Open Access

Distribution pattern of black fly (Diptera: Simuliidae) assemblages along an altitudinal gradient in Peninsular Malaysia

Parasites & Vectors20169:219

https://doi.org/10.1186/s13071-016-1492-7

Received: 2 February 2016

Accepted: 3 April 2016

Published: 19 April 2016

Abstract

Background

Preimaginal black flies (Diptera: Simuliidae) are important components of the stream ecosystem. However, there has been limited research undertaken on the vertical distribution of preimaginal black flies and their associated ecological factors. Stream conditions are generally variable along the altitudinal gradient. Therefore, we conducted an in-depth entomological survey to investigate the simuliid distribution pattern along an altitudinal gradient in Peninsular Malaysia.

Methods

A total of 432 collections were performed in this study (24 samplings at each of 18 fixed-streams at monthly intervals) from February 2012 to January 2014. Larvae and pupae attached on aquatic substrates such as grasses, leaves and stems, twigs, plant roots and rocks were collected by hand using fine forceps. Stream depth (m), width (m), velocity (m/s), water temperature (°C), acidity (pH), conductivity (mS/cm) and dissolved oxygen (mg/L) were measured at the time of each collection.

Results

A total of 35 black fly species were recorded in the present study. The most frequently collected species were Simulium tani (31.7 %) and S. whartoni (21.5 %), while the relatively common species were Simulium sp. (nr. feuerborni) (16.2 %), S. decuplum (15.5 %), S. angulistylum (14.8 %), S. bishopi (13.2 %) and S. izuae (11.8 %). Total estimated species richness ranged between 39.8 and 41.3, which yielded more than 80 % of sampling efficiency. Six simuliid species were distributed below 500 m, whereas eight species were distributed above 1400 m. Simulium sp. (nr. feuerborni) and S. asakoae were found from middle to high altitudes (711–1813 m). Simulium whartoni, S. brevipar and S. bishopi were distributed widely from low to high altitudes (159–1813 m). Regression analysis between species richness and PCs revealed that the species richness was significantly associated with wider, deeper and faster streams at low altitude, normal water temperature (23–25 °C), low conductivity, higher discharge, more canopy cover and riparian vegetation and with larger streambed particles (F = 20.8, df = 1, 422, P < 0.001). Forward logistic regression indicated four species were significantly related to the stream variables (S. whartoni, Simulium sp. (nr. feuerborni), S. tani and S. angulistylum). Canonical correspondence analysis indicated that the temperature, stream size and discharge were the most important factors contributing to the separation of the stream sites from different altitude and hence are the predictors for the distribution of black fly species assemblages.

Conclusions

This study has provided insight into the distribution pattern of preimaginal black fly assemblages along an altitudinal gradient in Peninsular Malaysia. This study could deepen our knowledge on the ecology and biology of the specialised taxa in response to environmental changes.

Keywords

Black fly Simulium ElevationHabitat characteristicsPeninsular Malaysia

Background

Species community structure and distribution vary spatially in response to a broad range of environmental factors including altitude [1, 2]. Towards high altitude, the main general changes observed involve stream size [3], stream depth [4], temperature, precipitation (i.e. snow and rain), partial pressure of atmospheric gases, atmospheric turbulence and wind speed, and radiation input, including short-wave ultraviolet radiation at different wavelengths [1, 5]. Consequently, all these changes create a barrier to species and drive to community diversification [2]. Moreover, communities appear to have been gradually decreasing in taxa richness with increasing altitude [6, 7].

Black flies (Diptera: Simuliidae) are insects of medical and veterinary significance. They are vectors of human diseases, notably human onchocerciasis or river blindness, the second ranking cause of infectious blindness [8]. In addition, black flies are also the vectors of diseases transmitted among wild animals and livestock [9, 10]. On the other hand, black fly larvae are dominant inhabitants of unpolluted streams and rivers over a wide range of altitudes [11]. They are postulated to have evolved in cool and mountainous environments [8]. The climate change during the glacial periods has driven the population expansion of simuliids along latitudinal and altitudinal gradients [8, 12, 13]. However, as our climate changes, the widespread alpine taxa in the cooler regions could be fragmented and isolated as specialised taxa or high-altitude specialists [8].

Black fly assemblage is a colony of different species occurring in similar ecological or habitat requirements. Defining this assemblage is of paramount importance in evaluating the comparative richness of populations, and the effect of isolation or fragmentation [14]. Numerous factors have been linked to black flies assemblage, these include competition [15], food availability [16], substrate type [17, 18], water current velocity [19, 20], water temperature [21, 22] and altitude [23, 24]. To date, the ecological studies of black flies have been given more attention on spatial distribution in response to habitat disturbance [25, 26], seasonal variation [21, 23] and locality richness [21, 2730] as well as the breeding habitat preference [31]. However, there has been limited research undertaken on the vertical distribution of preimaginal black flies and their associated ecological factors. The knowledge of distribution patterns related to altitude could contribute to the understanding of the geographical distribution of many species as well as their local diversity.

We hypothesized that stream conditions vary according to the altitude, thus black fly diversity and assemblages are expected to change along the altitudinal gradient. Certain preimaginal stages of black flies would have a broad range of vertical distribution (i.e. generalist species), while others might show a specific range of distribution (i.e. specialist species). The specialised taxa may limit their distribution to certain preferred microhabitat/niche conditions [25, 31, 32]. To test this hypothesis, we made our first attempt to investigate the distribution pattern of black flies and their associated environmental factors, along an altitudinal gradient in Peninsular Malaysia.

Methods

Study sites

A total of 18 stream points were selected as fixed sites for sampling. Streams were chosen according to their accessibility for collection, altitude and the presence of flow. These stream locations were divided into three categories: (1) low altitude (100–500 m), (2) middle altitude (501–1000 m), (3) high altitude (1001–1813 m). A total of six streams were assigned as low altitude: (E1–E6), five streams as middle altitude: (E7–E11) and seven streams as high altitude (E12–E18). Seven streams are located in the state of Pahang (Cameron Highland, 04°28.738'N, 101°22.979'E and Lipis, 04°23.715'N, 101°36.443'E) while 11 streams are located in the state of Perak (Tapah, 04°14.203'N, 101°18.354'E and Simpang Pulai, 04°34.956'N, 101°20.717'E). Location details for 18 fixed-stream sites and associated ecological characteristics are presented in Fig. 1 and Table 1.
Fig. 1

Map showing 18 fixed-streams (E1 − E18) located in the states of Pahang and Perak in Peninsular Malaysia (top right), small map showing the Peninsular Malaysia (bottom left)

Table 1

Locations of 18 fixed-streams and associated ecological characteristics

Stream/altitude

Locality

Altitude (m)

Code

GPS

Canopy cover

Riparian vegetation

Streambed

Low

      

E1

Tapah

159

TPS1

04°14.203'N, 101°18.354'E

Partial

Forest

Rubble

E2

Tapah

224

TPS3

04°16.522'N, 101°18.996'E

Complete

Forest

Rubble

E3

Tapah

235

TPS2

04°16.316'N, 101°19.022'E

Open

Forest

Boulder

E4

Tapah

337

TPS4

04°18.420'N, 101°19.658'E

Complete

Forest

Bedrock

E5

Lipis

388

RBS18

04°23.715'N, 101°36.443'E

Open

Brush

Sand

E6

Lipis

423

RBS17

04°23.715'N, 101°36.443'E

Partial

Brush

Bedrock

Middle

      

E7

Cameron Highland

711

CHS5

04°22.220'N, 101°21.512'E

Partial

Forest

Bedrock

E8

Cameron Highland

737

CHS6

04°22.660'N, 101°21.902'E

Complete

Forest

Small stone

E9

Cameron Highland

872

CHS7

04°23.165'N, 101°22.334'E

Open

Forest

Boulder

E10

Cameron Highland

992

CHS9

04°24.274'N, 101°22.572'E

Partial

Forest

Bedrock

E11

Cameron Highland

1000

CHS8

04°24.112'N, 101°22.356'E

Complete

Forest

Sand

High

      

E12

Cameron Highland

1160

CHS10

04°26.723'N, 101°22.979'E

Partial

Forest

Sand

E13

Simpang Pulai

1315

SPS12

04°34.956'N, 101°20.717'E

Open

Brush

Bedrock

E14

Simpang Pulai

1345

SPS13

04°34.760'N, 101°20.507'E

Complete

Brush

Sand

E15

Cameron Highland

1405

CHT11

04°28.738'N, 101°22.979'E

Partial

Forest

Rubble

E16

Cameron Highland

1602

CHS14

04°31.258'N, 101°24.247'E

Open

Brush

Boulder

E17

Cameron Highland

1750

CHS16

04°31.519'N, 101°23.365'E

Partial

Forest

Boulder

E18

Cameron Highland

1813

CHS15

04°31.461'N, 101°23.338'E

Complete

Brush

Small stone

Black fly sampling and identification

Overall, a total of 432 collections were performed in this study (24 at each 18 fixed-stream sites at monthly intervals) from February 2012 to January 2014. Our extensive repeated samplings were expected to reveal the most accurate species number at all surveyed streams. Thus, sampling bias due to seasonal fluctuations in physicochemical parameters was minimized in this study. Each stream was sampled from downstream to upstream (20 m), for approximately 1 h, by two people. Larvae and pupae attached on aquatic substrates such as grasses, leaves and stems, twigs, plant roots and rocks were collected by hand using fine forceps. These sampling protocols could represent the species occurrence in a locality [21, 33]. Pupae attached on similar substrates were individually kept alive in vials until emergence. The adults, together with their pupal exuviae and cocoons were preserved in 80 % ethanol for identification at the subgenus, species-group or species level. The methods of collection and identification followed those of Takaoka [34] and Adler et al. [10].

Physicochemical measurements

The following stream physicochemical parameters were measured at the time of each collection: stream depth (m), width (m), velocity (m/s) (one to three measurements along the collection path), water temperature (°C), acidity (pH), conductivity (mS/cm) and dissolved oxygen (mg/l). The values of pH, temperature, conductivity and dissolved oxygen were taken using a portable multi probe parameter (Hanna HI 9828). Meter tape and steel ruler were used to measure stream width and depth, respectively, while a cork and a timer watch were used to measure stream velocity; the time taken for a cork to move one meter in distance. Velocity, depth and width measurements were used to estimate discharge [35]. The ecological and physicochemical measurement protocols including those for major streambed particles, riparian vegetation, and canopy cover followed those of McCreadie et al. [35]. For each fixed-stream site, the latitude and longitudinal coordinates were taken once and recorded using a hand held global positioning system (GPS) instrument (Garmin International Inc., Olathe, KS).

Data analyses

Frequency of occurrence (FO) was designated in percentages (Table 2), calculated by the total number of a species occurrence, divided by the total number of collections (n = 432) [30]. Stream occurrence (SO) presented in percentages was calculated by the number of sites where a species was taken, divided by the total streams (n = 18). A rarefied species accumulation curve of individuals was created for all samples to determine if species in the site were adequately sampled [36]. The expected richness (First Order Jackknife and Chao estimates) was obtained to predict the possible number of species occurring in all fixed-stream sites. Species diversity estimations were calculated to determine the efficiency of the sampling by dividing the number of actual species collected by the number of estimated species [37]. To test the null hypothesis of random co-occurrence, we employed the null modeling software ECOSIM Version 7 [38] to create null models for co-occurrence, in which the C-score index [39] with fixed sums for row and column constraints was applied [40]. The presence or absence of a species was expressed on a binary scale (0 = absent, 1 = present), as in previous studies [25, 29, 41]. Cluster analysis based on Sorenson’s coefficient was used to compare the percentage of site similarity in species composition for each site. Regression analysis was used to determine relationships between species distribution and stream variables. Because stream variables are inter-correlated, principal components analysis (PCA) was used to reduce the number of variables into groups of independent components. Principal components (PCs) with eigenvalues greater than 1.0 were retained as variables. To interpret the PCs, Spearman’s rank correlation test was used to detect the relationship between principal components and stream variables using a significance level of P < 0.001. Forward logistic regression analysis was used to examine the relationships between spatial distribution and the PCs. Only species that occurred at more than 10 % of total collections were considered in regression analyses [30] because those present at a lower frequency have resulted in the lack of statistical power (large number of zero values were observed) [21]. Linear regression was used to test the relationship between species richness (i.e. number of species in each sampling site) and the stream variables of the sampling sites (i.e. PC scores). All collections were subjected to PCA, and the PC scores were used for regression analysis. Canonical correspondence analysis (CCA) was used to investigate the relationship between environmental variables and species assemblages. CCA was analysed using the combined data set (n = 432 collections). The CCA was conducted using the program PC-ORD (version 5.14) [42]. Species Diversity and Richness (SDR) version 4 [43], the SPSS statistical package, version 16.0, Chicago, IL, were employed for diversity and statistical analyses respectively.
Table 2

Abundance of black flies, frequency of occurrence (FO) and stream occurrence (SO) from 24 collections at 18 fixed-streams in Peninsular Malaysia

Species

Number of specimens

%

%

FO

SO

S. (Gomphostilbia) adleri Jitklang & Kuvangkadilok, 2008

3

0.7

5.6

S. (Gomphostilbia) angulistylum Takaoka & Davies, 1995

347

14.8

55.6

S. (Gomphostilbia) asakoae Takaoka & Davies, 1995

837

7.6

55.6

S. (Gomphostilbia) brinchangense Takaoka et al., 2014

11

0.7

11.1

S. (Gomphostilbia) burtoni Takaoka & Davies, 1995

19

3.0

16.7

S. (Gomphostilbia) cheongi Takaoka & Davies, 1995

12

2.3

22.2

S. (Gomphostilbia) decuplum Takaoka & Davies, 1995

176

15.5

61.1

S. (Gomphostilbia) duolongum Takaoka & Davies, 1995

3

0.7

11.1

S. (Gomphostilbia) gombakense Takaoka & Davies, 1995

117

10.0

50.0

S. (Gomphostilbia) izuae Takaoka et al., 2013

333

11.8

55.6

S. (Gomphostilbia) longitruncum Takaoka & Davies, 1995

1

0.2

5.6

S. (Gomphostilbia) lurauense Takaoka et al., 2013

26

3.2

33.3

S. (Gomphostilbia) roslihashimi Takaoka & Sofian-Azirun, 2011

112

9.3

55.6

S. (Gomphostilbia) sheilae Takaoka & Davies, 1995

120

4.4

44.4

S. (Gomphostilbia) sofiani Takaoka & Hashim, 2011

31

3.2

16.7

S. (Gomphostilbia) sp. (nr. parahiyangum)a

54

6.5

38.9

S. (Gomphostilbia) tanahrataense Takaoka et al., 2014

1

0.2

5.6

S. (Gomphostilbia) trangense Jitklang et al., 2008

312

6.7

38.9

S. (Gomphostilbia) whartoni Takaoka & Davies, 1995

709

21.5

77.8

S. (Nevermannia) aureohirtum Brunetti, 1911

103

4.9

27.8

S. (Nevermannia) caudisclerum Takaoka & Davies, 1995

17

0.7

5.6

S. (Nevermannia) sp. (nr. feuerborni) a

688

16.2

44.4

S. (Nevermannia) kurtaki Takaoka & Davies, 1995

1

0.2

5.6

S. (Simulium) bishopi Takaoka & Davies, 1995

305

13.2

77.8

S. (Simulium) brevipar Takaoka & Davies, 1995

122

10.6

77.8

S. (Simulium) digrammicum Edwards, 1928

1

0.2

5.6

S. (Simulium) grossifilum Takaoka & Davies, 1995

106

8.3

50.0

S. (Simulium) hackeri Edwards, 1928

96

3.7

11.1

S. (Simulium) hirtinervis Edwards, 1928

63

3.0

33.3

S. (Simulium) jeffreyi Takaoka & Davies, 1995

442

8.3

11.1

S. (Simulium) malayense Takaoka & Davies, 1995

188

8.6

38.9

S. (Simulium) nobile De Meijere, 1907

42

1.6

5.6

S. (Simulium) sp. (nr. grisescens)a

7

0.2

5.6

S. (Simulium) tani Takaoka & Davies, 1995

2669

31.7

66.7

S. (Simulium) yongi Takaoka & Davies, 1997

11

1.9

16.7

aUndetermined species, which probably are new species

Results

Black fly species composition

Thirty-five species were collected from 24 samplings at each of 18 fixed-stream sites (Table 2). The most frequently collected species (FO) were S. tani (31.7 %) and S. whartoni (21.5 %). Relatively common species were Simulium sp. (nr. feuerborni) (16.2 %), S. decuplum (15.5 %), S. angulistylum (14.8 %), S. bishopi (13.2 %) and S. izuae (11.8 %). Other species were collected at a frequency lower than 10 % and considered as rare. In terms of total individuals collected, S. tani, S. asakoae, S. whartoni and Simulium sp. (nr. feuerborni) were the four most abundant species. Based on stream occurrence (SO), S. whartoni, S. bishopi and S. brevipar were the widest distributed species (14 streams or 77.8 % each).

At the subgeneric level, Gomphostilbia was the most diverse subgenus found (19 species), followed by the Simulium (s. str.) (12 species) and the least was Nevermannia (four species). Of 18 species-groups in Malaysia, 17 were recorded in this study. The most abundant group was the S. asakoae species-group (six species), followed by the S. epistum species-group (four species). Other species groups were represented by one or two species.

Species richness and estimated richness are presented in Table 3. The maximum number of black fly species collected per total collections was 11 and the mean number was 3.2 ± 0.1 (SE). Total estimated species richness ranged between 39.8 and 41.3, which yielded more than 80 % sampling efficiency, while the estimated species richness for each stream ranged between 4.1 and 24.0, with 60 % sampling efficiency. Species reaching asymptote after approximately 54 samplings were performed, supporting the efficiency of the sampling method used in this study (Fig. 2).
Table 3

Actual and estimated species richness for 18 fixed-streams along an altitudinal gradient in Peninsular Malaysia. Numbers in parentheses indicate sampling efficiencya

Altitudes

Actual species

Mean richness (± SE)

Chao estimates

First Order Jackknife

All

35

3.15 ± 2.00

41.3 ± 2.08 (84.7)

39.8 ± 1.95 (87.9)

Low altitude

E1

13

2.52 ± 0.35

13.7 ± 1.10 (94.8)

16.8 ± 1.78 (77.4)

E2

15

3.43 ± 0.31

24.0 ± 7.69 (62.5)

21.7 ± 3.22 (69.1)

E3

15

3.50 ± 0.28

23.0 ± 8.31 (65.2)

19.8 ± 1.90 (75.7)

E4

8

3.21 ± 0.41

8.4 ± 0.95 (95.1)

8.95 ± 0.96 (89.4)

E5

12

4.30 ± 0.52

12.1 ± 0.92 (99.2)

12.9 ± 0.96 (93.0)

E6

14

4.64 ± 0.48

16.0 ± 3.01 (87.5)

16.9 ± 1.59 (82.8)

Middle altitude

E7

16

5.35 ± 0.67

18.0 ± 3.01 (88.8)

17.9 ± 1.33 (89.4)

E8

12

2.33 ± 0.40

16.1 ± 11.5 (74.5)

17.7 ± 2.07 (67.8)

E9

14

2.65 ± 0.33

20.3 ± 5.89 (68.9)

18.8 ± 1.95 (74.5)

E10

5

2.67 ± 0.67

7.0 ± 3.01 (71.4)

7.8 ± 2.87 (64.1)

E11

11

3.21 ± 0.32

13.6 ± 7.78 (80.9)

13.9 ± 1.59 (79.1)

High altitude

E12

11

3.44 ± 0.35

13.4 ± 0.60 (82.1)

12.9 ± 1.32 (85.2)

E13

16

3.34 ± 0.43

22.3 ± 5.89 (71.7)

20.8 ± 2.40 (76.9)

E14

13

3.36 ± 0.50

16.5 ± 5.66 (78.8)

17.8 ± 2.39 (73.0)

E15

13

2.83 ± 0.28

14.7 ± 6.17 (88.4)

20.7 ± 3.29 (62.8)

E16

4

1.69 ± 0.15

4.1 ± 0.37 (97.5)

4.09 ± 0.09 (97.8)

E17

7

1.29 ± 0.18

11.0 ± 1.03 (63.6)

11.2 ± 3.83 (62.5)

E18

10

1.76 ± 0.42

14.2 ± 6.82 (70.4)

16.1 ± 1.95 (62.1)

aSampling efficiency was calculated by dividing the number of actual species sampled by the number of estimated species [36]

Fig. 2

Species accumulation curve with error bars for overall 432 collections at 18 fixed-streams along an altitudinal gradient in Peninsular Malaysia

Species diversity and distribution patterns

Diversity indices are presented in Fig. 3. Species diversity and richness increased with altitude and declined at 1600 m and above. Diversity value was highest at stream E8 (0.87) followed by streams E12 (0.81), E5 (0.79) and E13 (0.78). The values were lower at streams E17 (0.35), E18 (0.33), E16 (0.27) and E15 (0.25). In contrast, dominance index (D) was highest at 1400 m and above (E15–E18).
Fig. 3

Diversity indices for black fly species along an altitudinal gradient in Peninsular Malaysia

Frequency of occurrence for distribution of 35 black fly species is presented in Table 4. Five patterns of species distribution were observed: (1) six species distributed at low altitude (159–423 m), (2) three species distributed from low to middle altitudes (159–1000 m), (3) two species distributed from middle to high altitudes (711–1813 m) (4) eight species distributed at high altitudes (1405–1813 m) and (5) 16 species distributed from low to high altitudes (Table 4).
Table 4

Frequency of occurrence per 24 collections for 35 black fly species along an altitudinal gradient in Peninsular Malaysia

Species

Site

Low

Middle

High

E1

E2

E3

E4

E5

E6

E7

E8

E9

E10

E11

E12

E13

E14

E15

E16

E17

E18

S. sp. (nr. grisescens)

 

1

                

S. duolongum

2

1

                

S. nobile

  

7

               

S. cheongi

2

1

3

 

5

             

S. adleri

     

3

            

S. jeffreyi

 

16

20

               

S. lurauense

 

2

2

 

3

1

5

1

          

S. yongi

   

3

  

2

 

3

         

S. sheilae

7

2

1

1

3

2

1

   

2

       

S. angulistylum

16

2

4

5

13

18

2

  

1

2

1

      

S. sp. (nr. parahiyangum)

1

15

6

 

3

1

  

1

   

1

     

S. roslihashimi

6

 

1

 

1

5

7

4

1

 

12

1

 

2

    

S. trangense

15

4

4

  

3

 

1

1

    

1

    

S. grossifilum

1

1

2

8

  

16

1

6

   

1

1

    

S. malayense

 

1

   

5

18

6

5

   

1

1

    

S. izuae

1

    

1

7

8

2

 

13

10

2

6

1

   

S. burtoni

 

5

7

           

1

   

S. hirtinervis

 

1

1

   

6

     

1

 

1

3

  

S. decuplum

2

 

2

 

16

14

10

 

3

  

9

6

2

1

 

1

 

S. gombakense

    

12

11

2

3

1

2

6

6

    

1

 

S. brevipar

2

  

2

2

8

3

2

5

3

2

5

3

7

  

1

1

S. bishopi

1

  

9

9

4

11

1

1

1

1

4

8

5

1

  

1

S. aureohirtum

2

           

4

 

4

10

 

1

S. tani

 

22

21

14

3

 

21

1

11

 

1

4

14

5

20

  

1

S. whartoni

 

1

1

3

14

19

6

3

2

1

15

9

5

11

1

 

1

1

S. asakoae

      

1

1

1

 

1

3

2

 

1

21

2

2

S. sp. (nr. feuerborni)

         

2

6

4

4

19

6

14

15

S. digrammicum

            

1

     

S. longitruncum

             

1

    

S. hackeri

            

9

 

7

   

S. sofiani

            

4

1

9

   

S. tanahrataense

              

1

   

S. brinchangense

                

2

1

S. kurtaki

                 

1

S. caudisclerum

                 

3

Of total collections (n = 432), 76.6 % (or 331) showed co-existence of species. Simulated and observed values of the C-score are presented in Fig. 4. The null model for co-occurrence indicates our observed index is above the simulated indices [observed index = 572.01, mean of simulated indices = 558.83, variance of simulated indices = 2.75, P (observed ≤ expected) = 1.00, P (observed ≥ expected) = 0.00], therefore, the distributional patterns of species were not considered random.
Fig. 4

Simulated and observed values of the C-score for the co-occurrence of the simuliid species collected at 18 fixed-streams along an altitudinal gradient in Peninsular Malaysia

Stream similarity based on species composition is presented in Fig. 5. Streams were clustered into two main groups with similarity value at 18 %: (1) ≤ 1345 m (E1–E14) and (2) ≥ 1405 m (E15–E18).
Fig. 5

Cluster analysis based on Sorenson’s coefficient for site similarity along an altitudinal gradient in Peninsular Malaysia

Means and coefficient of variations for eight measured physicochemical variables of all collections are presented in Table 5. PCA of all collections revealed five PCs, which have eigenvalues >1.0 accounted for 71.2 % of total inter-site variance of the physicochemical conditions (Table 6). Spearman’s rank correlations revealed that sites with higher PC-1 (which explained 24.8 % of the total variance) were at low altitude, normal water temperature (23–25 °C), wider, deeper, faster, low conductivity, higher discharge, more canopy cover and riparian vegetation and with larger streambed particles. Sites with higher PC-2 (which explained 15.1 % of the total variance) scores were at high altitude, cooler stream, wider, faster, higher dissolved oxygen with high discharge, less canopy cover and riparian vegetation. PC-3 accounted for 13.8 % of the total variance. Sites with higher PC-3 scores were at high altitude, cooler stream, smaller, slower, low conductivity and pH with low discharge and smaller streambed particles. PC-4 explained 9.3 % of the total intersite variance. Sites with higher PC-4 scores were at high altitude, cooler stream, high dissolved oxygen and pH with more canopy covers and larger streambed particles. PC-5 accounted for 8.4 % of the total variance. Streams with higher PC-5 scores were faster, high conductivity, low oxygen, more stream canopy covers and larger streambed particles. Regression analysis revealed that species richness was significantly associated with PC-1 (F = 20.8, df = 1, 422, P < 0.001).
Table 5

Physicochemical characteristics of all study sites presented as mean and coefficient of variation (CV)

Altitude/Site

Parameter

Temperature (°C)

Width (m)

Depth (m)

Velocity (m/s)

Conductivity (mS/cm)

Do (mg/l)

pH

Discharge (m3/s)

Low altitudes

E1

mean

25.03

0.89

0.14

0.42

0.15

4.84

6.51

0.07

 

CV

4.89

106.67

47.46

14.73

19.77

76.62

10.8

157.53

E2

mean

24.13

5.38

0.18

0.47

0.20

5.54

6.79

0.47

 

CV

3.12

16.83

31.99

27.11

18.72

68.29

8.88

55.1

E3

mean

24.26

5.7

0.16

0.44

0.18

5.67

6.93

0.41

 

CV

11.24

28.31

42.37

21.51

23.96

71.67

12.39

73.48

E4

mean

22.55

3.15

0.19

0.65

0.14

5.37

6.99

0.36

 

CV

5.09

49.38

39.05

32.43

11.39

53.42

11.00

61.65

E5

mean

23.92

0.61

0.12

0.59

0.44

5.94

6.85

0.04

 

CV

4.12

30.35

34.65

21.93

16.23

33.45

9.65

52.67

E6

mean

23.39

0.88

0.1

0.52

0.45

5.84

7.21

0.05

  

CV

5.33

30.8

59.13

30.36

21.99

30.43

8.08

78.22

Middle altitudes

E7

mean

21.68

1.64

0.22

0.53

0.34

5.54

7.31

0.23

 

CV

5.91

38.3

66.78

17.85

18.73

39.59

6.62

130.2

E8

mean

21.55

0.52

0.05

0.34

0.60

5.48

7.34

0.01

 

CV

4.09

38.99

29.14

23.56

13.75

38.92

7.12

53.62

E9

mean

20.21

1.81

0.14

0.52

0.33

6.35

7.24

0.15

 

CV

5.15

50.59

41.25

31.59

14.7

35.53

7.80

84.73

E10

mean

20.42

0.93

0.14

0.38

0.25

7.52

7.52

0.07

 

CV

2.98

61.12

44.8

6.00

50.00

7.63

5.13

73.4

E11

mean

19.36

0.37

0.09

0.35

0.43

5.18

7.06

0.01

  

CV

3.97

57.45

88.63

23.1

25.51

43.15

8.04

116.13

High altitudes

E12

mean

17.71

0.56

0.09

0.34

0.17

3.89

7.00

0.02

 

CV

5.69

38.44

41.64

24.44

49.45

59.67

8.67

75.36

E13

mean

18.65

3.07

0.07

0.67

0.53

6.27

7.19

0.16

 

CV

6.46

49.6

103.01

23.95

24.91

29.87

12.35

134.23

E14

mean

18.34

0.73

0.05

0.39

0.23

6.49

6.72

0.01

 

CV

4.13

15.05

34.01

42.18

18.2

18.95

11.36

54.31

E15

mean

17.22

3.17

0.22

0.54

0.18

5.94

6.79

0.37

 

CV

4.93

20.22

24.45

25.36

33.85

34.93

9.76

50.13

E16

mean

17.9

1.03

0.15

0.42

0.33

5.66

6.65

0.09

 

CV

6.72

127.76

43.18

28.77

51.06

32.39

10.53

194.76

E17

mean

17.99

0.62

0.14

0.33

0.18

5.23

6.56

0.02

 

CV

6.57

37.14

174.22

36.32

33.86

42.58

9.21

117.08

E18

mean

17.39

1.35

0.06

0.37

0.71

4.31

6.49

0.03

 

CV

8.09

129.59

38.68

69.16

93.68

61.94

8.78

154.23

Table 6

Principal component analysis and Spearman’s rank correlation coefficients between stream variables and principal components for all collections (n = 432)

Variable

Stream sites

Principal components

Min

Max

Mean ± SE

PC-1

PC-2

PC-3

PC-4

PC-5

Altitude (m)

159.00

1813.00

891.40 ± 30.1

−0.751**

0.268**

0.522**

0.224**

0.049

Temperature (°C)

14.40

28.40

25.30 ± 0.17

0.636**

−0.230**

−0.622**

−0.285**

−0.059

Width (m)

0.12

7.90

2.95 ± 0.11

0.692**

0.322**

−0.132**

0.044

0.037

Depth (m)

0.02

0.85

0.54 ± 0.005

0.616**

0.171

0.34

0.034

0.124

Velocity (m/s)

0.16

1.03

0.42 ± 0.009

0.416**

0.544**

−0.217**

0.115

0.360**

Conductivity (mS/cm)

0.01

0.15

3.48 ± 0.001

−0.297**

0.162

−0.327**

0.001

0.328**

Dissolved oxygen (mg/l)

1.27

17.20

15.77 ± 0.14

0.148

0.336**

−0.051

0.363**

−0.576**

pH

4.32

8.53

5.20 ± 0.04

0.036

−0.080

−0.337**

0.739**

−0.155

Discharge (m3/s)

0.002

1.56

2.23 ± 0.01

0.786**

0.376**

−0.208**

0.049

0.137

Canopy cover

open

complete

2a

0.213**

−0.718**

0.175

0.315**

0.306**

Riparian

open

forest

2a

0.400**

−0.763**

0.174

0.122

−0.015

Streambed

sand

bedrock

3a

0.449**

0.176

−0.503**

0.274**

0.366**

% Variance explained in PCA

      

Proportion

   

24.8

15.1

13.8

9.3

8.4

Cumulative

   

24.8

39.7

53.6

62.8

71.2

** P < 0.001

aMedian values given for riparian vegetation (1 = open, 2 = brush and 3 = forest), streambed-particle size (min; 1 = mud/silt and max; 6 = bedrock), and canopy cover. (1 = open, 2 = partial and 3 = complete). Rankings followed McCreadie et al. [34]

Regression analysis for four black fly species is presented in Table 7. Forward logistic regression analyses were conducted for eight species, which were found in more than 10 % (FO) of total collections. All regression models of species distribution except S. decuplum, S. izuae, S. brevipar and S. bishopi, were significant at P < 0.001 with correct classification varying from 73.4 to 85.3 %. Simulium whartoni was positively associated with PC-1 and PC-3. Simulium sp. (nr. feuerborni) was positively associated with PC-3, S. tani was positively associated with PC-1, PC-2 and PC-4. Simulium angulistylum was positively associated with PC-1 but negatively associated with PC-3 and PC-4. PC-5 was not related to any species.
Table 7

Regression analysis for the distribution of preimaginal black fly species at 18 fixed-streams along an altitudinal gradient in Peninsular Malaysia

Species

Regression coefficient

P

% Correct

K

PC-1

PC-2

PC-3

PC-4

PC-5

Simulium whartoni

-1.048

0.403

0.741

< 0.001

73.4

Simulium sp.(nr. feuerborni)

-1.449

1.214

< 0.001

78.1

Simulium tani

-0.461

1.837

0.590

0.393

0.001

79.3

Simulium angulistylum

-2.463

0.805

-2.012

−0.444

< 0.001

85.3

An ordination diagram for 18 fixed-stream sites and species are presented in Figs. 6 and 7, respectively. CCA indicated that temperature, stream size and discharge were the most important factors in differentiating streams from different altitudes. Therefore, these factors are good predictors for black fly species assemblages. The relationship between species and stream variable conditions was high (> 0.569) for the first three canonical axes, indicating that the variables used in this study were strongly related to black fly species assemblage. Temperature was the most important factor on the CCA axis 1. Species that associated with normal stream temperature were S. cheongi and S. trangense. The bottom left panel of the biplot is characterized by streams with wider and higher discharge. These sites were predominated by S. tani, S. nobile and S. jeffreyi. The upper right side of the biplot is composed of sites with lower discharge and smaller streams. Black fly species found predominantly at these sites were S. bishopi, S. izuae and S. longitruncum. The bottom right panel of the biplot is characterized by low water temperature, which is characteristic of high altitude streams. Black flies predominating at these sites were S. asakoae, S. caudisclerum and Simulium sp (nr. feuerborni) (Fig. 7).
Fig. 6

Ordination diagram of the first two axes of canonical correspondence analysis (CCA) of 432 sampling collections (open triangles represent low-altitude sites; closed triangles represent middle-altitude sites; and open square represent high-altitude sites). Arrows denote environmental variables with strength of the environmental condition indicated by arrow length of closeness to the CCA axis

Fig. 7

Ordination diagram of the first two axes of canonical correspondence analysis (CCA) of the 35 black fly species in Peninsular Malaysia

Discussion

This in-depth survey on the vertical distribution of black flies was conducted for the first time in Peninsular Malaysia and yielded 35 species, representing 42.7 % of total simuliids in Malaysia (82 species) [44]. Simulium digrammicum from Cameron Highland, an earlier described species and previously considered as locally extinct, was discovered in our study. These findings indicated that all sampled streams are the natural breeding habitats for black flies. As far as medico-veterinary importance of black flies is concerned, we collected S. (G.) asakoae, one of the well-known species that have been reported to be infected with filarial parasites in Thailand [45]. The host biting habits and vectorial capacity of Malaysian black flies, however, remain unknown.

Average number of species per total collections in this study was 3.2, slightly higher than previously reported [21, 25, 3032]. Our study also revealed 74.3 % of the sampled black flies as rare species (FO < 10 %). This pattern was consistent with our previous study in Peninsular Malaysia [31] and other geographical regions [26, 30, 46]. In contrast, 22.8 % (eight species) of total species had higher frequency of occurrence (10.6 to 31.7 %), stream occurrence (44.4 to 77.8 %) and total specimens collected, confirming the previous observations where the species were relatively abundant, on average and also widely distributed [47, 48]. The capability of these species to adapt over a broad range of stream physicochemical conditions has allowed them to occur almost in all places and become generalist. In contrast, rare species require a more specialised habitat, which consequently limits their distribution to certain streams conditions and defines them as specialist. This situation also corroborated with the prediction on taxa distribution [49] and neutral theory [50].

This study showed that the five principal components had eigenvalues > 1.0 and accounted for 71.2 % of total intersite variance of the stream condition variables. Streams at low altitude, with normal water temperature (23–25 °C), wider, deeper and faster, low conductivity, higher discharge, more canopy covers and riparian vegetations and with larger stream-bed particles, accommodate more preimaginal black fly species. Some of these core factors were consistent with previous studies [21, 23, 3032, 51, 52].

The first two CCA axes indicated the differentiation of low, middle and high altitude streams (Fig. 5). As expected, temperature, stream size and discharge are varied as altitude increases. The general conditions of low altitude streams were warmer, wider and higher discharge with average mean 23.8 °C, 2.9 m, and 0.2 m3/s respectively. High altitude sites were cooler (17.8 °C) than middle altitude sites (20.6 °C). However, the average mean for stream size and discharge observed both at middle (1.0 m; 0.09 m3/s) and high (1.5 m; 0.1 m3/s) altitudes were less different. In fact, stream sites at these altitudes are smaller and slower compared to low altitude sites. Our results showed gradual decrease of water temperature values as altitude increased. Dudgeon [53] suggested low water temperature as the most characteristic feature of high altitude streams. This finding is consistent with previous studies in other geographical regions [3, 4]. Besides, Tomanova et al. [4] reported stream depth as another factor that negatively related to altitude. Regarding difference in altitudes, Srisuka et al. [23] indicated that certain Thai simuliids occurred exclusively in a single zone while others were found in almost all gradients. This result was consistent with a previous study on other aquatic macroinvertebrates [3]. Our study corroborates this trend, species such as Simulium sp. (nr. feuerborni) of the subgenus Nevermannia and S. asakoae of the subgenus Gomphostilbia were found to be restricted to middle and high altitudes. The Thai S. asakoae, however, was distributed from low to high altitudes (500–2100 m) with predominance at low altitude [23]. Based on these distinct ecological conditions between Thai populations and Malaysian populations, coupled with the previous genetic evidence [54], we suggest the presence of cryptic species in S. asakoae. Three species in each of the subgenus Gomphostilbia (S. duolongum, S. cheongi and S. adleri) and the subgenus Simulium (S. nobile, S. jeffreyi and Simulium sp. nr. grisescens) were distributed at low altitude streams in our study (e.g. 159–423 m), further supporting previous published ecological data [55, 56]. Nearly half of total species collected (45.7 % or 16 species) were euryzonal or showing wide vertical distributions. These species, for example, S. brevipar, S. whartoni and S. bishopi were found occupying more than 80 % (16 streams) of total surveyed streams varying from low to higher altitudes. Simulium tani and S. angulistylum were found at 12 and ten altitudes, respectively (Table 4). A similar trend observed in Thai simuliid species such as S. yuphae were found distributed from 650–2534 m [23]. This wide vertical distribution pattern was also observed in other aquatic macroinvertebrates in temperate streams [53]. Black fly species that are widely distributed and adaptable in various physicochemical conditions are likely to be a species complex [57]. This situation has been highlighted in previous studies where the Thai S. tani and S. angulistylum were found to be a cytological species complexes [58, 59]. Our results revealed that some of the widespread species (i.e. S. tani, S. angulistylum, S. bishopi) along this altitudinal gradient were also commonly found in other locations in Peninsular Malaysia [31]. Cryptic diversity might be found in Malaysian samples and further cytogenetic and molecular studies would help to clarify this hypothesis.

The observed spatial distributions of preimaginal black flies in our study were predictable on the basis of stream-site characters. Our results revealed that distributions of four common species were related to altitude, temperature, stream size, velocity, streambed particle and discharge. Most of these factors are consistent with the patterns observed in tropical streams in the Oriental region and other geographical regions [21, 25, 30]. Temperature is a well-known variable that reversely correlated with altitude and has been widely associated with black fly distribution [21, 23, 25, 30, 32]. Moreover, Henriques-Oliveira & Nessimian [3] indicated that both temperature and stream size were the influencing factors in other aquatic insect distribution and composition in Southeastern Brazil. Based on our observation, species such as Simulium sp. (nr. feuerborni) was largely found in cooler streams (E11-E18) with temperature ranging between 14 °C and 19 °C and the mean between 17 °C and 19 °C. In a broader context, this species was reported as a species complex, which comprised cytoforms A and B in Thailand [60], cytoform C in Malaysia and cytoform D in Indonesia [61]. In particular, high frequency of B chromosome was detected in the S. feuerborni cytoform C, a unique character for temperate and arctic species [61]. Similarly, S. caudisclerum, S. hackeri, S. digrammicum and S. tanahrataense were inhabitants of cooler streams [55, 62]. The populations of these high-altitude specialists are fragmented and isolated at high altitude probably as a result of a glacial period, and thus, considered more vulnerable to extinction [8, 63]. In contrast, species such as S. cheongi was a common inhabitant at normal stream temperatures (23.9–25 °C) [55]. Based on our observation, the availability of larger streams with higher discharge rates gradually decreases with increasing altitude. Species such as S. jeffreyi and S. nobile, were largely associated with these stream characters and abundantly found at low altitude. In fact, stream velocity has been emphasized as one of the important factors determining the distribution of black fly larvae [21, 25, 30, 6466].

Species richness and composition are strongly interconnected with their habitat characteristics [30]. Species richness could increase with altitude but decline at above 1500 m [2, 6, 7, 23, 67]. Our study corroborated these trends, where species richness started to decline at 1405 m. Similar results found with other macroinvertebrates where taxa richness starts to decrease as altitude increases [3, 6870]. Regarding species composition observed in cluster analysis, there was a marked separation of streams at similar altitude (1405 m). This reflects the remarkable change of abiotic factors particularly in the observed water temperature and thus creates a boundary for most of the species with narrow ecological tolerance except several generalists and highly specialised taxa or high-altitude specialists. A similar pattern was reported in other macroinvertebrate studies along altitudinal gradients [70, 71].

Regards to species co-occurrence pattern, the assemblages observed in this study were not random and most of our samplings recorded the co-existence of species (76.6 % or 331), implying high stream environmental heterogeneity (e.g. larger stream with larger streambed particles) [25]. However, species co-existence could also be found in homogenous streams with respect to the availability of microhabitats [40]. A group of species in this particular stream would require similar microhabitat preference [40, 72]. A recent study indicated that habitat filtering is a major factor that shaping community structure of black flies in tropical streams [66]. Co-existence species usually show similar morphological traits that associate with stream conditions. Our results revealed that most of the co-occurring species possess similar labral fan morphologies, the food-filtering organ that is strongly associated with stream conditions [73]. These species for example, S. angulistylum and S. trangense were found co-existing in 50 % of total samplings, while other species such as S. tani, S. jeffreyi and Simulium sp (nr. parahiyangum) were found co-occurring in 46 % of total samplings. Therefore, patterns of species assemblage of the black flies in tropical streams in Malaysia mirror previous findings in Thailand and suggest that ecological conditions of the larval habitat play a significant role in determining black fly species assemblage.

Conclusions

In conclusion, this comprehensive surveillance on the vertical distribution of black flies was conducted for the first time in Peninsular Malaysia and yielded 35 species, representing 42.7 % of total simuliids in Malaysia. The current study has provided new insight into the distribution patterns of preimaginal black fly along an altitudinal gradient in Peninsular Malaysia. Our results indicated that physicochemical characteristics of the stream habitats that are associated with black fly distribution (e.g. stream size, velocity and temperature) varied along an altitudinal gradient. Thus, species diversity and assemblages varied accordingly. We found that certain black fly species are habitat specialists, whereas some are habitat generalists and distributed in wide range of ecological conditions. These species are likely to contain cryptic taxa and further taxonomic study using cytogenetic and molecular methods are required to support this hypothesis. Moreover, this study could deepen our knowledge on the ecology and biology of the specialised taxa in response to environmental changes.

Declarations

Acknowledgements

Thanks are due to Nor Azhar Jamil and Muhammad Rasul Abdullah Halim (Institute Biological Sciences, University of Malaysia) for field assistance. This work was supported by research grants from the University of Malaya (Project No. PG084-2014B and RP003A-13SUS). This project is a part of the first author’s PhD research at the University of Malaya, Kuala Lumpur.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Institute of Biological Sciences, Faculty of Science, University of Malaya
(2)
Department of Biology, Faculty of Science, Mahasarakham University

References

  1. Hodkinson ID. Terrestrial insects along elevation gradients: species and community responses to altitude. Biol Rev. 2005;80(3):489–513.View ArticlePubMedGoogle Scholar
  2. McCain CM, Grytnes JA. Elevational gradients in species richness. In: Encyclopedia of Life Sciences (ELS). Chichester: John Wiley & Sons, Ltd; 2010.Google Scholar
  3. Henriques-Oliveira AL, Nessimian JL. Aquatic macroinvertebrate diversity and composition in streams along an altitudinal in Southeastern Brazil. Biota Neotrop. 2010;10(3):115–28.View ArticleGoogle Scholar
  4. Tomanova S, Tedesco PA, Campero M, Van Damme PA, Moya N, Oberdorff T. Longitudinal and altitudinal changes in macroinvertebrate functional feeding groups in Neotropical streams: a test of the river continuum Concept. Fundamental and applied limnology, arch. Hydrobiol. 2007;170(3):233–41.Google Scholar
  5. Barry RG. Mountain climatology and past and potential future climatic changes in mountain regions – a review. Mt Res Dev. 1992;12(1):71–86.Google Scholar
  6. Wolda H. Altitude, habitat and tropical insect diversity. Biol J Linn Soc. 1987;30(4):313–23.View ArticleGoogle Scholar
  7. McCoy DE. The distribution of insects along elevational gradients. Oikos. 1990;58(3):313–22.View ArticleGoogle Scholar
  8. Adler PH, Cheke RA, Post RJ. Evolution, Epidemiology, and population genetics of black flies (Diptera: Simuliidae). Infect Gen Evol. 2010;10(7):846–65.Google Scholar
  9. Crosskey RW. The natural history of blackflies. Chichester: John Wiley & Sons Inc; 1990. p. ix+711 pp.Google Scholar
  10. Adler PH, Currie DC, Wood DM. The black flies (Simuliidae) of North America. Ithaca: Cornell University Press; 2004.Google Scholar
  11. Currie DC, Adler PH. Global diversity of black flies (Diptera: Simuliidae) in freshwater. Hydrobiol. 2008;595(1):469–75.View ArticleGoogle Scholar
  12. Pramual P, Chaliow K, Baimai V, Walton C. Phylogeography of the black fly Simulium tani (Diptera: Simuliidae) from Thailand as inferred from mtDNA sequences. Molecular Ecol. 2005;14(13):3989–4001.View ArticleGoogle Scholar
  13. Low VL, Adler PH, Takaoka H, Ya’cob Z, Lim PE, et al. Mitochondrial DNA markers reveal high genetic diversity but low genetic differentiation in the black fly Simulium tani Takaoka & Davies along an elevational gradient in Malaysia. PLoS ONE. 2014;9(6):e100512. doi:10.1371/journal.pone.0100512.View ArticlePubMedPubMed CentralGoogle Scholar
  14. Dufrene M, Legendre P. Species assemblages and indicator species: The need for a flexible asymmetrical approach. Ecol Monogr. 1997;67(3):345–66.Google Scholar
  15. Hart DD. The adaptive significance of territoriality in filter-feeding larval black flies (Diptera: Simuliidae). Oikos. 1986;46(1):88–92.View ArticleGoogle Scholar
  16. Colbo MH, Porter GN. Effects of the food supply on the life history of Simuliidae (Diptera). Can J Zool. 1979;57(2):301–6.View ArticleGoogle Scholar
  17. Ciborowski JJ, Adler PH. Ecological segregation of larval black flies (Diptera: Simuliidae) in northern Saskatchewan, Canada. Can J Zool. 1990;68(10):2113–22.View ArticleGoogle Scholar
  18. Halgos J, Illésová D, Krno I. The effect of some ecological factors on longitudinal patterns of black fly community structure (Diptera: Simuliidae) in a foothill stream. Biologia. 2001;56(5):513–23.Google Scholar
  19. McCreadie JW, Colbo MH. Larval and pupal microhabitat selection by Simulium truncatum Lundström, S. rostratum Lundström and S. verecundum AA (Diptera: Simuliidae). Can J Zool. 1993;71(2):358–67.Google Scholar
  20. Figueiró R, Nascimento ES, Gil-Azevedo LH, Maia-Herzog M, Monteiro RF. Local distribution of blackfly (Diptera: Simuliidae) larvae in two adjacent streams: the role of water current velocity in the diversity of blackfly larvae. Rev Bras Entomol. 2008;52(3):452–4.View ArticleGoogle Scholar
  21. McCreadie JW, Adler PH, Hamada N. Patterns of species richness for blackflies (Diptera: Simuliidae) in the Nearctic and Neotropical regions. Eco Entomol. 2005;30(2):201–9.View ArticleGoogle Scholar
  22. Nascimento ES, Figueiró R, Becnel JJ, Araújo-Coutinho CJPC. Influence of temperature on microsporidia infections in a natural population of Simulium pertinax Kollar, 1832 (Diptera; Simuliidae). Braz J Biol. 2007;67(3):519–26.View ArticlePubMedGoogle Scholar
  23. Srisuka W, Takaoka H, Otsuka Y, Fukuda M, Thongsahuan S, Taai K, Choochote W, Saeung A. Seasonal biodiversity of black flies (Diptera: Simuliidae) and evaluation of ecological factors influencing species distribution at Doi Pha Hom Pok National Park, Thailand. Act Trop. 2015;149:212–9. doi:10.1016/j.actatropica.2015.05.024.View ArticleGoogle Scholar
  24. Tate CM, Heiny JS. The ordination of benthic invertebrate communities in the South Platte River basin in relation to environmental factors. Freshwater Biol. 1995;33(3):439–54.View ArticleGoogle Scholar
  25. Pramual P, Kuvangkadilok C. Agricultural land use and black fly (Diptera: Simuliidae) species richness and species assemblages in tropical streams, Northeastern Thailand. Hydrobiol. 2009;625(1):173–84.View ArticleGoogle Scholar
  26. Couceiro SRM, Hamada N, Sagot LB, Pepinelli M. Black-fly assemblage distribution in streams in disturbed areas in southern Brazil. Act Trop. 2014;140:26–33. doi:10.1016/j.actatropica.2014.07.018.View ArticleGoogle Scholar
  27. McCreadie JW, Adler PH. Scale, time, space, and predictability: species distributions of preimaginal black flies (Diptera: Simuliidae). Oecologia. 1998;114(1):79–92.View ArticleGoogle Scholar
  28. McCreadie JW, Adler PH. Ecoregions as predictors of lotic assemblages of blackflies (Diptera: Simuliidae). Ecography. 2006;29(4):603–13.View ArticleGoogle Scholar
  29. Hamada N, McCreadie JW. Environmental factors associated with the distribution of Simulium perflavum (Diptera: Simuliidae) among streams in Brazilian Amazonia. Hydrobiol. 1999;397:71–8.Google Scholar
  30. Hamada N, McCreadie JW, Adler PH. Species richness and spatial distribution of blackflies (Diptera: Simuliidae) in streams of Central Amazonian, Brazil. Fresh Biol. 2002;47(1):31–40.View ArticleGoogle Scholar
  31. Ya’cob Z, Takaoka H, Pramual P, Low VL, Sofian-Azirun M. Breeding habitat preference of black flies (Diptera: Simuliidae) in Peninsular Malaysia. Act Trop. 2016;153:57–63. doi:10.1016/j.actatropica.2015.10.007.View ArticleGoogle Scholar
  32. Pramual P, Wongpakam K. Seasonal variation of black fly (Diptera: Simuliidae) species diversity and community structure in tropical streams of Thailand. Entomol Sci. 2010;13(1):17–28.View ArticleGoogle Scholar
  33. McCreadie JW, Colbo MH. Spatial distribution patterns of larval cytotypes of the Simulium venustum/verecundum complex (Diptera: Simuliidae) on the Avalon Peninsula, Newfoundland: factors associated with occurrence. Can J Zool. 1991;69(10):2651–9.View ArticleGoogle Scholar
  34. Takaoka H. The Black flies (Diptera: Simuliidae) of Sulawesi, Maluku and Irian Jaya. Fukuoka: Kyushu University Press; 2003. p. xxii + 581 pp.Google Scholar
  35. McCreadie JW, Adler PH, Grillet ME, Hamada N. Sampling statistics in understanding distributions of black fly larvae (Diptera: Simuliidae). Act Entomol Serb. 2006;11(Suppl):89–96.Google Scholar
  36. Brühl CA, Gunsalam G, Linsenmair KE. Stratification of ants (Hymenoptera, Formicidae) in a primary rain forest in Sabah, Borneo. J Trop Eco. 1998;14(3):285–97.View ArticleGoogle Scholar
  37. Brühl CA. Leaf litter ant communities in tropical lowland rain forests in Sabah, Malaysia: Effects of forest disturbance and fragmentation. PhD Thesis. Würzburg: Julius-Maximilians-Universität; 2001.Google Scholar
  38. Gotelli NJ, Entsminger GL. EcoSim: null models software for ecology, v.7. Acquired Intelligence Inc and Kesey-Bear [software on the Internet]. Published by the authors. [cited 2015 Sept 20]. Available from: garyentsminger.com/Ecosim; 2009.Google Scholar
  39. Stone L, Roberts A. The checkerboard score and species distributions. Oecologia. 1990;85(1):74–9.View ArticleGoogle Scholar
  40. Figueiró R, Gil-Azevedo LH, Maia-Herzog M, Monteiro RF. Diversity and microdistribution of black fly (Diptera: Simuliidae) assemblages in the tropical savanna streams of the Brazilian Cerrado. Mem Inst Oswaldo Cruz. 2012;107(3):362–9.View ArticlePubMedGoogle Scholar
  41. McCreadie JW, Hamada N, Grillet ME. Spatial-temporal distribution of preimaginal blackflies in Neotropical streams. Hydrobiol. 2004;513(1):183–96.View ArticleGoogle Scholar
  42. McCune B, Mefford MJ. PC-ORD. Multivariate Analysis of Ecological Data, Version 5.14. Gleneden Beach: MjM Software; 2006.Google Scholar
  43. Seaby RM, Henderson PA. Species Diversity and Richness, Ver. 4. 2006.Google Scholar
  44. Adler PH, Crosskey RW. World blackflies (Diptera: Simuliidae): A comprehensive revision of the taxonomic and geographical inventory [2015]. 120 pp. Available from: http://www.clemson.edu/cafls/biomia/pdfs/blackflyinventory.pdf (accessed on 1st July 2015).
  45. Fukuda M, Choochote W, Bain O, Aoki C, Takaoka H. Natural infections with filarial larvae in two species of black flies (Diptera: Simuliidae) in northern Thailand. Jpn J Trop Med Hyg. 2003;31(2):99–102.View ArticleGoogle Scholar
  46. McCreadie JW, Adler PH. Spatial distribution of rare species in lotic habitats. Insect Conserv Divers. 2008;1(3):127–34.View ArticleGoogle Scholar
  47. Gotelli NJ, Simberloff D. The distribution and abundance of tallgrass prairie plants: a test of the core- satellite hypothesis. Am Nat. 1987;130(1):18–35.View ArticleGoogle Scholar
  48. Gaston KJ, Lawton JH. Patterns in the distribution and abundance of insect populations. Nature. 1988;331(6158):709–12.View ArticleGoogle Scholar
  49. Hutchinson GW. Concluding remarks. Cold Spring Harb Symp Quant Biol. 1957;22:415–27.View ArticleGoogle Scholar
  50. Hubbell SP. Neutral theory in community ecology and the hypothesis of functional equivalence. Funct Ecol. 2005;19(1):166–72.View ArticleGoogle Scholar
  51. Grillet ME, Barrera R. Spatial and temporal abundance, substrate partitioning and species co-occurrence in a guild of Neotropical blackflies (Diptera: Simuliidae). Hydrobiol. 1997;345(2):197–208.View ArticleGoogle Scholar
  52. Scheder C, Waringer JA. Distribution patterns and habitat characterization of Simuliidae (Insecta: Diptera) in a low-order sandstone stream (Weidlingbach, Lower Austria). Limnologica. 2002;32(3):236–47.View ArticleGoogle Scholar
  53. Dudgeon D. Tropical Stream Ecology. London: Academic; 2008. p. 316.Google Scholar
  54. Low VL, Takaoka H, Adler PH, Ya’cob Z, Norma-Rashid Y, Chen CD, Sofian-Azirun M. A multi-locus approach resolves the phylogenetic relationships of the Simulium asakoae and Simulium ceylonicum species groups in Malaysia: evidence for distinct evolutionary lineages. Med Vet Entomol. 2015;29(3):330–7.View ArticlePubMedGoogle Scholar
  55. Takaoka H, Davies DM. The Black Flies (Diptera: Simuliidae) of West Malaysia. Fukuoka: Kyushu University press; 1995. p. viii + 175pp.Google Scholar
  56. Jitklang S, Kuvangkadilok C. A new species of Simulium (Gomphostilbia) (Diptera: Simuliidae) from southern Thailand, with description of its polytene chromosomes. Stud Dipterol. 2007;14(2):369–75.Google Scholar
  57. Adler PH, McCreadie JW. The hidden ecology of black flies: Sibling species and ecological scale. Am Entomol. 1997;43(3):153–62.View ArticleGoogle Scholar
  58. Tangkawanit U, Kuvangkadilok C, Baimai V, Adler PH. Cytosystematics of the Simulium tuberosum group (Diptera; Simuliidae) in Thailand. Zool J Linn Soc. 2009;155(2):289–315.View ArticleGoogle Scholar
  59. Pramual P, Kuvangkadilok C. Integrated cytogenetic, ecological, and DNA barcode study reveals cryptic diversity in Simulium (Gomphostilbia) angulistylum (Diptera: Simuliidae). Genome. 2012;55(6):447–58.View ArticlePubMedGoogle Scholar
  60. Pramual P, Wongpakam K. Population genetic of the high elevation black fly Simulium (Nevermannia) feuerborni Edwards in Thailand. Entomol Sci. 2013;16(3):298–308.View ArticleGoogle Scholar
  61. Pramual P, Thaijaren J, Sofian-Azirun M, Ya’cob Z, Hadi UK, Takaoka H. Cytogenetic and molecular evidence of additional cryptic diversity in high elevation black fly Simulium feuerborni (Diptera: Simuliidae) populations in Southeast Asia. J Med Entomol. 2015;52(5):829–36.View ArticlePubMedGoogle Scholar
  62. Takaoka H, Sofian-Azirun M, Ya’cob Z, Hashim R. Two new species of Simulium (Gomphostilbia) (Diptera: Simuliidae) from Cameron’s Highlands, Peninsular Malaysia, with keys to 21 species of the Simulium asakoae species-group. Zootaxa. 2014;3765(1):054–68.View ArticleGoogle Scholar
  63. Finn DS, Adler PH. Population genetic structure of a rare high-elevation black fly, Metacnephia coloradensis, occupying Colorado lake outlet streams. Freshwater Biol. 2006;51(12):2240–51.View ArticleGoogle Scholar
  64. Zhang Y, Malmqvist B, Englund G. Ecological process affecting community structure of blackfly larvae in regulated and unregulated rivers: a regional study. J App Ecol. 1998;35(5):673–86.View ArticleGoogle Scholar
  65. Palmer RW, Craig DA. An ecological classification of primary labral fans of filter-feeding black fly (Diptera: Simuliidae) larvae. Can J Zool. 2000;78(2):199–218.Google Scholar
  66. Pangjanda S, Pramual P. Trait-based and phylogenetic community ecology of black flies (Diptera: Simuliidae) in Tropical streams of Thailand. Hydrobiol. 2015;763(1):345–56.View ArticleGoogle Scholar
  67. Lawton JH, MacGarvin M, Heads PA. Effects of altitude on the abundance and species richness of insect herbivores on bracken. J Anim Ecol. 1987;56(1):147–60.View ArticleGoogle Scholar
  68. Jacobsen D, Schultz R, Encalada A. Structure and diversity of stream invertebrate assemblages: the influence of temperature with altitude and latitude. Freshwater Biol. 1997;38(2):247–61.View ArticleGoogle Scholar
  69. Jacobsen D. Contrasting patterns in local and zonal family richness of streams invertebrates along an Andean altitudinal gradient. Freshwater Biology. 2004;49(10):1293–305.View ArticleGoogle Scholar
  70. Huamantinco AA, Nessimian JL. New Neotropical genus and species of Odontocerinae (Trichoptera: Odontocerinae) from Southeastern Brazil. Aquatic Insect. 2004;26(3/4):281–8.View ArticleGoogle Scholar
  71. Palmer C, Palmer A, O’Keeffe J, Palmer R. Macroinvertebrate community structure and altitudinal changes in the upper reach of warm temperate southern African river. Freshwater Biol. 1994;32(2):337–47.View ArticleGoogle Scholar
  72. Pramual P, Kuvangkadilok C, Jitklang S, Tangkawanit U, Adler PH. Geographical versus ecological isolation of closely related black flies (Diptera: Simuliidae) inferred from phylogeny, geography, and ecology. Org Divers Evol. 2012;12(2):183–95.View ArticleGoogle Scholar
  73. Zhang Y, Malmqvist B. Relationships between labral fan morphology and habitat in North Swedish blackfly larvae (Diptera: Simuliidae). Biol J Linn Soc. 1996;59(3):261–80.Google Scholar

Copyright

© Ya’cob et al. 2016

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Advertisement