Regional β-Diversity of Stream Insects in Coastal Alabama Is Correlated with Stream Conditions, Not Distance among Sites

Simple Summary

Biodiversity is measured differently depending on the spatial scale of the study. Beta (β)-diversity, for example, is a calculated measurement that addresses changes in diversity across different assemblages in a specific region. Studies of β-diversity often address changes in diversity across gradients of environmental conditions and distance among sites. However, β-diversity of stream insects can be difficult to measure due to their hyper-diversity and challenging taxonomy. Our study investigated the association of β-diversity with habitat conditions and distance among these habitats for insects found in the coastal streams of Alabama, USA. Additionally, we looked for potential influences caused by seasonality (fall and summer) and the level of taxonomic identification (genus, species). Regardless of season, stream conditions were highly correlated with β-diversity. More specifically, stream size and water chemistry showed the highest associations with β-diversity. Changes in β-diversity were largely driven by species replacement (turnover) rather than species loss (nestedness). The taxonomic resolution had minimal effects on the calculations of β-diversity across environmental conditions. Distance among stream sites was never correlated with β-diversity. As we continue to face global insect declines, our study provides valuable insight into the patterns that drive changes in diversity across environmental gradients.

Abstract

β-diversity is often measured over both spatial and temporal gradients of elevation, latitude, and environmental conditions. It is of particular interest to ecologists, as it provides opportunities to test and infer potential causal mechanisms determining local species assemblages. However, studies of invertebrate β-diversity, especially aquatic insects, have lagged far behind other biota. Using partial Mantel tests, we explored the associations between β-diversity of insects found in the coastal streams of Alabama, USA, and stream conditions and distances among sites. β-diversity was expressed using the Sørensen index, β_Sor, stream conditions were expressed as principal components (PCs), and distances as Euclidean distances (km) among sites. We also investigated the impact of seasonality (fall, summer) and taxonomic resolution (genus, species) on β_Sor. Regardless of season, β_Sor was significantly correlated (p < 0.01; r > 0.44) with stream conditions (stream size and water chemistry), while taxonomic resolution had minimal effect on associations between β_Sor and stream conditions. Distance was never correlated with changes in β_Sor (p > 0.05). We extended the use of the Sørensen pair-wise index to a multiple-site dissimilarity, β_Mult, which was partitioned into patterns of spatial turnover (β_Turn) and nestedness (β_Nest). Changes in β_Mult were driven mostly by turnover rather than nestedness.

1. Introduction

A fundamental pattern of great interest to ecologists is how and why species diversity changes with location or time. Species diversity is scale dependent [1,2] and, as such, is generally examined with regards to three spatial levels: alpha (α)-, beta (β)-, and gamma (γ)-diversity [3,4,5]. Alpha diversity is the number of species in a local assemblage, whereas γ-diversity is the number of species in a regional pool. The change in species composition among local assemblages within a region is β-diversity. One can view β-diversity as the measurement that links α- and γ-diversity within a region [4,6]. β-diversity is of particular interest to ecologists as it provides an opportunity to test and infer potential causal mechanisms determining local species assemblages [7,8].

Although β-diversity is simple in concept, there are a bewildering number of indices, with no clear consensus for meaning, measurement, statistical analysis, or interpretation [8,9,10,11,12]. One reason for multiple perspectives is that β-diversity is a calculated index as opposed to an observed or estimated value, such as α-diversity and γ-diversity. However, two broad approaches have emerged. The first approach examines directional changes in species composition across local assemblages over measured or inferred gradients, such as distance or local environmental conditions [8,12]. The second approach examines changes across local assemblages without regard to a specific gradient and thus can be considered a measure of variation in species composition [12]. This second approach was expressed by Whittaker’s measurement, β = γ ÷ α, in which β-diversity is the ratio of γ-diversity and mean α-diversity [12]. We explore the former approach by examining directional changes in β-diversity over gradients of stream conditions and distance among sites.

Studies have examined patterns of β-diversity over various gradients, such as altitude [13,14], environmental conditions [15,16], and latitude [17,18,19]. However, studies of invertebrate β-diversity [20], especially stream insects, lag far behind studies of other biota [14]. Such studies are particularly important in stream systems, where the high biodiversity of organisms in the low area of the streambed results in habitats that are especially vulnerable to global changes [21]. Accordingly, these habitats are some of the most endangered ecosystems on the planet [22].

Though some studies have been conducted in similar stream systems in the southern United States [23,24] little is known about the ecology of the coastal streams of Alabama, which ultimately flow into the Gulf of Mexico [25,26]. These streams tend to be short, usually less than 40 km, with few, if any, tributaries. These sandy-bottom streams are generally meandering and slow-moving, flowing mostly through forested habitat. Like other freshwater systems, these streams are especially vulnerable to global changes [21], which makes them a sensitive “biological meter” for measuring widespread changes in biodiversity. These streams are of particular interest as many drain into what E.O. Wilson referred to as “America’s Amazon”—the Mobile Tensaw Delta (MTD). The MTD is a 1150-km² relatively pristine area consisting largely of cypress-gum swamps and bottomland hardwood forests interlaced with large rivers, streams, canals, bayous, and marshes [27].

We calculated β-diversity as Sørensen pair-wise indices, β_Sor, among pairs of stream sites [28]. β_Sor is calculated using presence/absence data and can be expressed as:

$${\beta }_{Sor}=\frac{2C}{S1+S2}$$

where C is the number of species the two communities have in common, S1 is the total number of species found in community 1, and S2 is the total number of species found in community 2 [11]. Hence, this index can be viewed as the dissimilarity in species composition between any two sites.

Accordingly, our aim was to explore how patterns of β_Sor of lotic insects in these historically understudied coastal streams of Alabama changed over gradients of environmental conditions and distance. We also explored how seasons (fall and summer) and different taxonomic resolutions (genus only, genus/species mix, and species only) influenced patterns of β_Sor. We then extended the use of the Sørensen pair-wise index to a multiple-site dissimilarity index, β_Mult. [29]. Whereas Sørensen is an index of two sites in a region, β_Mult provides a measurement of dissimilarity among all sites in a region. This latter index can further divide into diversity due to species turnover (change) among sites (β_Turn) and species nestedness (loss) among sites (β_Turn). Hence, β_Mult = β_Turn + β_Nest.

2. Materials and Methods

2.1. Study Sites and Sampling Protocols

Details of the study area, sampling protocols, and measurements of stream conditions can be found in McCreadie and Bedwell [25]; here we present a brief summary for clarity. The study area is within the Southern Pine Hills and Coastal Lowlands districts of the lower Coastal Plain of Alabama, USA [30]. Watersheds in this area drain into the Mobile-Tensaw Delta, Mobile Bay, or the Gulf of Mexico. These coastal streams are low-gradient, meandering systems, with sand as the primary streambed substrate. Twenty-three streams between 30.74–31.25° N and 87.76–88.24° W (Figure 1) were sampled from 2 October to 17 November 2007 (fall collections), with 17 of these streams sampled from 29 May to 29 June 2007 (summer collections). Mean Euclidean distance (±SE) among sites in the fall was 26.7 ± 0.67 km and for summer sites, 25.6 ± 0.96 km.

At each stream site, a 100-m representative length of stream (scale = local assemblage) was delineated, and samples (n = 5), taken (randomly) within this length at each site for both the fall and summer sampling regimes. Insects were sampled using a standard 1 m × 1 m kick-net, mesh size 0.3 mm, over a standard quadrate size (PVC pipe) of 60 cm × 60 cm [31]. The streambed was dominated by shifting sand, with insects confined to isolated habitat patches such as snags or leaf packs. Using the protocols of McCreadie et al. [32], the following stream variables were recorded at each collection: width (W), depth (D), velocity (U), conductivity, pH, temperature, dissolved oxygen, riparian vegetation, canopy cover, and dominant streambed particle size. Riparian vegetation (open, brush, and forest), canopy cover (none, partial, and complete), and streambed (mud, sand, gravel, and cobble) were measured as rank variables. Discharge, Q (m³ s⁻¹), was calculated as “W × D × U = Q”. Latitude and longitude were recorded using Garmin GPS units (Rino 110) (Kansas City, MO, USA). Water hardness was measured using CHEMetric^® (Midland, VA, USA) titration cells. The above variables are considered to be influential determinants (or at least strong predictors) of species assemblages in stream habitats [33]. Range and mean stream conditions by site for both summer and fall collections are given in Table S1.

Samples were transported to the laboratory on ice and preserved in 95% ethanol until identification. For each sample, an effort was made to remove all insects, with no samples being sub-sampled. Of the separated insects, five taxonomic orders were selected for study due to their high abundance, functionalities, ease of identification, and broad representation of functional feeding groups. Specimens were identified following the keys of Epler [34] for Coleoptera, Pescador et al. [35] for Plecoptera, Rasmussen and Pescador [36] for Megaloptera, Daigle [37] and Richardson [38] for Odonata, and Pescador and Rasmussen [39] for Trichoptera. Ideally, identification should be at the species level; however, larvae of many aquatic species can only be identified by genus (e.g., Leuctra). In addition, while species keys exist, they are often only useful for later instars. Accordingly, all specimens were identified by either the level of genus or species. Hence, three taxonomic levels were subject to statistical analysis—mixed taxa of genus/species, generic data only, and species data only. Voucher specimens for identification were deposited in the University of South Alabama’s Arthropod Depository.

2.2. Statistical Analyses

All statistical tests were considered significant at p < 0.05, unless otherwise stated. To determine if our sampling protocol produced a reasonable estimate of the species present in the regional pool, species accumulation curves, using a bootstrap approach [40], were calculated for each combination of season (fall, summer) and level of identification (mixed genus/species, genus only, species only), with six calculations produced in total. Bootstrap estimators were based on 999 random sample-based re-orderings of observed taxonomic grouping for each level of sampling effort (number of sites), with 1 to 23 reorderings for fall collections and 1 to 17 for summer data. The maximum bootstrap estimator of species richness was then compared to the observed species richness for each season/taxonomic level combination.

Each partial Mantel test [41] was based on three matrices constructed for each season/taxonomic level combination. These matrices included one for distance, one for stream conditions, and one for β-diversity. Distance was expressed as the Euclidean pairwise distance between each site (km). Stream conditions were log-transformed and subsequently subjected to principal component analyses (PCA) for each season. PCs with eigenvalues greater than 1.00 were selected to represent stream variable proxies [42]. The interpretation of each PC was based on correlations between each PC and the original stream variable (p-value set at p < 0.01) to provide a rigorous interpretation of the PCs [42]. The third matrix was the pairwise comparison of the Sørensen index, β_Sor, a comparison of diversity between each site [12].

For each set of matrices, partial Mantel tests [41] were used to detect significant associations between the β_Sor andstream conditions (accounting for distance effects) and the β_Sor and distance matrix (accounting for stream conditions). Partial Mantel tests were calculated separately for both season and taxonomic resolution, with the significance of each partial correlation based on 5000 random permutations between matrices. Mantel tests have been used by previous authors and are considered a powerful approach for detecting spatial correlations [43].

We then extended the use of the β_Sor pairwise index to a multiple-site dissimilarity index, β_Mult, following Baselga [44]. β_Mult can further partition the pattern of species occurrences in the site-by-species matrix into spatial turnover (β_Turn) and nestedness (β_Nest) or combinations thereof [44,45]. These two broad processes—replacement of species (spatial turnover) and species loss (nestedness)—determine the pattern of species occurrences on the site by species matrix [43]. These were presented as single measurements for each combination of season x taxonomic level data sets. Nestedness among local assemblages results when assemblages with fewer species are subsets of species in larger assemblages, reflecting processes that result in species loss from local assemblages [44,46,47]. Spatial turnover results from the replacement of species with other species across local assemblages [44].

PCA was conducted using Minitab v 20, and bootstrap estimators of number of taxa using Primer v 6.0 [48]. Partial Mantel tests were conducted using the PAST software package v. 190 [49], and multiple-site measures of diversity were calculated using the R package Betapart v 1.5.1. [29].

3. Results

3.1. Collection and Identification

Table 1. Family and genera found in over 50% of sites for the summer (29 May to 29 June 2007) and fall (2 October to 17 November 2007) collections. A complete list of all genera is given in Table S2.

Summer Collections			Fall Collections
Family	Genus	% Occurrence	Family	Genus	% Occurrence
Elimidae	Ancyronyx	94.1	Elmidae	Ancyronyx	100.0
Aeshnidae	Boyeria	94.1	Elmidae	Stenelmis	95.7
Elimidae	Stenelmis	94.1	Coenagrionidae	Argia	91.3
Elimidae	Dubiraphia	82.4	Philopotamidae	Chimarra	91.3
Leuctridae	Leuctra	82.4	Hydropsychidae	Hydropsyche	91.3
Gomphidae	Gomphus	76.5	Gomphidae	Progomphus	91.3
Hydropsychidae	Chimarra	70.6	Hydropsychidae	Cheumatopsyche	87.0
Elimidae	Gonielmis	70.6	Perlidae	Acroneuria	82.6
Perlidae	Acroneuria	64.7	Leuctridae	Leuctra	82.6
Perlidae	Neoperla	64.7	Gomphidae	Gomphus	78.3
Libellulidae	Neurocordulia	64.7	Libellulidae	Neurocordulia	78.3
Hydropsychidae	Cheumatopsyche	58.8	Leptoceridae	Oecetis	78.3
Macromiidae	Macromia	58.8	Calamoceratidae	Anisocentropus	73.9
Gomphidae	Progomphus	58.8	Elmidae	Gonielmis	73.9
Perlidae	Perlesta	52.9	Elmidae	Microcylloepus	73.9
			Elmidae	Dubiraphia	69.6
			Perlidae	Perlinella	69.6
			Aeshnidae	Boyeria	65.2
			Brachycentridae	Brachycentrus	65.2
			Perlidae	Paragnetina	65.2
			Psephenidae	Ectopria	56.5
			Calopterygidae	Calopteryx	52.2
			Polycentropodidae	Neuroclipsis	52.2

summarizes the genera found in over 50% of sites, with the entire fauna collected presented in Table S2. For the summer collections, a total of 6281 specimens were identified from 17 stream sites, belonging to either genus or species, in 27 families and 61 genera. The most common genera encountered (>80% of sites) included the Elmidae (Dubiraphia, Stenelmis) and Leuctridae (Leuctra). A total of 20,739 specimens were identified from 23 sites in the fall for either genus or species, in 27 families and 66 genera. In these collections, the most frequent genera encountered (>80% of sites) included the Elmidae (Ancyronyx, Stenelmis), Coenagrionidae (Argia), Philopotamidae (Chimarra), Hydropsychidae (Hydropsyche, Cheumatopsychidae), Gomphidae (Progomphus), Perlidae (Acroneuria), and Leuctridae (Leuctra).

Differences between the number of taxa observed and the estimated bootstrap estimate of taxa (

Table 2. Observed number of taxa for each data set and the bootstrap estimate of the number of taxa.

Data Set	Observed No. of Taxa	Bootstrap Estimator of Taxa
Fall
Genus/species	98	107.8
Genus	75	80.9
Species	44	49.2
Summer
Genus/species	83	93.9
Genus	62	67.8
Species	50	56.5

) were between 7.3 and 11.6%, with all estimated taxa higher than the observed number of taxa. We interpreted this to indicate a reasonable representation of the local insect stream fauna during the dates of collection.

3.2. Principal Component Analyses of Stream Variables

Five PCs were selected to express stream conditions for both the fall and summer data sets. Correlation analysis (p < 0.001) for the fall collections (

Table 3. Principal component analysis of stream variables and correlation analysis of principal component scores to the original stream variables, fall 2007. Mean values and ranges of the stream variables are listed in Table S1.

	Principal Components (PC)
	PC1	PC2	PC3	PC4	PC5
Eigen Analysis
Eigenvalue	3.1060	2.7122	1.9497	1.3903	1.1557
% Proportion variance explained	0.239	0.209	0.150	0.107	0.089
% Cumulative variance explained	0.239	0.448	0.598	0.704	0.793
Correlation analysis 1
Depth	0.363	−0.650 **	0.377	0.343	−0.190
Velocity	0.622 *	0.184	−0.081	0.526 *	−0.240
Elevation	−0.449	−0.567 *	0.065	0.126	0.379
Width	0.511	0.215	0.276	−0.548 *	0.060
Discharge	0.815 **	−0.196	0.343	0.148	−0.153
Temperature	0.110	−0.252	0.372	−0.743 **	−0.211
pH	0.359	0.314	0.649 **	0.086	0.158
Dissolved O2	0.537 *	0.298	−0.426	0.016	−0.278
Conductivity	−0.409	0.662 **	0.500	0.133	0.134
Hardness	−0.240	0.707 **	0.541 *	0.139	−0.052
Canopy	−0.472	−0.454	0.457	−0.053	−0.359
Riparian Veg.	0.394	−0.586 *	0.216	0.135	0.506
Bed Substrate	0.558 *	0.382	−0.146	−0.199	0.477

¹ p < 0.01 *, p < 0.001 **.

) suggested PCs were largely a measure of stream size (discharge, depth) and water column variables (pH, conductivity, temperature, and hardness). In a similar vein, correlation analysis (p < 0.001) for the summer collections (

Table 4. Principal component analysis of stream variables and correlation analysis of principal component score and original stream variables, summer 2007. Mean values and ranges of the stream variables are listed in Table S1.

	Principal Components (PC)
	PC1	PC2	PC3	PC4	PC5
Eigen Analysis
Eigenvalue	3.5595	2.5328	1.9530	1.4662	1.1505
% Proportion variance explained	0.274	0.195	0.150	0.113	0.089
% Cumulative variance explained	0.274	0.469	0.619	0.732	0.820
Correlation analysis 1
Depth	0.628 *	0.188	−0.535	0.026	0.175
Velocity	0.016	−0.621 *	−0.218	−0.091	−0.059
Elevation	0.551	0.318	−0.156	0.058	0.373
Width	0.176	−0.803 **	−0.330	−0.021	0.228
Discharge	0.590	−0.352	−0.669 *	0.050	0.043
Temperature	−0.530	−0.139	0.407	−0.196	0.578
pH	−0.323	−0.166	−0.071	−0.789 **	0.338
Dissolved O2	−0.125	−0.855 **	0.183	0.157	0.011
Conductivity	−0.777 **	−0.011	−0.487	−0.302	−0.115
Hardness	−0.720 **	0.227	−0.422	−0.248	−0.345
Canopy	0.269	0.626 *	−0.233	−0.224	0.289
Riparian Veg.	0.799 **	−0.122	0.322	−0.358	−0.262
Bed Substrate	0.483	−0.272	0.353	−0.606 *	−0.426

¹ p < 0.01 *, p < 0.001 **.

) indicated PCs were also a measure of stream size (width and depth) and water column variables (pH, conductivity, oxygen, and hardness), in addition to the type of riparian vegetation (i.e., ranked as open, brush, or forest) [32].

3.3. Partial Mantel Test Correlations

Results of the partial Mantel tests (

Table 5. Partial Mantel tests, matrix fill, and multisite β-diversity estimates ¹.

Data Set	Partial Mantel Correlations 2		Matrix Fill 3	βMult	βturn	βnest
	Stream Conditions	Distance among Sites
Fall
Genus/species	0.6693 p = 0.0001	−0.0612 p = 0.7100	0.1852	0.8471	0.7692	0.0778
Genus	0.6333 p = 0.0001	−0.1071 p = 0.8907	0.1417	0.8291	0.7297	0.0994
Species	0.4464 p = 0.0003	0.0684 p = 0.2098	0.0831	0.8616	0.7916	0.0697
Summer
Genus/species	0.6182 p = 0.0013	0.1458 p = 0.1216	0.2871	0.8436	0.7525	0.0911
Genus	0.5859 p = 0.0028	0.1436 p = 0.1259	0.2145	0.8210	0.7084	0.1126
Species	0.4980 p = 0.0041	0.1776 p = 0.0619	0.1730	0.8479	0.7280	0.1200

¹ β_Mult = total β-diversity among all sites. β_Turn = β-diversity due to species turnover. β_Nest = β-diversity due to loss (nested pattern). ² Top value partial correlation ³ Matrix fill is the proportion of cells in the species matrix with at least one taxon compared to the total number of cells in the matrix.

) showed that changes in β_Sor among sites were highly correlated (p = 0.0041–0.0001) with stream variables expressed as PCs. Given the correlations between stream variables and PCs, we interpreted this to indicate that β-diversity changed largely along gradients of stream size and water chemistry for both fall and summer collections, as well as riparian vegetation for summer collections. Mantel test correlations for species-level identification data were slightly lower than data for generic or mixed levels, regardless of season (Table 5). In contrast, distance among sites was never correlated with β-diversity (Table 5).

β_Mult was relatively consistent among all combinations of season and level of identification (β_Mult = 0.8210–0.8616). In addition, changes in β_Mult diversity among sites were largely the result of species turnover (β_Turn = 0.7084 to 0.7916) as opposed to local species loss (β_Nest = 0.0697–0.1220) (Table 5).

4. Discussion

We examined two aspects of β-diversity: β_Sor was calculated as the dissimilarity for each pairwise site comparison, whereas β_Mult extended to all sites, producing a single estimate of overall β-diversity. Our results suggest that stream conditions had a profound effect on β-diversity of lotic insects, as indicated by the significant correlations between site conditions (expressed as PCs) and β_Sor. In contrast, spatial factors (i.e., distance among sites) showed no significant correlations with β_Sor at a regional scale. The level of taxonomic precision (species, mixed, and genus) had minimal influence on the correlation, with β_Sor correlations using species-level identification being marginally lower than data using mixed or generic-level identifications. Correlations between stream condition and taxonomic level of identification showed little difference between seasons (fall and summer). The complementary analysis of multisite estimates of β-diversity indicated that species turnover, β_Turn, was consistently higher than species loss, β_Nest, for all data sets.

β-diversity is a measurement of differences in species composition among sites across gradients of habitat conditions or time. Accordingly, there has been substantial interest in β-diversity recently [8,11,12,44,50] as a means of understanding the causal factors that both determine and modulate community assemblages over space and time. In the current study, as stream conditions diverged among sites, so did the composition of the lotic insect assemblages. Changing habitat conditions over a gradient increases the number of niches and the potential for more species; thus, variation in species composition should increase among habitats [51,52], which in turn should increase β-diversity, as seen in our study. Correlation analysis with PCs and stream conditions suggested that the stream size and water column conditions were the main drivers of species replacement among sites. Stream size and water column conditions have been shown to be important factors (or at least predictors) in shaping local species assemblages of stream macroinvertebrates in both tropical and temperate streams [50,53,54,55,56].

Change in β-diversity among habitats is generally considered the result of two different processes, species turnover and species loss or gain [43], measured by β_Turn and β_Nest, respectively. Species turnover is generally viewed as a result of niche-based species sorting along abiotic and biotic factors [43,44,57], although other processes can determine local assemblages [57,58]. Our analysis of partitioning β_Mult into these components of turnover and nestedness shows species turnover (β_Turn) was much higher than nested patterns (β_Nest) in all six data sets. Hence, β_Turn is essentially a measure of β_Mult at a regional scale in our study. To date, the majority of studies that have partitioned β_Mult show that changes in species composition are largely the result of species turnover (β_Turn), with nested effects having minimal influence at regional scales [59]. Nestedness has often been viewed as a limitation in dispersal ability, particularly in aquatic habitats [60,61]. The consistently low β_Nest values and the nonsignificant results of distance in the Mantel tests suggest dispersal limitation was not an important mechanism for structuring species assemblages. However, the use of spatial variables as proxies for dispersal is not without criticism [62]. The low influence of spatial factors on insects at a regional level may be the result of at least three factors. First, all the insects we investigated have a winged adult stage, which would promote dispersal ability [50]. The second factor is a distribution/time element. Hence, though a species may not be able to directly disperse between two large distances, intervening habitats (i.e., other streams) could provide a means of ‘leap frogging’ to new sites. Given enough time, a species could then disperse to all viable sites within a region. It must also be noted that the scale of our study is relatively small (mean distance between sites ~25–30 km), which may have limited the influence of distance.

The minimal influence of taxonomic resolution (genus, mixed, and species) may be advantageous for studies of diversity that include taxa that are difficult or impossible to identify as species based on the morphology of early instars. We suggest the minimal effect of the season (fall, spring) in our results may be due to the fact that many of the species encountered in our study are multivoltine and are thus present throughout most or all of the year.

5. Conclusions

Despite the importance of aquatic insects as bioindicators of stream health, little is known about the spatiotemporal patterns of aquatic insect diversity. Additionally, even less is known about those that inhabit the coastal streams of Alabama, which drain into the highly diverse Mobile-Tensaw River Delta. Researching patterns of diversity in these insect communities has become especially pertinent as we continue to face a global crisis of insect decline. Our study provides foundational insight into the influences that drive aquatic insect diversity in coastal Alabama, but there is still more work to be done to understand the specific impacts and extent of these environmental changes over time. We suggest that future studies address the impacts of stream conditions on coastal Alabama’s insect diversity across both spatial and temporal scales.