 0 oo 0 0~- 0 0 0 ci 0 0 0 0 i 0 0 rP 0 0 ii 0 0 0 0 .... i 08 ° 0 0 ":_ 0 0 00 0 0 rP 0 ci 00 0 0 0 o o rP 0 0 0 0 0 0 I (lO 0 ~- Q) @, 0 0 0 0 ci 0 fl/J 0 0 0 Q) 0 r=0.47 IQ 0 r=0.54 IQ_ 0 0 0 I I I I I I I 0.1 0.2 0.3 0.4 0.5 0.6 0.1 0.2 0.3 0.4 0.5 0.6 0.7 CV {log) Wetted Width CV {log} Wetted Width Figure 5-7: Corre1ations between average distance to centroid values and the coefficient of variation for altitude, pH and (log) wetted width. Sub-drainage units (a-c), sub-sub- drainage units (d-f). 176 6. General Discussion and Conclusions The two principal objectives of my thesis were to llllderstand the suitability of invertebrate biommitoring samples for the wider assessment of riverine insect biodiversity and to investigate the role of dispersal in structuring riverine insect assemb1ages. Articles 1 and 2 addressed the first objective by descnbing cross-habitat and cross-tax.on congruence of key biodiversity measmes: assemb1age composition, tax.on richness, and assemb1age variation Article 3 addressed the second objective by testing the prediction that a group with overall poor dispersal capacity (Trichoptera) would demonstrate greater spatial structlll'ing than a group with strong dispersal capacity (Odonata). The final article tested the hypothesis that dispersal-driven structuring of benthic insect assemb1ages would be stronger at Jarger spatial scales due to the increased influence of dispersal limitation Biomonitoring programs that focus on riflles inaccmately descnbe patterns of biodiversity in riparian wetland mesohabitats. Ahhough riffles and riparian wet1ands supported similar numbers of fumilies overall, the composition of their assemb1ages differed markedly and riparian wetlands demonstrated greater assemb1age variation (Cuny, Cuny & Baird 2012). Additionally, their spatial patterns of tax.on richness and assemb1age variation were poorly corre1ated. While the fi.mctional importance of riparian wet1ands in headwaters and small rivers is poorly llllderstood, these mesohabitats are believed to p1ay an important role in maintaining the biodiversity of riverine ecosystems 177 (Mensing, Ga1atowitsch & Tester 1998). This is not to suggest that riftles or other instream mesohabitats are inconsequential, rather that wider protection of riverine macroinvertebrate biodiversity requires an approach that accounts for differences m biodiversity am:mg mesohabitats. Better llllderstanding the biodiversity of riparian wetlands and their fimctional role in riverine ecosystems involves accurately re1ating their physical and chemical dimensions to assemb1age structure and biodiversity. The habitat variables collected to characterize riparian wetlands were identical to those collected for riffles, save for those that could not be measured in a lentic waterbody (e.g. flow velocity, discharge). There were no statistically significant re1ationships between these variables and benthic invertebrate assemb1age structure in riparian wet1ands (Curry et al 2012a, Figure 2-4b). Assemb1age variation was much greater in riparian wet1ands, possibly because these mesohabitats incorporate a greater degree of environirental variation than riffles do. Habitat characters that could be measured in m:>re detail include depth profiles, macrophyte composition and density, substrate composition and dimensions, hydroperiod and the frequency of connection to the main channel Developing a better understanding of the environmental drivers of assemb1age variation in riparian wet1ands would allow m:>re formal c1assifications to be developed and incorporated within existing river habitat c1assification systems. Multi habitat surveys conducted across three 5th order catcl:nrents (sensu Strahler 1952) revealed inconsistent patterns of taxon richness and beta diversity among Odonata 178 and Trichoptera. Trichoptera were expected to dermnstrate greater assemb1age variation across sites due to their weaker overall dispersal capacity. However, Trichoptera assemblage variation was statistically similar to that of Odonata in two catchments (Renous and Little Southwest Miramichi), and significantly lower in one catchment (Nashwaak) (Curry, Zhou & Baird 2012, Figure 2). When differences in abundance are accounted for, no differences in assemblage variation were observed in any of the catchments (Curry et al 2012b, Figure 2), and cross-taxon correlations in taxon richness and distance to centroid values were stronger (Curry et al 2012b, Table 2, Figure 3). If dispersal is an important driver of assemb1age structure in riverine ecosyste~, then purely spatial variables should explain a smaller component of assemblage variation in the strongly dispersing Odonata than the small-bodied Trichoptera. I found very little evidence to support this hypothesis, as both linear and network-based spatial predictors explained a relatively small component of assemb1age variation in both Odonata and Trichoptera (Chapter 3). This resuh supports a view that macroinvertebrate groups demonstrate broadly similar levels of spatial autocorre1ation (Smuin et al 2009). The degree of Trichoptera assemblage variation exp1ained by spatial predictors was similar to that observed in other recent studies conducted across a range of colllltries and spatial scales (Galbraith et al 2008, Heino & Mykrii 2008, Gombeer et al 2011, Landeiro et al 2011, 2012). In Chapter 4 I attempted to quantify the degree of spatial structuring in benthic insect assemblages at larger spatial scales using samples from the CABIN database. However, the component of variance explained by purely spatial fractions was negligible for the entire Pacific Drainage and ten of its constituent sub drainages, with the 179 exception of the Southern Coastal Waters. Ultimately, this pattern suggests that dispersal driven processes may not be a strong structuring force across broad areas in benthic insect assemb1ages. Failure to detect spatial structuring in benthic insect assemb1ages coukl be exp1ained in a number of ways, though it is not wholly inconsistent with results from recently published work (Chapter 5, Table 5-4). Ahhough Odonata may have greater capacity for dispersai they may not utilize this capacity. Various species of Odonata observed in fiekl-based studies have dennnstrated site fidelity (Switzer 1993). Cost benefit studies of odonate dispersal behavior are Jacking, though McCauley (2010) fmmd that dispersing male Pachydiplax longipennis were smaller than non-dispersers. Even if out-of.site dispersal is a "Jast resort" for a majority of individuals, it is likely that it still occurs as males seek lllldefended territories and females seek out high fitness males. Propagule density may also p1ay a role; even if odonate species can and do disperse greater distances, they may not colonize all suitable sites due to the re1atively small number of offspring, Allee effects and chance colonization events. Alternatively, Trichoptera tray be better dispersers than their re1atively small body size suggests (Wilcock, Hildrew & Nichols 2001 ). Many Trichoptera species emerge en masse, and regional weather conditions could potentially result in the spread of species over large distances (Corbet 1964). Trichoptera larvae are also susceptible to drift, suggesting the ability to spread rapidly through a river network once it is colonized (Brittain & Eike1and 1988). 180 Approaches to understanding and testing dispersal's role in structuring aquatic insect assemb1ages can be broadly categorized as top-down or bottom-up. Top-down approaches, such as that employed in this thesis, tend to be conducted at catchtrent or Jarger spatial scales (ca. > 100 km2) and involve corre1ational tests of spatial structuring ( e.g. variance partitioning) or turnover in assemblage composition along spatial gradients and based on field surveys (e.g. Brown et al 2010). These studies deal with multiple species and in some instances environmental gradients represent proximate rather than ultimate causes of species presence/absence. For instance, a relatively Jarge component of assemb1age variation was exp1ained by sub-catchment area for both Odonata and Trichoptera (Chapter 3) and the wider benthic insect connnunity (Chapter 4). It is I1X>re likely that factors correlated with sub-catchment area such as habitat complexity and the distribution of flow microhabitats (Vinson & Hawkins 1998, Brooks et al 2005), predators (Wellborn, Skelly & Werner 1996) and basal resources (Vannote et al 1980) are the actual mechanisms driving the composition of benthic insect assemb1ages. However, many of these factors are difficult to measure directly in a rapid bioI1X>nitoring protocol The bulk of the literature regarding the spatial structuring of aquatic insect assemb1ages has employed what coukl be considered a top-down approach. In contrast, the bottom-up approach focuses on the development of spatially explicit I1X>dels that include parameters for dispersal behaviour to predict the spatial scales where structuring of aquatic taxa shoukl be observed. Rather than using broad field surveys, the bottom-up approach relies on experimental techniques, detailed observation 181 of organism behaviom and habitat preferences, usually conducted over small spatial scales (10 m2 - 10 km2) and focused on one or a small number of species. While less connmn, and employing different statistical m>dels than the top down approach, some of these studies find that dispersal can influence local assemb1ages ofbenthic insects (e.g. Lancaster, Downes & Arnold 2011 ). If studies conducted across a Jarger spatial extent are to provide insight into the role which dispersal may play in structlll'ing benthic insect assemb1ages, then they require some prediction regarding the scales where such structlll'ing might be observed. Biom>nitoring studies tend to stress the location of reference sites in separate stream segments or stream tnbutaries to avoid pseudoreplication (Reynoldson et al 1997), resulting in a re1atively Jarge sampling grain. A sampling design such as this would fail to detect dispersal-driven structlll'ing ofbenthic insect assemblages if it occms at smaller spatial scales. Studies ofbenthic invertebrate assemblage variation at smaller scales tend not to look at patterns in relation to dispersal processes; rather, they have sought to quantify the relative magnitude of assemblage variation within vs. between patches. Downes, Lake & Schreiber (1993) studied the relative degree of variation in macroinvertebrate density and composition across several scales (within riffle, between riffles within streams and between streams). They found that the density of individuals varied significantly am>ng stones within riffles, and both density and composition could vary am>ng riffles within sites. In a similar study, Boyero (2003) folllld observed the greatest degree of assemb1age variation am>ng samples within a section of riffie and am>ng riffles within a site. In these studies, analysis was limited to assessing the 182 variability of aquatic invertebrate assemb1ages at re1atively small scales, as opposed to detecting spatial autocorrelation, patterns of distance decay or partitioning assemb1age variation among spatial and environmental components. Effort standardized samples employed by biommitoring protocols such as CABIN ostensibly integrate between-patch variation within riffles by using a travelling kick sample. However, such a sampling teclmique does not pennit the analysis of spatial structuring among patches within a riffle, or among riffles within a site. Going forward, a key question appears to be whether dispersal-driven spatial structuring in aquatic insect assemb1ages exists at small scales. Addressing this question will require a spatially explicit sampling design with replication at key spatial scales (ie. muhip le riflle s within a site, muhip le sites within a segirent or tributary). A number of such datasets already exist for benthic macroinvertebrates (see examples above). If reanalysed using a variance partitioning approach with spatial and environmental predictor matrices, these studies could clarify the extent to which small-scale variability in macroinvertebrate assemblages is spatially structured. To meaningfully conduct such an analysis, the sampling protocol would also require detailed measurement of microhabitat or patch-scale environmental variation, an approach that may be impractical within the context of present biommitoring programs. Replication of such a nested sampling approach across larger spatial scales (e.g. individual 5th order catchments, or using Water Survey of Canada's sub-drainage or sub-sub-drainage definitions) would pennit the relative contribution of spatial and environmental components to be accurately 183 measured, and would permit the effect of sampling grain on observed patterns of biodiversity to be tested. Spatially explicit models of riverine insect dispersal integrated with predictive models of assemb1age composition could be used to generate predictions regarding the strength of dispersal-driven structuring at different spatial scales. Dispersal behaviour is inherently complex, and involves both spatial and temporal parameters. A criticism of spatial predictors such as principal coordinates of neighbour hood matrices (PCNM, Dray, Legendre & Peres-Neto, 2006) asynnnetric eigenvector maps (ABMs, B1anchet et al, 2008) is that selected eigenvectors which exp1ain a significant component of assemb1age variation are not necessarily re1ated to a biological mechanism (Landeiro et al, 2011 ). However, inspection of these eigenvectors does allow ecologists to hypothesize what sort of dispersal processes (or underlying but l.lllllleasured environmental factors) could create spatial structure, and design studies or experiments to explicitly test those hypotheses. Ah:hough smaller-scale spatial structuring may be important to understanding the composition and variability of aquatic insect assemb1ages, it does not preclude the existence of Jarger-scale structuring. Ahhough I was unable to detect a significant component of variance attnbutable to spatial factors in the Pacific Drainage, there was a re1atively Jarge component of llllexp1ained variance that could be attributable to trore complex spatial variables or unmeasmed environmental variables. Also, given the use of genus-level data, the variance of the site x taxa table was lower than what would be observed if specirrens were identified to species level 1he geographic range of a genus 184 is inevitably greater than or equal to its constituent species. Historic processes or events that could generate Jarge-scale structuring of benthic insect assemblages, such as speciation (Donnam et al 2007), congeneric differences in the timing of post-g]acial recolonisation (Dehling et al 2010) or inter-basin dispersal events (e.g. MacNeale, Peckarsky & Likens 2005) would go lllldetected with a genus-only dataset. Differences m habitat preference among congeneric species would also be impossible to detect with a genus-only dataset. Designing aquatic insect surveys that meet the requirements of both biomonitoring and biodiversity assessment requires some consideration of how to assign limited resources to the sampling of different mesohabitats, as well as replication within and between sites. Aquatic biomonitoring programs collect one sample per site, and use a stratif1ed random sampling design to choose reference sites among the pool of candidate sites. For biomonitoring protocols such as CABIN, both local (within site) and regional ( across sites) taxon accumu1atio n curves :fail to reach an asymptote, a pattern that is corrnnon to all invertebrate surveys (Magurran 2004). A subsample of 300+ individuals JS unlikely to accurately reflect richness at species-level taxonomic resolution, even with as few as 100 species present at a site. 1his may be less of an issue for biomonitoring, where analyses are often performed using family- or genus-level data. While assessing species richness at both site and catchment scales is difficult, estimating beta diversity (sensu Anderson et al 2006) for a given area and sampling grain may be more feasible. Although complete saturation of the species accumu1ation curve is 185 not attained, the rate at which new taxa are encountered with the inclusion of additional sites begins to decrease. New samples tend to add increirentally less variance to the total variance of the site x taxa matrix and, as a resuh, the average distance of samples to the centroid in muhivariate space increases at a rapidly diminishing rate. Where there are a finite number of possible samples, the average distance to centroid for the entire region could be estimated. 1be sample size at which the observed average distance to centroid approximates the overall value may be re1atively small. Figure 6-1 presents a resampling of the Odonata and Trichoptera datasets from the N ashwaak river catchment; while average distance to centroid values vary at a given sample size, the tredian rapidly approaches the overall trean of the full dataset. Implications for professional practice A trotivation for much of my thesis was to understand the utility of aquatic biotronitoring information, particu1arly that collected by CABIN, in biodiversity assessirent and systematic conservation planning. Although the structure of the required information is the same (site x taxa matrix), the distnbution of sites, and the targeted taxonomic groups may be different. I observed that riflles were a poor indicator of the composition, richness and assemb1age variation in riparian wetlands, which suggests that, at best, riffles provide an incomplete picture of riverine biodiversity. If used as the sole source of information in biodiversity assessirent or systematic conservation planning, this could lead to skewed estimates of aquatic biodiversity, or inadequate protected areas 186 for aquatic biodiversity. Management actions, such as the regulation of dam outflows are often monitored to evaluate their effectiveness, but if biomonitoring is based exclusively on one mesohabitat it may present an inaccurate picture of overall biodiversity trends. A goal of riverine restoration projects is often the reestablishment of pool-riffle sequences (Gregory et al 2006), and while the importance of floodplain connectivity is recognized in Jarge rivers (Buisje et al 2002), it is llllClear whether practitioners recognize the importance of riparian wet1ands for aquatic insect biodiversity in small rivers and headwaters. Groups such as Trichoptera, which are typically abundant in the riffles of tmdegraded stream;, may disp1ay different spatial patterns of riclmess and assemblage variation than Odonata, whose diversity is poorly represented in riffles. However, these patterns may reflect differences in the local abtmdance of each group. When compared on the basis of equal abundances, their patterns of diversity were simi1ar. To date, investigations of cross-taxon congruence in biodiversity patterns have not accounted for differences in abundance, and this may explain the inconsistent results in previous studies of cross-taxon congruence in the biodiversity of aquatic organisms. Indicator taxa or groups are often employed in monitoring and conservation, and the results of my study point to the importance of accotmting for differences in abundance among taxa. Even if equivalent sampling effort has been expended in the assessment of each group, it cannot be ass~d that the efficiency of sampling is equal across groups. 187 Although purely spatial structuring was weak across the New Bnmswick Odonata/frichoptera dataset and the genus-level Pacific Drainage dataset obtained from CABIN, it does not rule out the possibility of small-scale spatial structuring in riverine insect assemb1ages driven by dispersal However, it is possible for the spatial arrangement of sites to be accounted for in the developm:mt of reference condition models. Spatial predictors, whether based on raw coordinates, cubic trend-surface regression, or eigenfunction approaches (PCNM, AEM, Moran's Eigenvector Maps) could be included in model development, and in certain cases may identify spatial patterns in macroinvertebrate assemb1age composition unre1ated to environmental :factors. While the CABIN program relies on a standardized protocol to collect samples of the benthic corrnmmity, the way this information is processed in the Jab can limit its use in biodiversity analyses. Although the field sample is always time limited (3 minutes), it may be subsampled in the Jab using a 100-cell Marchant Box (Marchant 1989). Macroinvertebrates from successive cells are identified mtil 300 individuals are encountered; the rest of the specimens in that cell are identified, but subsampling is terminated. This can result in different levels of subsampling effort both within and between studies. Although re1ative abmdances can be extrapo1ated to the whole sample, uneven subsampling renders comparisons of riclmess and composition among samples more difficuh as mcommon taxa will be better represented in some samples than others. Another issue is patchiness within subsamples. In samples with many individuals (e.g. > 10,000), as few as 1-3 cells may be identified. In addition to this, less common but Jarge 188 specnnens (e.g. mature Odom.ta) may occupy a significant portion of the cell vohnne, thereby excluding other invertebrates from the cell and potentially introducing bias into extrapolated estimates of abundance. Adjustrrents to the way samples are processed in the laboratocy may address some of these problems. First, a minimum nmnber of cells could be identified for each sample, thus preserving equal sampling effort across samples. Alternatively, the threshold for stopping the subsample could be raised from 300 individuals to permit better sampling of uncorrnnon taxa. Second, the composition of each cell could be recorded separately, which when combined with a minimum subsample would allow direct comparisons of richness and composition across samples. It would also permit the calcu1ation of standard deviation and confidence intervals for estimated abtmdances of individual taxa. Finally, large specimens such as mature odona.te or plecopteran (stone:fly) Jarvae could be removed prior to sample processing and identified separately; the resuhing sub-list of taxa could be randomly subsampled in the same proportion as material processed via the Marchant Box. The above amendments to Jaboratoty procedures could provide more comprehensive taxonomic information for both biomonitoring and biodiversity assessment. However, they may be rendered tmnecessacy by the rapid advancement of molecular identification techniques that will provide higher resolution taxonomic data, and may eventually reduce the overall cost of obtaining a taxa list for given site. This is an important advancement, as it allows for the collection of more specimens from a site, 189 or the collection of samples from a greater number of sites. If a Canada-wide database of aquatic insect biodiversity is to be established and maintained, it will require teclmological advancements that lower the cost associated with processing samples. Addressing the loss of biodiversity from :freshwater ecosystems requires solutions based on somd science. Biommitoring programs, while limited by their individual habitat and taxonomic focus are nevertheless the principal somce of biodiversity information for :freshwaters. The growth of molecu1ar methods for identifying organisms offers much promise for providing finer resolution information, and may greatly improve the suitability of biomonitoring data for biodiversity assessment purposes. Aquatic science should focus on identifying the general mechanisms that lead to contemporary patterns of biodiversity, and mderstand how ecological processes such as dispersal may alter om interpretation ofbiomonitoring and biodiversity assessment data. 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Error bars reflect standard error. 195 max, 0.21) 0.15) 0.26) 0.11) 1.9) 34.4) 33.3) (min, 1.17, 0.48, 0.85, 5.1, 100, 75, 0.9) deviation} 6, Range (0.03,0.37, (0.13, (0.06, (7.1, (0.33, (0.01, (3.1, (3, 1.28 0.52 0.31 0.22 0.84 27.4 0,1 0,1 20.1 0,1 4.9 0,1 0,1 0,1 0,1 0,1 0,1 standard 0,1 0,1 0,1 Mean, at at a records re1ationships meter. Marsh a along velocity flow measured assessed assessed with presence and of depth locations 10 McBirney Sum Depth 2/3 transect Visually Method Visually species-environment of analysis Errata 7. m m m m's m's Cmmt rrf/s m Presence/absence Presence/absence Units exploratory for max) avg) {V BO) PE) CB) (V (CO) (DE) (Dmax) (BW) (Davg) cm, (WW) FS) cm, cm, measured (LP) Trees Trees depth velocity (Q) measured (>25 Wiclth <6.5 depth GR) velocity (BR) (<25 ( ( Width (SH) (DB) <4nnn, ( Packs variables Maximum Bankfull Maximum Average Average Wetted subIC Discharge Wood(WO) Boulders Debris Cobbles Leaf Bedrock Fines Shrubs Pebbles Deciduous Coniferous Grasses Variables environmental variables composition Riffle vegetation 2 2-1: Hydrologic Substrate Category Riparian Table Chapter ...... \0 °' % Rifile/Run/Pool % Rifile Proportion Visually estinnted 0.41 (0.10, 0.90, 0.23) %Run 0.30 (0, 0.80, 0.30) %Pool 0.29 (0, 0.70, 0.34) % Macrophyte Cover ICmp Proportion Visually estinnted 0.02 (0, 0.10, 0.01) % Canopy Closure !Cea Proportion Handheld densimreter 0.25 (0, 0.87, 0.36) Water Chemistry Temperature (1) oc YSI 556 handheld 16.6 (10.5, 23.9, 4.5) Dissolved 02 (%Sat) Percent saturation multiprobe 1.01 (0.86, 1.04, 0.06) Dissolved 02 (DO) l11!¥L 9.89 (8.15, 11.38, 1.04) Conductivity (Cond) ~/cm 33.9 (14, 62, 17.7) pH (pH) [H+] 7.22 (6.55, 7.66, 0.30) Cl l11!¥L Grab sample 1.81 (0.5, 11.9, 2.64) SQ4 l11!¥L 2.7 (1.8, 3.5, 0.5) NQ3 l11!¥L 0.03 (<0.02, 0.08, 0.02) NH4 l11!¥L 0.05 (<0.01, 0.11, 0.03) 1--' PQ4 l11!¥L 0.002 (<0.001, 0.006, 0.001) '°.....J Mg l11!¥L 0.69 (0.39, 0.97, 0.17) K l11!¥L 0.34 (0.26, 0.41, 0.12) Na l11!¥L 2.04 (1.05, 7 .21, 1.40) Ca l11!¥L 4.76 (1.26, 11.55, 2.54) TOC l11!¥L 7.6 (3.3, 11.6, 2.8) DOC l11!¥L 5.4 (3.3, 9.6, 1.8) 1N l11!¥L 0.09 (<0.02, 0.2, 0.07) TP l11!¥L 0.02 (0.006, 0.031, 0.007) Alkalinity l11!¥L 13.21 (4.41, 29.89, 6.22) Hardness l11!¥L 14.75 (4.96, 32.09, 6.78) Turbidity NTU 0.4 (0.1, 0.8, 0.2) Colour Hazen units 41 (16, 72, 17) Table 2-2: Riverine wetland envirorurental variables measured for e~loratory analysis of s;eecies-envirorurent re1ationsh~s. Category Variables measured Units Method Mean (min, max, standard deviation) Riparian vegetation Grasses ( GR) Presence/absence Visually assessed 0,1 Shrubs (SH) 0,1 Coniferous Trees (CO) 0,1 Deciduous Trees (DE) 0,1 Substrate composition Bedrock (BR) Presence/absence Visually assessed 0,1 Fine sediment (<4mm, FS) 0,1 Pebbles (<6.5 cm, PE) 0,1 Cobbles (<25 cm, CB) 0,1 Boulders (>25 cm, BO) 0,1 Debris (DB) 0,1 Leaf Packs (LP) 0,1 Wood(WO) 0,1 -"°00 SubOC Comt Sum of presence 4.5 (2,7, 1.3) records Riverine wetland Length (OCx) M Measuring tape/meter 21.7 (4, 50) dimensions Width (OCy) m stick 8.5 (1, 20.6) Depth (OCz) m 0. 73 (0.25, 1.6) % Macrophyte Cover OCmp Percentage Visually estn:mted 0.07 (0, 0.5, 0.27) % Canopy Closure OCca Percentage Handheld densiometer 0.40 (0, 1, 0.56) Water Chemistry Temperature (1) oc YSI 556 handheld 16.4 (10.4, 28.3, 4.5) Dissolved 02 (%Sat) Percent saturation multiprobe 0. 72 (0.14, 1.12, 0.30) Dissolved 02 (DO) mglL 7.17 (1.22, 11.3, 3.01) Conductivity µSiem 50.0 (17.7, 118.1, 26.7) pH (pH) log [H+] 7.06 (6.33, 7.53, 0.33) Cl ~ Grab sample 2.7 (0.4, 18.1, 4.5) SQ4 mwL 2.3 (1, 3.8, 0.8) N03 mwL 0.02 (<0.02, 0.07, 0.01) NH4 mwL 0.10 (<0.05, 0.29, 0.076) P04 mwL 0.007 (<0.001, 0.06, 0.01) Mg mwL 0.83 (0.38, 1.24, 0.27) K mwL 0.46 (0.23, 1.16, 0.24) Na mwL 2.63 (1.21, 11.91, 2.58) Ca mwL 5.42 (1.17, 10.03, 2.91) TOC mglL 7.26 (2, 18.4, 4.08) DOC mwL 4.9 (2.2, 10.9, 2.3) 1N mwL 0.17 (0.02, 0.62, 0.16) 1P mwL 0.036 (0.01, 0.094, 0.02) Alkalinity mwL 16.46 (3.38, 29.21, 8.69) Hardness mwL 16.96 (4.82, 28.05, 8.15) Turbidity N1U 2.03 (0.2, 14.7, 3.58) - Colour Hazen units 47(11, 193,48) '° dev.) 393.1) std. 0.07) 0.14) 0.19) 0.08) 0.10) 0.03) 106) 0.12) 0.03) 76, 31.9) max, 0.33, 0.41, 0.83, 1445.1, 0. 0.73, 0.08, 0.30, 0.05, 116, (min, (4.4, (0.02, (0.17, (0, (0.15, (0.09, (0, (0.34, (0.03, (17,310, re1ationships. (15, (2,5,1) 134 0.49 0.09 0.14 0.25 0.58 0.44 0.02 58 0.06 4 253.5 Mean the of .2 as method 9 forest any thinning any use to to to to delineating using prinmy years toolbox 1and species-environment 3 0 3 site NBDNR Strahler of ArcGIS after confluence subject subject where subject designated subject with the the each the using by area area area area area area area settled for analysis and Delineation coniferous preceding using or calcu1ated is activity the SRTM-DTM sites catchment cutting. provided was in calcu1ated the of catchment catchment catchment catchment catchment catchment catchment assessed catchment cutting Watershed of of of of of of of clear exploratory Area coverage 1ayer was agricuhure forest from Analyst between a slope or activity activity 9.2 as for order on upstream Percentage Percentage vegetation clearing Percentage orselective plantation forestry Percentage Percentage forestry Percentage defined Percentage Stream catchment Distance based Obtained Average Catchment Network ArcGIS Method the measured variables Percentage Percentage Percentage Percentage Percentage Percentage Percentage Percentage metres kilometres Units km2 1and-use (Deon) arxl Cutting (Sip) (Fh%) (WA) human (Fr°/o) (Co%) (SO) and confluence Slope Area (Hu%,) Landscape (Ev) (F3%) to (F2%) forestry cover Order forestry 2-3: Conifer Clearing Planting (Fl%) settlement Thinning/Selective Historic Recent Agricuhure Catchment Stream Elevation Distance Catchment Variable Table N 0 0 Chapter 2, Page 36, paragraph #2, line 5: should read "At the site levei riffles contained a greater... " Table 2-1: Table has been amended to include standard deviation Table 2-2: Table has been amended to include standard deviation Table 2-3: Table has been amended to include standard deviation Chapter 3 Page 76, paragraph, line 2: should read ''Plus or minus one standard deviation, we observed an average of 8.4±4.5 odonate taxa ... " 201 Appendix 1 Table 1: Site bl taxa lists for riffles Class/Order Family 5M BL CR DU MA NA NI N2 N3 N4 NS N6 N7 PE RO Tl T2 WI Oligochaeta 53 13 3 I 3 7 20 18 6 4 8 0 37 23 5 5 11 0 Acarifonnes 58 0 6 I 0 8 24 5 11 0 32 7 37 I 3 35 12 38 Cladocera 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 3 Amphipoda 0 0 0 0 0 I 0 0 0 0 0 I 0 0 0 0 0 I Copepoda 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 I 8 0 Ostracoda 0 0 0 0 0 0 2 0 0 0 10 0 0 0 0 0 I 0 Collembola 0 0 0 0 0 I 0 0 0 0 3 I 12 0 I 0 0 4 Coleoptera Dytiscidae 0 0 0 0 0 0 I 0 0 0 0 0 0 0 0 0 0 0 Elmidae 794 63 20 32 22 149 270 125 129 65 112 298 40 93 99 81 41 570 Psephenidae 0 0 32 6 I 1 10 28 52 4 40 16 293 18 0 26 20 0 Diptera Athericidae 11 0 0 2 0 7 0 0 0 0 0 0 79 0 I 3 0 5 N 0 Empididae 3 7 2 3 0 5 IO 3 4 0 18 0 135 0 0 3 7 3 N Tabanidae 0 0 0 0 0 0 0 0 0 0 0 0 13 0 0 0 0 0 Blep hariceridae 0 0 IO 0 0 3 0 5 I 8 0 I 0 0 0 0 I 0 Ceratopogonidae 5 31 6 0 0 9 6 3 10 2 4 3 4 10 5 9 9 0 Chironomidae 356 189 142 48 37 266 539 267 168 86 400 91 1815 199 60 360 442 453 Psychodidae 0 0 0 0 I 0 0 0 0 0 0 0 0 0 0 0 0 I Simuliidae 109 160 3 11 3 5 15 16 9 4 0 12 189 36 20 467 8 214 Tanyderidae 0 0 0 0 0 0 13 0 0 0 0 0 0 0 0 0 0 0 Tipulidae 28 21 0 3 5 14 165 94 45 13 9 19 18 24 7 48 31 IO Hempitera Hebridae 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 Veliidae I 0 0 0 0 0 0 I 7 0 0 0 2 0 0 0 0 0 Megaloptera Corydalidae 21 2 3 4 7 6 I 0 I I 5 I 25 3 9. 6 2 0 Plecoptera Capniidae 50 22 0 4 0 0 0 2 0 4 0 30 0 3 14 2 23 0 Leuctridae 3 16 0 0 I 33 0 0 0 2 42 12 276 0 0 0 2 Nemouridae 6 170 0 0 0 0 0 0 0 0 0 I I 0 7 0 0 130 Chloroperlidae 0 8 8 6 I 16 25 17 22 0 I 2 0 0 16 34 3 4 Perlidae 24 0 12 10 IO 70 25 23 42 9 4 20 0 64 14 96 26 0 0 0 0 0 5 5 0 0 0 0 0 0 0 0 0 0 1 1 0 0 2 2 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 50 50 51 51 1 1 1 1 9 9 0 0 8 8 0 0 1 1 8 8 8 8 6 6 0 0 76 76 68 68 IO IO 46 46 58 58 12 12 40 40 712 712 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 5 5 0 0 0 3 0 0 0 0 0 6 6 3 0 0 3 1 3 3 6 6 0 0 6 0 0 0 0 0 3 0 0 2 2 0 6 6 19 19 20 20 51 51 26 26 256 256 209 209 280 280 475 475 151 151 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 7 0 0 0 0 7 7 0 0 1 1 0 0 0 0 7 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 12 12 26 26 48 48 425 425 2 2 0 0 1 1 0 0 4 4 0 0 2 2 30 30 25 25 21 21 108 108 103 103 542 542 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 0 0 0 0 3 3 0 0 5 5 3 3 0 0 0 2 2 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 4 2 2 1 20 20 15 15 78 78 44 44 101 101 132 132 513 513 1 1 1 1 1 1 0 0 0 0 4 7 7 2 2 0 0 2 1 17 17 IO IO 83 83 25 25 33 33 23 23 36 36 196 196 247 247 0 0 0 0 0 1 1 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 5 0 0 0 0 0 0 0 0 3 0 0 0 7 7 0 0 0 0 0 6 6 0 0 0 0 0 0 6 6 1 0 0 0 12 12 96 96 14 14 IO IO 23 23 21 21 142 142 148 148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 5 5 1 1 0 0 4 4 0 0 0 0 4 4 0 0 0 0 18 18 27 27 54 54 14 14 36 36 13 13 78 78 58 58 141 141 0 0 0 0 0 0 0 0 0 0 0 0 2 5 5 0 0 0 0 6 6 0 0 0 0 6 6 0 0 91 91 IO IO 42 42 39 39 20 20 189 189 136 136 185 185 411 411 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 3 3 6 0 0 0 2 2 0 0 2 0 0 0 0 2 2 0 0 4 27 27 12 12 13 13 12 12 53 53 23 23 65 65 133 133 105 105 104 104 226 226 1 1 0 0 1 1 0 0 0 0 0 0 0 0 6 6 0 0 6 6 0 0 8 8 2 0 0 0 0 0 0 77 77 16 16 12 12 16 16 64 64 77 77 39 39 42 42 152 152 132 132 266 266 803 803 1 1 1 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 6 6 0 8 8 0 0 0 0 0 3 3 5 5 2 2 60 60 79 79 17 17 38 38 44 44 28 28 123 123 363 363 2 2 0 0 0 0 0 0 9 9 1 1 0 0 0 0 0 0 5 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 4 0 0 0 0 0 0 0 0 2 2 0 0 0 13 13 14 14 125 125 0 0 5 5 2 2 0 0 2 2 0 0 0 0 0 1 18 18 21 21 29 29 64 64 13 13 13 13 303 303 1 1 0 0 0 1 1 3 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 3 3 0 0 0 0 0 3 3 0 0 0 7 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 72 72 39 39 14 14 72 72 36 36 26 26 113 113 272 272 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 9 9 2 9 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 1 1 9 9 16 16 13 13 14 14 11 11 11 11 35 35 196 196 1 1 0 0 1 1 4 4 0 0 6 6 0 0 1 1 0 0 0 0 0 0 6 6 8 8 0 0 3 3 0 0 2 2 5 5 0 0 9 9 0 0 0 0 0 0 0 0 4 4 15 15 78 78 98 98 13 13 11 11 20 20 351 351 idae idae sosomatidae sosomatidae idostomatidae idostomatidae droptilidae droptilidae Cordulegastridae Cordulegastridae Siphlonuridae Siphlonuridae Isonychiidae Isonychiidae Aeshnidae Aeshnidae Heptageniidae Heptageniidae Baetidae Baetidae Ameletidae Ameletidae Leptophlebiidae Leptophlebiidae Potamanthidae Potamanthidae Polymitarcyidae Polymitarcyidae Uenoidae Uenoidae Leptohyphidae Leptohyphidae Ephemerellidae Ephemerellidae Ephemeridae Ephemeridae Polycentropodidae Polycentropodidae Philopotamidae Philopotamidae Rhyacophilidae Rhyacophilidae Psychomyidae Psychomyidae Litmephilidae Litmephilidae Leptoceridae Leptoceridae Lep Lep Odontoceridae Odontoceridae Hydropsychidae Hydropsychidae Hy Hy Goeridae Goeridae Glos Glos Helicopsychidae Helicopsychidae Pteronarcyidae Pteronarcyidae Dipseudops Dipseudops Brachycentridae Brachycentridae Apatanidae Apatanidae Perlodidae Perlodidae tera tera hemerop hemerop Odonata Odonata Ep Ep Trichoptera Trichoptera N N 0 0 v.> v.> Corduliidae 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 Gomphidae 1 2 6 3 2 12 11 1 19 5 25 8 31 5 4 51 IO 2 Caloptery gidae 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 Lestidae 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 Bivalvia Sphaeriidae 29 3 1 1 0 0 0 0 0 0 4 8 205 5 4 2 1 3 Gastropoda 0 0 2 0 0 0 3 0 0 9 12 0 17 0 2 0 0 0 Nemata 2 2 0 1 0 0 35 16 29 0 50 0 47 0 1 9 0 0 Turbellaria 0 0 1 0 0 0 1 0 0 0 5 0 4 0 0 1 0 0 N 0 ~ Table 2: Site x taxa lists for ti£arianwetland habitats Class/Order Family SM BL CR DU MA NA Nl N2 N3 NS N6 N7 PE RO Tl T2 WI Hirudinea 6 0 0 0 2 0 8 22 2 4 1 12 0 2 2 5 0 Oligochaeta 9 10 2 0 9 11 18 28 9 34 5 17 78 69 3 18 0 Acarifonnes 1 1 1 2 1 1 15 9 14 37 86 14 21 3 1 0 0 Cladocera 0 0 0 0 0 0 12 3 0 172 0 49 10 8 2 11 37 Amphipoda 9 1 30 0 0 0 63 25 0 4 0 0 0 72 0 0 12 Copepoda 0 1 1 0 11 1 34 0 6 · 112 78 29 53 13 0 9 3 Ostracoda 1 1 1 0 0 0 10 0 13 124 68 0 1 3 0 253 21 Collembola 0 0 0 0 5 0 15 0 5 1 2 2 0 0 0 0 0 Coleoptera Dytiscidae 0 0 0 0 1 0 0 1 0 0 3 0 0 0 0 0 0 Oyrinidae 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 Haliplidae 0 0 0 0 0 0 5 0 0 0 0 5 21 0 0 0 0 Elmidae 0 7 3 1 14 1 25 10 124 2 2 1 17 13 0 0 14 N Hydrophilidae 1 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 Vi Psephenidae 0 0 2 0 0 0 1 0 12 1 0 0 1 0 0 0 0 Diptera Fmpididae 0 1 0 0 0 0 0 0 4 2 0 0 0 0 0 0 0 Tabanidae 2 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 Ceratopogonidae 6 2 0 0 0 0 1 8 2 2 0 2 7 17 0 4 0 Chironomidae 51 108 73 1 38 12 145 40 145 301 1532 656 877 157 9 298 0 Culicidae 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Dixidae 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 6 Simuliidae 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 Tipulidae 0 7 0 1 0 0 2 0 1 0 1 0 0 1 0 0 0 Hemiptera Belos tomatidae 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 Corixidae 0 0 3 0 0 0 0 0 0 0 0 4 8 1 10 0 0 Gerridae 0 0 0 0 2 0 0 0 0 2 0 0 0 2 3 0 0 Notonectidae 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 Saldidae 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 Megaloptera Corydalidae 0 3 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 Sialidae 2 0 0 7 0 0 0 0 2 0 0 0 0 3 0 0 2 0 0 0 0 0 0 0 0 0 0 9 9 1 1 0 0 0 0 0 0 4 4 0 0 2 2 4 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 2 2 3 3 1 1 9 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 25 25 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 7 7 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11 11 113 113 258 258 1 1 1 1 0 0 4 4 2 2 0 0 3 3 3 3 35 35 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 4 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 12 12 60 60 0 0 0 0 0 0 0 0 0 0 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 13 13 1 1 0 0 0 0 0 0 0 0 4 4 1 1 0 0 0 0 0 0 5 5 0 0 0 0 97 97 23 23 279 279 7 7 0 0 0 0 0 0 0 0 0 0 0 0 1 1 7 7 1 1 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 2 2 4 4 1 5 5 9 0 0 0 5 5 0 0 0 35 35 24 24 23 23 1 1 1 1 0 0 0 0 0 0 0 0 0 0 3 3 0 0 0 0 5 5 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 23 23 1 1 0 0 4 4 0 0 0 0 0 5 5 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9 9 1 0 0 0 0 0 0 0 0 0 6 6 0 0 0 0 0 0 0 0 0 0 0 25 25 25 25 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 4 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 4 4 3 3 0 0 2 2 0 0 0 0 101 101 5 5 1 1 0 0 0 0 0 0 5 5 0 0 0 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 0 0 0 0 0 3 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 13 13 1 1 0 0 0 0 0 0 0 0 0 0 0 0 8 8 1 1 0 0 2 2 5 5 0 0 8 8 1 1 6 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 10 1 1 2 2 0 0 0 0 1 1 0 0 8 8 3 3 1 1 0 0 1 1 0 0 2 2 0 0 3 3 0 0 2 2 0 0 4 4 19 19 10 10 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 3 3 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 41 41 111 111 chidae chidae y y tomatidae tomatidae ganeidae ganeidae centropodidae centropodidae Aeshnidae Aeshnidae Cordulegastridae Cordulegastridae Siphlonuridae Siphlonuridae Gomphidae Gomphidae Heptageniidae Heptageniidae Corduliidae Corduliidae Baetidae Baetidae Ameletidae Ameletidae Arthropleidae Arthropleidae Ephemeridae Ephemeridae Leptophlebiidae Leptophlebiidae Neoephemeridae Neoephemeridae Caenidae Caenidae Ephemerellidae Ephemerellidae Poly Poly Uenoidae Uenoidae Philopotamidae Philopotamidae Hydroptilidae Hydroptilidae Linmephilidae Linmephilidae Leptoceridae Leptoceridae Phcy Phcy Hydropsychidae Hydropsychidae Helicops Helicops Lepidos Lepidos Brachycentridae Brachycentridae Chloroperlidae Chloroperlidae Sisyridae Sisyridae Leuctridae Leuctridae Gossosomatidae Gossosomatidae Nemouridae Nemouridae Odonata Odonata Ephemeroptera Ephemeroptera Plecoptera Plecoptera Trichoptera Trichoptera Neuroptera Neuroptera N N 0 0 °' °' Libellulidae 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Coenagrionidae 18 0 1 0 0 0 0 0 0 0 0 2 2 0 1 0 0 Lestidae 16 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Bivalvia 0 5 160 0 6 7 32 0 4 1 0 14 15 230 0 6 61 Gasropoda 73 1 17 0 0 0 199 0 7 7 3 24 70 3 4 1 1 Nemata 1 0 0 0 0 0 26 1 10 108 0 176 2 0 0 0 0 Turbellaria 0 0 0 0 0 0 0 0 0 2 0 0 10 0 0 0 0 Porifera 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 Tardigrada 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 N 0 -.....J Appendix 2 Table la: Alphabetical list of odonate taxa and number of individuals collected per site for the Nashwaak catchment. Taxa Family SM Cl C2 DU M NA NI N3 N4 N7 PE RO RY Tl T2 YO Aeshna umbrosa Walker Aeshnidae 11 2 3 1 12 0 2 4 1 5 2 2 0 11 0 0 Basiaeschnajanata Say Aeshnidae 0 0 0 0 0 0 1 2 0 5 0 0 0 0 0 0 Boyeria gra.fiana Williamson Aeshnidae 4 1 0 0 0 1 0 2 5 3 0 4 0 1 1 3 Calopteryx aequabilis Say Caloptery gidae 0 5 0 12 1 2 7 6 8 0 0 0 0 1 1 7 Calopteryx maculata Beauvois Caloptery gidae 0 0 0 0 0 0 0 0 1 83 0 1 0 0 0 0 Cordulegaster diastatops Selys Cordulegas tridae 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 Cordulegaster maculata Selys Cordulegas tridae 2 3 0 6 4 3 1 10 6 2 10 13 0 1 14 3 Enallagma Charpentier Coenagrionidae 1 55 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Epitheca canis McLachlan Corduliidae 0 16 0 0 0 0 2 0 0 0 0 0 0 1 0 0 Gomphus (Hylogomphus) abbreviatus Gomphidae 0 0 0 0 0 0 1 2 0 32 0 0 0 0 0 0 Hagen in Selys N Gomphus (Hylogomphus) adelphus 0 Gomphidae 0 2 0 0 0 0 0 0 2 1 0 0 0 0 0 0 00 Selys Gomphus borealis Needham Gomphidae 4 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Gomphus exilis Selys Gomphidae 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Helocordulia uhleri Selys Corduliidae 0 0 0 0 0 0 1 0 0 16 0 0 0 0 0 0 lschnura Charpentier Coenagrionidae 4 41 0 0 0 0 16 0 0 0 0 0 0 7 0 0 Ladonajulia Uhler Libellulidae 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Lan thus parvulus Selys Gomphidae 1 1 11 18 10 8 0 4 1 0 0 20 7 0 2 1 Ophiogomphus mainensis Packard in Gomphidae 0 8 0 1 0 1 9 16 8 0 0 0 0 10 38 2 Walsh Ophiogomphus rupinsulensis Walsh Gomphidae 0 1 0 0 0 0 1 2 0 0 0 0 0 1 5 0 Plathemis lydia Drury Libellulidae 28 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Somatochlora elongata Scudder Corduliidae 11 4 0 0 2 0 10 0 3 1 0 0 0 2 0 0 Somatochlora walshii Scudder Corduliidae 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 Somatochlora williamsoni Walker Corduliidae 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 Sf1.lo~om-e_husalbisfi.lus Ha~en in Sel~s Gomphidae 0 7 0 0 0 0 2 2 0 40 1 0 0 5 1 0 Table lb: Alphabetical list of odonate taxa and nwnber of individuals collected £er site for the Renous catchtrent. Taxa Family DUI DU2 DU3 LDU MON REN NBD NRl NR2 NR3 SR2 Aeshna umbrosa Walker Aeshnidae 1 5 0 0 10 3 2 0 4 1 5 Argia Rambur Coenagrionidae 0 0 0 0 0 0 0 0 0 0 5 Basiaeschnajanata Say Aeshnidae 0 2 0 0 0 1 0 0 0 0 1 Boyeria grafiana Williamson Aeshnidae 3 1 1 3 2 0 0 4 1 0 0 Calopteryx aequabilis Say Caloptery gidae 2 7 0 0 2 0 0 0 1 0 0 Calopteryx maculata Beauvois Caloptery gidae 4 2 0 3 0 0 0 2 0 0 3 Chromagrion conditum Selys Coenagrionidae 4 0 0 0 0 3 0 0 3 0 0 Cordulegaster diastatops Selys Cordulegastridae 0 0 0 0 0 0 0 0 0 0 2 Cordulegaster maculata Selys Cordulegastridae 5 7 9 7 25 2 0 3 3 0 1 Cordulia shurtle.ffii Scudder Corduliidae 6 1 0 0 0 0 0 0 0 0 0 Enallagma Charpentier Coenagrionidae 12 0 0 0 0 0 0 0 0 0 0 Epitheca can is Mclachlan Corduliidae 0 3 0 0 0 5 0 0 0 0 0 Gomphus (Hylogomphus) abbreviatus Gomphidae 21 3 1 0 0 3 1 1 0 1 0 Hagen in Selys Gomphus (Hylogomphus) adelphus Selys Gomphidae 1 3 0 0 0 2 0 2 0 1 0 N Gomphus (Hylogomphus) Needham, 0 Gomphidae 0 0 0 0 0 0 0 2 0 \0 Westfall & May 0 0 Gomphus borealis Needham Gomphidae 0 0 0 1 0 0 0 0 0 0 1 Gomphus descriptus Banks Gomphidae 0 0 0 0 0 0 0 0 0 17 0 Gomphus exilis Selys Gomphidae 0 0 0 0 0 0 0 0 0 2 20 Hagenius brevistylus Selys Gomphidae 0 1 0 0 4 0 0 0 0 0 0 Helocordulia uhleri Selys Corduliidae 0 0 1 0 0 3 0 2 1 0 1 Lanthus parvulus Selys Gomphidae 0 1 8 9 1 1 8 1 0 0 0 Leucorrhinia Brittinger Libellulidae 5 0 0 0 0 0 0 0 0 0 0 Libellula quadrimaculata Linnaeus Libellulidae 1 0 0 0 0 0 0 0 0 0 0 Macromia illinoiensis Walsh Corduliidae 1 3 0 0 0 0 0 0 0 3 2 Neurocordulia Selys Corduliidae 0 0 0 0 0 1 0 0 0 0 0 Ophiogomphus mainensis Packard in Walsh Gomphidae 1 47 61 8 3 6 0 44 15 2 10 Ophiogomphus rupinsulensis Walsh Gomphidae 1 3 6 0 0 2 0 10 0 0 0 Somatochlora elongata Scudder Corduliidae 0 5 1 0 31 3 0 0 8 0 1 Somatochlora williamsoni Walker Corduliidae 0 2 0 0 6 1 0 0 1 1 0 Stylogomphusalbistylus Hagen in Selys Gomphidae 1 1 2 2 0 2 0 0 4 1 9 SJ:::E.efrum Newman Libellulidae 11 0 0 0 0 0 0 0 0 0 0 Table 3: AJphabetical list of odonate taxa and number of individuals collected per site from the Little Southwest Miramichi catchment. Taxa Family LSWl LSW3 LSW4 LSW5 LSW8 LSWlO LSWll Aeshna umbrosa Walker Aeshnidae 14 0 2 1 0 14 0 Argia Rambur Coenagrionidae 0 0 0 1 0 1 0 Basiaeschnajanata Say Aeshnidae 0 0 0 4 0 0 0 Boyeria grafiana Williall15on Aeshnidae 2 2 0 0 2 1 7 Calopteryx aequabilis Say Caloptery gidae 0 0 0 8 0 16 0 Calopteryx maculata Beauvois Caloptery gidae 1 6 0 6 1 0 0 Chromagrion conditum Selys Coenagrionidae 0 0 0 0 0 4 0 Cordulegaster maculata Selys Cordulegastridae 11 1 0 1 6 4 6 Cordulia shurtleffii Scudder Corduliidae 0 0 0 2 0 0 0 Enallagma Charpentier Coenagrionidae 14 0 0 0 0 2 0 Gomphus exilis Selys Gmiphidae 0 0 0 3 0 0 0 Helocordulia uhleri Selys Corduliidae 0 1 0 0 0 0 0 Lan thus parvulus Selys Gomphidae 3 2 0 0 3 0 7 Leucorrhinia Brittinger Libellulidae 2 0 0 0 0 0 0 N 1--' Macromia illinoiensis Walsh Corduliidae 0 0 0 3 0 0 0 0 Nehallenia irene Hagen Coenagrionidae 0 0 0 0 0 1 0 Ophiogomphus mainensis Packard in Walsh Gomphidae 0 0 0 25 0 0 1 Ophiogomphus rupinsulensis Walsh Gomphidae 0 0 0 10 0 0 0 Somatochlora elongata Scudder Corduliidae 1 0 0 7 0 1 2 St~loaome.hus albis1,1.lus Ha~en in Sel~s Gomphidae 0 0 0 7 0 0 Appendix 3 Table 1: AJphabetical list of TrichoJ2tera taxa and ntnnber of individuals collected Eer site for the N ashwaak catchment. Taxa Family 5M Cl C2 DU M NA NI N3 N4 N7 PE RO RY Tl T2 YO Agapetus Curtis Glossosomatidae 28 0 0 0 10 4 0 0 0 0 0 33 I 0 0 0 Agarodes Banks Sericos tomatidae 0 0 0 0 0 0 0 I 0 76 0 0 0 0 0 0 Agraylea Curtis Hydroptilidae 0 0 0 0 0 0 0 0 0 I 0 0 0 0 0 0 Anabolia Stephens Limnephilidae 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 Apatania Kolenati Apataniidae 18 18 3 37 0 I 8 8 I 0 6 I 2 12 19 7 Arctopsyche Mclachlan Hydropsychidae I 0 2 9 0 3 I 0 0 0 0 0 0 I 0 3 Banksiola Martynov Phryganeidae 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Brachycentrus Curtis Brachycentridae 0 33 0 I 0 11 30 31 6 0 2 0 0 3 12 I Cerac/ea Stephens Leptoceridae 0 0 0 0 0 0 0 4 0 44 0 0 0 0 0 0 Cheumatopsyche Wallengren Hydropsychidae 2 4 2 9 I 1 47 0 9 3 4 9 1 8 6 3 Chimarra Stephens Philopotamidae 0 0 0 0 0 0 18 0 0 30 0 0 0 0 0 0 Diplectrona Westwood Hydropsychidae 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 N Dolophilodes Ulmer Philopotamidae 51 35 14 48 3 25 0 6 2 0 35 21 3 5 0 59 - Glossosoma Curtis Glossosomatidae 0 27 3 25 0 7 19 I 10 0 17 4 0 4 8 24 - Fabricius Limnephilidae Glyphopsyche irrorata 0 0 0 0 0 0 0 I 0 0 0 0 0 65 0 0 Goera Stephens Goeridae 2 2 0 0 0 0 2 0 3 0 I 3 0 I 0 2 Helicopsyche von Siebold Helicopsychidae 0 23 0 0 0 0 31 52 10 5 0 0 0 0 2 4 Hydatophylax argus Harris Limnephilidae 7 7 10 12 7 9 5 5 26 I 45 22 21 10 259 16 Hydropsyche Pictet Hydropsychidae 42 47 39 102 2 34 74 50 9 28 202 25 5 48 27 40 Hydroptila Dalman Hy droptilidae 6 0 3 0 0 0 0 0 0 0 0 0 3 0 0 0 Lepidostoma Rambur Lepidostomatidae 5 129 192 122 6 56 157 474 59 50 85 46 307 460 120 136 Limnephilus Leach Limnephilidae 426 19 12 0 0 0 15 0 0 36 I 0 0 0 2 0 Lype diversa Banks Psychomyiidae 0 0 0 0 0 0 0 0 0 0 I 0 0 0 0 0 Micrasema Mclachlan Brachycentridae 10 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 Molanna Curtis Molannidae 19 23 I 14 I 10 I 4 I 7 2 2 0 10 0 I Mystacides Berthold Leptoceridae 0 23 0 6 0 4 1 38 46 38 3 1 0 38 13 17 Nemotaulius hostilis Hagen Limnephilidae 26 58 0 0 0 0 17 0 0 6 0 0 0 4 0 0 Neophylax Mclachlan Uenoidae 0 0 0 0 0 0 0 0 0 0 2 5 0 0 0 0 N eureclipsis Mclachlan Polycentropodidae 0 0 0 4 0 2 3 I 5 2 0 0 0 0 0 0 Nyctiophylax Brauer Polycentropodidae 0 0 0 0 I 0 0 0 0 0 0 0 0 I 0 0 Oecetis Mclachlan Leptoceridae 0 4 0 0 0 0 0 8 2 8 0 0 0 I 0 0 Oligostomis Kolenati Phryganeidae 2 10 0 3 7 7 0 5 4 0 1 2 1 9 0 4 Phylocentropus Banks Dipseudopsidae 0 0 0 1 2 0 0 0 0 1 0 0 0 1 0 0 Polycentropus Curtis Po lycentropodidae 3 0 1 12 5 20 2 0 3 0 0 7 0 1 4 8 Psilotreta Banks Odontoceridae 52 14 5 19 8 20 50 62 27 117 62 12 9 101 65 6 Ptilostomis Kolenati Phry ganeidae 11 6 2 1 0 0 9 5 2 22 1 0 0 3 7 2 Pycnopsyche Banks Limnephilidae 0 0 0 1 0 0 0 0 0 34 0 1 0 0 0 0 Rhyacophila Pictet Rhyacophilidae 11 8 3 23 5 4 4 0 10 1 12 13 3 15 1 29 Setodes Rambur Leptoceridae 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 Triaenodes Mclachlan Leetoceridae 0 0 0 2 0 0 0 0 0 12 0 0 1 0 0 N 1--' N Table 2: AJphabetical list ofTrichol?tera taxa and number of individuals collected l?er site for the Renous catchment. Taxa Family DUI DU2 DU3 LDU MON REN NBD NRl NR2 NR3 SR2 Agrypnia Curtis Phry ganeidae 0 5 0 1 0 0 0 0 0 0 0 Anabolia Stephens Limnephilidae 0 0 0 0 0 0 0 1 0 0 0 Apatania Kolenati Apataniidae 0 2 20 1 1 0 0 0 0 2 0 Arctopsyche Mclachlan Hydropsychidae 0 0 1 6 0 0 0 0 0 0 0 Brachycentrus Curtis Brachycentridae 6 77 7 3 0 38 0 6 1 1 0 Cheumatopsyche Wallengren Hydropsychidae 0 5 1 0 0 1 0 2 0 0 4 Chimarra Stephens Philopotamidae 0 2 1 0 0 1 0 0 0 16 5 Do/op hilodes Ulmer Philopotamidae 12 15 6 150 5 0 1 16 IO 0 5 Frenesia Betten & Mosely Limnephilidae 0 0 0 0 2 0 2 0 7 1 0 Glossosoma Curtis Oossosomatidae 1 20 5 22 6 13 0 1 0 0 0 Glyphopsyche irrorata Fabricius Limnephilidae 0 5 0 0 14 14 6 0 2 1 0 Goera Stephens G>eridae 0 0 1 0 0 0 0 0 0 0 0 Grammotau/ius Kolenati Limnephilidae 0 0 0 0 1 0 0 0 1 0 0 Helicopsyche von Siebold Helicopsychidae 0 1 3 0 0 1 0 0 0 0 2 Hydatophylax argus Harris Limnephilidae 0 5 0 3 0 2 0 0 0 0 0 N Hydropsyche Pictet Hydropsychidae 8 27 4 4 8 15 0 27 0 30 15 w""""" Lepidostoma Rambur Lepidostomatidae 0 16 5 3 12 22 22 0 5 0 3 Limnephilus Leach Limnephilidae 0 0 0 0 0 0 0 0 1 0 0 Lype diversa Banks Psychomyiidae 0 1 0 0 0 0 0 0 0 1 0 Molanna Curtis Molannidae 0 6 1 2 0 5 4 0 0 0 0 Mystacides Berthold Leptoceridae 0 21 1 5 2 1 1 2 2 0 IO Nemotaulius hostilis Hagen Limnephilidae 5 2 0 0 0 9 1 0 1 0 0 Neophylax Mclachlan Uenoidae 0 1 0 0 0 0 0 0 0 3 0 Neureclipsis Mclachlan Polycentropodidae 2 0 0 0 0 1 0 0 0 0 3 Oecetis Mclachlan Leptoceridae 4 7 4 1 1 2 0 1 1 0 6 0/igostomis Kolenati Phryganeidae 0 2 1 3 8 2 0 2 3 1 0 Oxyethira Eaton Hy droptilidae 0 1 0 0 0 0 0 0 0 0 0 Phryganea Linnaeus Phryganeidae 1 0 0 0 0 5 0 0 0 0 0 Phylocentropus Banks Dipseudopsidae 0 4 0 0 0 0 1 0 0 1 0 Polycentropus Curtis Polycentropodidae 0 0 1 1 0 0 1 2 5 0 0 Psilotreta Banks Odontoceridae 0 4 8 3 0 21 1 1 0 0 0 Psychoglypha Ross Limnephilidae 0 0 0 0 3 0 2 0 IO 0 0 Ptilostomis Kolenati Phryganeidae 0 9 0 0 0 5 0 0 0 0 0 Pycnopsyche Banks Limnephilidae 2 0 2 5 3 2 0 5 4 7 2 Rhyacophila Pictet Rhyacophilidae 0 I 0 0 0 0 0 0 I 0 0 Setodes Rambur Leptoceridae 0 2 2 0 0 0 0 0 0 0 0 Triaenodes McLachlan Leetoceridae 0 5 I 0 0 0 0 0 0 0 0 N ..... ~ Table 3: AJphabetical list of Trichoptera taxa and number of individuals collected per site from the Little Southwest Miramichi catchment. Taxa Family LSWl LSW3 LSW4 LSW5 LSW8 LSWlO LSWll Apatania Kolenati Apataniidae 0 0 0 0 0 0 5 Arctopsyche Mclachlan Hydropsychidae 0 1 0 0 0 0 0 Banksiola Martynov Phryganeidae 69 0 1 0 0 1 0 Cheumatopsyche W allengren Hydropsychidae 0 0 0 3 0 2 8 Chimarra Stephens Philopotamidae 0 0 0 2 0 10 0 Dolophilodes Ulmer Philopotamidae 1 41 0 1 35 2 26 Frenesia Betten & Mosely Limnephilidae 1 0 0 1 1 0 0 Glossosoma Curtis Gos sosomatidae 10 4 42 11 0 0 4 Glyphopsyche irrorata Fabricius Limnephilidae 1 0 2 0 0 0 0 Goera Stephens Goeridae 7 0 38 0 0 0 0 Helicopsyche von Siebold Helicopsychidae 0 0 0 15 0 0 3 Hydatophylax argus Harris Limnephilidae 2 10 6 123 1 23 4 Hydropsyche Pictet Hydropsychidae 24 18 0 22 13 17 76 Hydroptila Dalman Hy droptilidae 0 0 0 0 0 77 108 N Lepidostoma Rambur Lepidostomatidae 0 4 0 3 42 11 105 VI - Limnephilus Leach Limnephilidae 43 0 6 91 5 63 2 Lype diversa Banks Psychomyiidae 1 0 0 0 0 0 1 Molanna Curtis Molannidae 1 0 0 3 9 24 0 Mystacides Berthold Leptoceridae 0 0 0 0 0 16 5 Nemotaulius hostilis Hagen Limnephilidae 0 0 4 3 0 5 I Neophylax Mclachlan Uenoidae 1 1 4 0 0 0 0 Oecetis Mclachlan Leptoceridae 0 0 0 1 0 3 0 Oligostomis Kolenati Phry ganeidae 2 2 2 1 5 2 5 Oxyethira Eaton Hydroptilidae 0 0 0 0 0 0 1 Phylocentropus Banks Dipseudopsidae 1 1 0 0 1 0 0 Polycentropus Curtis Po lycentropodidae 1 1 0 0 3 33 15 Psilotreta Banks Odontoceridae 47 1 0 0 13 17 2 Psychoglypha Ross Limnephilidae I 0 22 0 0 0 0 Ptilostomis Kolenati Phryganeidae 30 0 1 24 I 11 0 Rh~acoe.hila Pictet Rhyacophilidae 1 7 13 0 20 0 3 Appendix 4 Table 1: Variance partitioning using PCNM spatial predictors. Fractions are [a] date (regular) or year (italics) [b] purely envirorurentai [c] purely spatiai [d] date or year*environment, [e] envirorurent*space, [f] date or year*space, [g] date or ~ear*environment*s;Eace. Significance for testable fractions [a, b, cJ based on ;Eermutational ANOVA bold: ;E<0.05. Drainage Environment Space Sites [a] [b] [c] [d] [e] [t] [g] residual explained AVG SWPE PACIFIC SC AREA V3, Vl, V2 622 0.097 0.019 0.011 0.87 0.13 AVG_MAX_TEMP Skeena- AVG SWPE V3, Vl, Vl8 79 0.035 0.052 0.020 0.0082 0.021 0.0029 0.0033 0.86 0.14 Coast [l] WC ELEV Queen MACROPHYTE Charlotte WC SWPE Vl, V7, VlO 40 0.016 0.062 0.046 0.0040 0.030 0.019 -0.0055 0.83 0.17 Islands [2] SUBCATl N SC AREA Nechako - -0\ WC ELEV V2, V3, Vl 45 0.0052 0.042 0.022 0.029 0.0097 0.041 0.031 0.82 0.18 [3] AVG-- MAX TEMP Upper SC AREA Vl, V2, V4 35 0.029 0.038 0.0076 0.0016 0.083 -0.0013 -0.0015 0.84 0.16 Fraser [4] AVG SWPE Southern SC AREA Coastal Vl, VS 28 0.054 0.057 0.17 0.72 0.28 AVG SWPE Waters [5] WC ELEV Thompson AVG-SWPE V2, V4 61 0.019 0.092 0.018 0.0047 0.033 0.0064 0.028 0.80 0.20 6 [ ] PCT=AGR AVG-- MAX TEMP Lower SC AREA Vl, V2, V3 89 0.10 0.024 0.089 0.78 0.22 Fraser [7] PCT_URBAN AVG_PRECIP Vancouver SC AREA V2, V6, Vll 39 0.064 0.0086 0.061 0.87 0.13 Island [8] AVG_SWPE PCT_AGR Skagit [9] SC_AREA V6 20 0.18 0.063 -0.00083 -0.018 0.034 0.027 -0.012 0.72 0.28 Columbia AVG_MAX_TEMP, VI, V2, V3 147 0.017 0.057 0.026 0.0094 0.042 0.013 0.044 0.79 0.21 [IO] SC AREA N ..... -.J Curriculum Vitae Colin James Curry B.Sc. in Ecology, University of Calgary, 2002 Publications: Curry, C., Curry, RA., and D.J. Baird (2011) Re1ative patterns of1arval biodiversity in Trichoptera and Odonata: the role of dispersal ability in freshwater insect biodiversity assessment. Bulletin of the Entomological Society ofCanada, 43, 183-184. Curry, C.J., Curry, RA. & Baird, D.J. (2012) The contnbution of riffles and riverine wetlands to benthic rmcroinvertebrate biodiversity. Biodiversity and Conservation, 21, 895-913. Curry, C.J., Zhou, X. & Baird, D.J. (2012) Congruence of biodiversity measures am:mg Jarval dragonflies and caddisflies from three Canadian rivers. Freshwater Biology, 57, 628-639. Conference Presentations: Oral presentations Curry, C.J., Baird, D.J. And Monk, W.A. (2012) Ecoregional and national-scale patterns ofbenthic insect biodiversity in Canadian Rivers. Society for Freshwater Science 60th Annual Meeting, Louisville, Kentucky, USA. 20-24 May. Curry, C., Baird, D.J., Curry, RA., Heard, K. and X. Zhou (2012) Aquatic insect biodiversity in the Miramichi. Miramichi Science Workshop, Miramichi, NB. 13 March. Curry, C., Curry, RA., and D.J. Baird (2011) Re1ative patterns of Jarval biodiversity in Trichoptera and Odonata: the role of dispersal ability in freshwater insect biodiversity assessment. Canadian Entomological Society Joint Annual Meeting, Halifu:x, NS. 8 November. Curry, C., Baird, D.J., and RA. Curry (2010) Biodiversity of Dragonflies and Caddisflies in the Miramichi River Watershed. Miramichi Science Workshop, Miramichi, NB. 16 March. Curry, C. (2010) Assessing biodiversity in riverine insect connmmitie s: a comparison of dragonflies and caddisflies in New Bnmswick. Seminar, Department of Biology, University of New Bnmswick, Fredericton 29 January. Cuny, C., Baird, D.J. and RA. Curry (2009) Does dispersal ability influence compositional variation in aquatic insect communities? North American Benthological Society 57th Annual Meeting, Grand Rapids, Michigan, USA. 16-23 May. Cuny, C., Baird, D.J. and RA. Curry (2008) The importance of dispersal in assessing insect diversity in riverine habitats. 8th INTECOL Wetlands Conference, Cuiaba, Brazil 20-25 July. Cuny, C., Baird, D.J. and RA. Curry (2007). Assessment of1arval odonate diversity in two tnbutary watersheds of the Saint John River, New Bnmswick. Canadian Conferences for Fisheries Research and Society of Canadian Limnologists, Montreai Canada. January 4-6. Poster presentations Cuny, C., Curry, RA., and D.J. Baird (2011) The importance of habitat, taxon, and dispersal in freshwater insect biodiversity assessment. Canadian Rivers Institute Research Day, Fredericton, NB. 8 July. Cuny, C., Baird, D.J. and RA. Curry (2007). Sampling considerations in the assessment of Jarval dragonfly diversity. NorthAmerican Benthological Society 55thAnnual Meeting, Cohnnbia, South Carolina, USA 3-8 Jtn1e. Cuny, C., Baird, D.J. and RA. Curry (2005). A multi-scale approach to freshwater biodiversity assessment and biomonitoring. New Currents in Conserving Freshwater Diversity, American Museum ofNatural History, New York, USA. 7-8 April