Open access peer-reviewed chapter - ONLINE FIRST

Vertical Arthropod Dynamics across Organic Matter Fractions in Relation to Microclimate and Plant Phenology

By María F. Barberena-Arias and Elvira Cuevas

Submitted: July 9th 2020Reviewed: October 26th 2020Published: December 29th 2020

DOI: 10.5772/intechopen.94747

Downloaded: 34


Plant diversity is a key factor influencing belowground dynamics including microclimate and decomposer arthropod communities. This study addresses the effect of individual plant species on belowground arthropods by focusing on seasonal variations in precipitation, temperature and arthropods along the vertical organic matter profile. In the Guanica Dry Forest, Puerto Rico, microclimate was described and 5 plant species and 10 trees/species were selected. Under each tree, for one year, temperature was measured and samples collected along the organic matter fractions. Collected arthropods were standardized to ind/m2, identified to Order/Family and assigned to morphotypes. The annual temperature pattern was similar for all species and OM fractions. Arthropod abundance was similar among plant species and higher in humus than in litter fractions. Richness and species composition were different among plant species and OM fractions. All plant species and OM fractions showed low arthropod abundance and richness, and similar arthropod species composition in the dry season, while in the wet season abundance and richness were higher and species composition varied across plant species and OM fractions. These data suggest that arthropods form specific assemblages under plant species and stages of decomposition that, during the dry season, represent a subgroup adapted to extreme environmental conditions.


  • Berlese funnels
  • decomposition
  • extreme conditions
  • plant structure
  • seasonal dynamics

1. Introdution

Individual trees modify the below microclimate, resources and associated biodiversity [1]. Examples of trees modifying the below microclimate include trees serve as wind shelters [2], canopies intercept rainfall [3] and solar radiation [2, 4, 5, 6] resulting in calm drier and warmer below microclimate in comparison to nearby open areas. Through leaf fall, trees influence belowground microclimate and litter quantity and quality, for example dark litter warms more than light-colored litter [7], deciduous species show a pulse in litter production [8] and litter C: N and C:P ratios directly influence soil N and P [9] and N transformation rates [10]. In addition, physicochemical changes that happen during litter decomposition [11] are paralleled in the vertical stratification of the organic matter (OM) [12]. As a consequence, trees produce spatial variation in microenvironments and a patchy distribution of litter that is vertically stratified into progressive decomposition stages [13].

The distribution of trees results in a patchy distribution of litter and associated organisms [14, 15, 16]. For example, microarthropods were more abundant in aspen than in pine [17] earthworms were abundant under Qualea and completely absent under Dicorynia guianensis [15] and Heliconia caribaea [18]. On the other hand, OM in early decomposition was dominated by cellulolitic bacteria, omnipotent fungi and collembolans while later on, by proteolytic bacteria, no-lygnolitic fungi, diplopoda and isopoda [19]. Cryptostigmata and collembolans dominated late decomposition [20], while others report Cryptostigmata diversity to be independent of the decomposition stage [21] but mites and collembolans species composition changed simultaneously with a decrease in collembolan abundance as decomposition proceeded [22]. In Guanica, enzymatic activity and microbe diversity were consistently different between Tabebuia, an evergreen facultative deciduous species, and Ficus and Pisonia, two deciduous species [23]. In this forest, arthropod species composition was different among tree species, and this difference was better explained by detritivores that covaried with the physicochemical characteristics of mature green leaves [24].

With the overwhelming impact of human activities on biodiversity [25, 26], an enormous amount of studies (e.g. [25, 27, 28, 29, 30] are only few examples) have addressed the relationship between diversity and ecosystem processes at the stand and local scales, but few happen at the individual tree species scale [23, 24, 31]. Therefore, we evaluated how aboveground diversity, specifically individual tree species, modify microclimate and litter, and the relation to the dynamics and diversity of belowground arthropods in the vertical organic matter profile. For this, we selected isolated individual trees, such as those found in the Guanica dwarf forest, that provide the ideal setting to study tree species in complete isolation [24].

2. Methodology

2.1 Study site

The study was conducted in the Guánica dry forest, a Biosphere Reserve established in 80’s (for the specific location, please see Figure 1 in [31]) that has been disturbed by humans (urbanization, selective logging and charcoal pits) [32, 33] and hurricanes such as Hurricane Maria in 2017, but at the time of the study, the eyes of less severe hurricanes, such as Hortense (1995) and Georges (1998), had crossed the forest [34]. Average temperature is 25.1°C and precipitation is 860 mm (range 288–1348 mm) but the monthly distribution of the rain is highly erratic [35]. In average, there are 6 dry months (3–8 mo.) that occur as one major dry period from December to April and a minor one from June to August [36]. The forest presents several vegetation associations [37, 38, 39], the coastal vegetation is an open forest that occurs on limestone hills with rock depressions that accumulate highly fertile organic matter [33] where trees grow dwarf and isolated from neighbor trees by exposed rock [39]. Here five representative tree species were selected: Coccoloba uvifera (evergreen) and Conocarpus erectus (evergreen) both present near the cliff shore, and Tabebuia heterophylla (facultative deciduous), Pisonia albida (obligate deciduous) and Ficus citrifolia (deciduous) which are present from the coast to the upper ridges in the forest. Ten trees belonging to each species were selected for a total of 50 trees which represent the sampling units. Under each tree, organic matter samples were collected and divided into fractions showing progressive stages of decomposition that were loose litter (recently fallen leaves), old litter (fragmented leaves) and humus. During the study (from September 2004 to November 2005), the structure of each tree was measured once while microclimate was measured and arthropods were collected under each tree for one year encompassing wet and dry periods, the wet period received unusual high precipitation. The data for each fraction was kept separately.

2.2 Mesoclimate

Mesoclimate was characterized by using data obtained from the NRCS [40] and RAWS sites [41]. The first site is a SCAN (Soil Climate Analysis Network) weather station that is located in an open area in the forest and up 165 m from the coastal plateau while the second site is a RAWS (Remote Automated Weather Stations) weather station located near the coast but the data for the study period is too fragmentary. Temperature and precipitation were taken from the SCAN site, except for October 2005, that is missing and was then was calculated by difference between the previous and next month. This datum was corroborated with the RAWS site. This information is presented in Figure 1.

Figure 1.

Diagram showing the relation between average monthly temperature (dashed line) and total monthly precipitation (solid line) in the Guánica forest during the study period. When the solid line is below the dashed one, this indicates water deficit (dry period), when the solid line is above the dashed one this indicates water availability (wet period), and when precipitation surpasses 100 mm, this indicates water surplus. Please note that months begin in September 2004.

2.3 Microclimate

Microclimate data was obtained from data loggers. For each of the study species, three (out of five) trees were selected to ensure trees were interspersed within the study area and therefore represented the local variation. Data loggers were placed under each tree as follows: one HOBO temperature/humidity data logger was placed at 25 cm above ground, and will be referred as understory temperature throughout the chapter, one TidBit temperature data logger was placed in the old litter fraction (∼2.5 cm depth) and one TidBit temperature data logger was placed in the humus fraction (∼4.5 cm depth). Data loggers collected temperature data every hour from September 2004 to November 2005. Although the HOBO data logger collected temperature and humidity, only temperature is presented because the humidity sensor shorted out as it was exposed to a salty environment and produced unreliable data.

2.4 Tree species characterization

Each tree was characterized by measuring tree height, canopy area, organic matter dry mass and depth. Tree height was measured as the distance between the ground and the highest canopy point in vertical orientation, and canopy length and width were measured in horizontal orientation. Canopy area was calculated from length and width. Organic matter dry mass and fraction depth were measured separately for each tree and fraction in November 2004, February, April, June, September and November 2005.

2.5 Arthropods

Arthropod collections were performed under each tree on November 2004, February, April, June, and September 2005. During each sampling, one 10 cm x 10 cm sample/tree/species was collected, and the sample was separated into three fractions: loose litter, old litter and humus. Each fraction was kept separately and placed in a Berlese funnel for one week for arthropod extraction using light [42]. This sampling design gave 5 species x 10 trees x 3 fractions x 5 samplings = 750 samples. Collected arthropods were taxonomically identified to the lowest category possible, either class, subclass, order or suborder, and classified as adult or immature, and assigned to a morphospecies. Collembolans were not assigned to morphotypes since variation in the morphology can only be seen in mounted slides and by a specialist. The abundance of each morphospecies was recorded and standardized to number of individuals per square meter. Morphospecies composition was used as a surrogate for species composition.

2.6 Statistical analyses

Anovas were used to evaluate differences in tree structure and organic matter among plant species. Also, a two-way Anova was used to establish the effect of plant species and fraction on the modulation of temperature. Modulation was estimated as the maximum, minimum and daily range in temperature. To assess the effect of tree structure on temperature modulation we used spearman rank correlations. To assess the effect of time (sampling dates), plant species and fraction on the abundance and richness of arthropods three-way AOV were used. The abundance of arthropod morphotypes was used in a Multi-Response Permutation Procedure (MRPP) to evaluate the effect of time, plant species and fraction on the species composition of adult arthropods. MRPP is a non-parametric test that calculates a distance matrix of average observed distance within predefined groups and compares it to an average distance expected by chance. Within group distance is used to calculate A, a homogeneity parameter of within group variability that ranges between zero and one. In community ecology, values close to zero are common and suggest a heterogenous community that can still be different from other communities [24, 43, 44, 45].

3. Results and discussion

3.1 Mesoclimate

During the study, temperature decreased from September to February, and then it increased again (Figure 1) and total precipitation was 1575 mm, and was distributed as follows: 480 mm from September to October 2004 producing wet conditions at the end of 2004, 120 mm between November 2004 and April 2005 producing dry conditions at the beginning of 2005, and 975 mm between May and October 2005 producing wet conditions in the second half of 2005 that were interrupted by a short water deficit in June 2005 (Figure 1). In general, during the study, the pattern was similar to the one historically established for the Guanica forest [36, 46] with some variations. Historically, the dry season runs from January to July interrupted by a small pulse in precipitation in May, and then follows a wet season from August to December with water surplus during September. This study encompassed two wet periods and one dry period, there was water surplus in September and October 2004, followed by dry months up to April 2005 indicating 6 consecutive months of water deficit. Then, there were wet conditions after April 2005 with three pulses of water surplus in May, July and October 2005. This indicates that during the study, precipitation was atypical because dry conditions lasted 6 consecutive months while Lugo et al. [36] found that, for this forest, dry conditions usually last 3–4 consecutive months. In addition, historically the wet season usually presents one pulse of water surplus but during wet 2005 there were three pulses in precipitation. These data confirm that precipitation in Guánica is highly erratic [35] and show that the 2004–2005 months encompassed in this study represent extreme dry conditions followed by extreme wet conditions.

3.2 Microclimate

3.2.1 Variation among tree species

There was a significant effect of plant species and fraction on maximum, minimum and temperature range (Table 1, Figure 2). Understory maxima temperatures ranged between 35.9°C and 34.9°C and followed the pattern Conocarpus > Pisonia > Tabebuia, Ficus, Coccoloba while minima followed the pattern Tabebuia ≥ Coccoloba, Ficus, Pisonia ≥ Conocarpus. Therefore, in the understory, temperature daily range was largest in Conocarpus intermediate in Pisonia and smallest in Ficus, Tabebuia, Coccoloba. In the litter, maxima temperatures ranged between 37.8°C and 30.2°C, and followed the pattern Conocarpus > Ficus, Pisonia > Pisonia, Tabebuia > Coccoloba. Minima temperatures were Pisonia ≥ Ficus ≥ Conocarpus ≥ Tabebuia ≥ Coccoloba, therefore temperature daily range in the litter was largest in Conocarpus, intermediate in Ficus followed by Pisonia and Tabebuia, and smallest in Coccoloba. In the humus, maxima ranged between 31.7°C and 28.2°C and the pattern was Conocarpus, Ficus > Pisonia, Tabebuia > Coccoloba and in the humus between. Minima were Conocarpus > Pisonia > Tabebuia > Ficus and Coccoloba. Therefore, humus temperature daily range showed the pattern Ficus > Conocarpus > Tabebuia > Pisonia > Coccoloba.

Species x fraction8149<0.00127<0.001142<0.001

Table 1.

Effect of plant species and fraction (location: understory, litter, humus) on the magnitude of temperature fluctuations during the study period. Anovas were run using daily maximum, minimum and temperature range. There were 435 days (from September 8, 2004 to November 16, 2005).

Figure 2.

Mean monthly temperature (±s.e.) in the understory, litter and humus below the five tree species. (understory: ∼25 cm understory, Litter: inside the old litter fraction ∼2.5 cm depth, Humus: inside the humus fraction ∼4.5 cm depth). Symbols represent mean values and whiskers represent standard error.

Throughfall and stem flow are affected by tree size and differentially moisten soil under canopies [47] while surrounding rock similarly warms the OM underneath trees [48]. In this study temperature range was largest in the litter suggesting that the buffer capacity of humidity was absent in litter (upper OM) and present in humus (deeper OM). On the other hand, among the five species, Coccoloba has the largest leaves and Conocarpus the smallest ones, and canopy openness is intermediate in Tabebuia, Ficus and Pisonia [49] suggesting that tree characteristics play a role in modulating temperature fluctuations.

3.2.2 Variations among seasons

As expected, we found that the pattern of temperature fluctuations under all species (Figure 2) was similar to the pattern of mesoclimate temperature fluctuations (Figure 1) (Kolmogorov Smirnov, p = 1.00), e.g. lower temperatures in January and February in comparison to the remaining months. Also, understory air temperature under the five species was consistently higher than temperature from the mesoclimate site by 2.13°C on average. This is due to differences in altitude, the mesoclimate SCAN site was located 165 masl while trees were located at 0 masl, and since temperature decreases by ∼1°C for every 100 m in altitude, therefore this resulted in the consistent higher temperatures under the trees. Across months, understory, litter and humus temperature fluctuations were similar for all species, and the largest temperature increase occurred from February to March, an increase of 2.98°C (Figure 2). Our data is consistent with the historical pattern described for this forest [36] and with other studies that established that different vegetation associations within a geographic area follow similar fluctuations [50] because fluctuations at the regional scale drive the seasonal pattern at smaller scales, i.e. tree understory temperature.

In all species, through time, daily range significantly varied in all fractions and was larger from December 2004 to March 2005 (Figure 3). In Coccoloba, Pisonia and Tabebuia daily variation was largest in the understory and progressively smaller in litter and humus, while in Conocarpus and Ficus daily variation was largest in the litter particularly in March 2005. During the first quarter of the year, Puerto Rico is affected by cold temperate fronts that decrease average minimum temperatures from December to March [51] but do not affect maxima temperatures producing larger daily variations for all species when compared to remaining months. The largest variation in daily temperature occurred in March 2005 (Figure 3), when there was no precipitation (Figure 1).

Figure 3.

Mean daily temperature range per month below the five tree species and for each of the three depths. Daily range was calculated as Tmax – Tmin/day. Symbols represent mean values and whiskers represent standard error.

Therefore, in Guanica, months of water deficit (Figure 1) coincide with cool months suggesting that mesoclimate drives the decrease in rainfall and minima temperatures while plant species modulate maxima temperatures. In addition, temperature fluctuations in the soil are buffered by soil moisture [52] and isothermal karst rocks warm OM [48] therefore water deficit [53] in combination with an increase in temperature better explain the largest daily variations found in March 2005.

3.3 Tree species characterization

On average, Pisonia trees were 2.0 m height, Coccoloba’s were 1.8 m and Ficus’ 1.8 m, and significantly taller than Tabebuia’s and Conocarpus’ that were 1.2 m and 1.1 m respectively (Figure 4). Canopies were significantly larger in Coccoloba, 155 m2, than in the other species (Figure 4) therefore Coccoloba trees were tall and large, Ficus and Pisonia’s were tall and small, and Conocarpus and Tabebuia’s were short and small. Litter weight varied among species and through time (Figure 5), there was a trend for Coccoloba to have the largest litter mass and Tabebuia to have the lowest. Litter depth tended to be higher in Pisonia particularly at the beginning of the dry season, i.e. January 2005 (Figure 5). On the other hand, humus weight and depth were similar among species and months suggesting that although new litter is produced, there is an effect of past accumulation. In Guánica, the ground surface is irregular presenting small depressions of varying sizes [33], then variations in humus may be due to changes in size of the exact sampling points and not because of changes in soil organic matter per se.

Figure 4.

Average canopy height (m) and average canopy area (m2) for the five species. Dots represent means, whiskers represent standard error, and lower-case letters indicate significant differences.

Figure 5.

Average litter and humus dry weight (g m−2) and depth (cm) for the five tree species and in each of the six sampling dates. Symbols indicate the mean and whiskers indicate standard error. Asterisks indicate significant differences among plant species within censuses.

Across months, litter fractions varied significantly (Figure 5). For example, litter depth under Coccoloba was highest in November 2004 and lowest in June 2005, while it was highest in Pisonia during the dry months possibly because this species is deciduous and drops the leaves during the dry season [54, 55]. Ficus tended to have large litter depth in the wet months; as a facultative deciduous species, it drops the leaves during the dry season [54, 55] but these leaves had a slower decomposition possibly in relation to milky sap that deters feeding organisms than did Pisonia. Tabebuia and Conocarpus consistently had small litter depth possibly due to low production of litter fall. Our results are consistent with Lugo et al. [36] who found that litter production increased during the dry season because of deciduous species and because decomposition was arrested (due to water deficit). Humus is a stable fraction of soil organic matter, and loose and old litters are the dynamic fractions, then any change in soil organic matter depth can be attributed to changes in litter.

3.4 Tree species and microclimate

In summary, we have shown that microclimate fluctuations follow the seasonal pattern of mesoclimate, that maxima and temperature range, tree height, canopy area and litter are different among plant species. Different tree species characteristics correlated significantly with temperature, and these correlations varied seasonally (Table 2). In general, understory air temperature correlated with tree characteristics in the dry season but not in the wet season, while litter and humus temperatures correlated with tree characteristics in both seasons and with litter mass only in the wet season. Canopy size correlated significantly with understory maximum temperature in February 2005 when deciduous species drop the leaves [37] suggesting that more open canopies allowed for higher maximum temperatures in the dry season. On the other hand, litter mass and depth correlated with litter temperature suggesting that the quantity of organic matter and its water holding capacity is important for buffering litter temperature during the wet season.

February 2005September 2005
  • Canopy size**

  • Tree volume***

  • Canopy size**

  • Tree volume***

  • Canopy size***

  • Tree volume***

  • Tree height*

  • Tree height***

  • Litter depth***

  • Canopy size***

  • Tree height***

  • Tree volume***

  • Litter depth***

  • Canopy size***

  • Tree volume***

  • Litter depth***

  • Litter mass***

  • Tree volume***

  • Litter mass***

  • Canopy size***

  • Tree volume***

  • Litter mass***

  • Canopy size***

  • Tree volume***

  • Canopy size***

  • Tree volume***

  • Canopy size***

  • Tree height***

  • Tree volume***

  • Litter depth***

  • Litter mass***

  • Canopy size***

  • Tree volume***

  • Litter depth***

  • Litter mass***

  • Canopy size***

  • Tree height***

  • Tree volume***

  • Litter mass***

Table 2.

Spearman rank order correlations among maximum, minimum and range in temperature and tree characteristics (height, size, volume, organic matter depth and volume).

Only significant correlations are shown, and asterisks next to the variable indicate level of significance: *: p < 0.05, **:p < 0.01, ***:p < 0.001. February and September 2005 are shown as representatives of dry and wet periods.

Canopy area significantly correlated with temperature, for example maximum temperature was highest in Conocarpus, a species with small canopies while species with similar canopy areas such as Ficus, Pisonia and Tabebuia had lower maximum temperatures, and Coccoloba had taller trees with larger canopy areas which possibly resulted in less understory heating and lower temperatures. Leaf area and leaf orientation result in light differentially entering plant canopies producing different degrees of heating of understory air [1, 52]. Therefore, our data suggest that the size of the tree (canopy and height – volume) influence the microclimate created underneath it but not in a linear additive way, rather tree characteristics interact and influence microclimate underneath the tree in complex ways, suggesting that the tree is an integrate that modulates the understory microclimate.

3.5 Arthropods

3.5.1 Litter fractions

A total of 8702 arthropods representing 22 orders and 301 morphotypes were collected. Arthropod abundance and richness were significantly different among fractions (Table 3). Overall Tabebuia and Ficus had similarly higher abundance, followed by Pisonia and Coccoloba, and abundance was lowest in Conocarpus. In general, there was a trend for lower arthropod abundance in loose litter and highest in humus except in Coccoloba where old litter had higher abundance than the other two fractions (Figure 6). Overall, richness was highest in Ficus and Pisonia, followed by Tabebuia, and lowest in Coccoloba and Conocarpus, but among fractions the trend for lower richness in loose litter and higher in humus was similar as for abundance (Figure 6). These data suggest that for arthropod abundance and richness, the decomposition stage of the organic matter (paralleled in the vertical profile: loose, old, humus) is a determining factor as important as plant species identity [24]. Furthermore, the decomposition stage is advanced in humus where the biological activity is high and thus there are high available resources (e.g. higher nutrient availability in humus than in litter fractions, [11]) suggesting a higher abundance of decomposer microbes that, in the trophic food web theory would result in higher abundance of arthropods.

Date x species160.900.571.520.09
Date x fraction82.920.007.23<0.001
Species x fraction82.590.011.930.05
Date x species x fraction320.790.790.930.58

Table 3.

Three-way analysis of variance for the effect of date, species and fraction on the abundance and richness of arthropods.

Figure 6.

Average arthropod abundance (±s.d.) (ind/m2) and average number of morphotypes (±s.e.) in each fraction for the five study species. Lower case letters indicate significant differences among fractions within species.

Hansen [12] found that large litter dwellers were positively associated with particular litter substrates, Berg et al. [22] similarly found that litter in different decomposition stages had sets of associated arthropods, and Prinzing et al. [56] found that even sexes within a mite species differentially used organic matter strata. At the same time, Hansen [12] proposed that a higher abundance and richness of arthropods would be found in strata with higher diversity of resources (i.e. litter). Our results support that specific groups are associated to specific litter strata, being more abundant where more resources are found, but not in the litter, instead in the humus fraction. Kardol et al. [57] suggest that plant–soil feedbacks are mediated by microbes; specifically, they found that soil microbial pathogens affected the development of the future plant community because soils have a biotic legacy of past vegetation cover. Similarly, soils may have a biotic legacy of beneficial microbes that, as Wardle states, maximizes decomposition, and can have a positive historical feedback on the soil community. In addition, Bezemer et al. [58] found that plant–soil feedbacks are dependent on plant species. In our case, these data suggest that trees in the coastal plateau have been there for long enough to result in a biotic legacy that, under certain plant species favors some arthropods, while under other plant species other types of arthropods are favored.

3.5.2 Variations through time

Arthropod abundance and richness were significantly different among sampling dates (Table 3). Through time, arthropod abundance varied with a trend for a decreased abundance in the dry period (April 2005) and higher abundance in the wet period (Figure 7). In all species and all samplings, arthropod abundance was consistently low in loose litter while variations in old litter and humus explained significant differences. For example, in Coccoloba, Ficus and Tabebuia abundance peaked at the onset of the wet period (June 2005) while in Conocarpus and Pisonia peaked abundance was observed later in the wet period (September 2005). Similarly, for all plant species, arthropod richness was significantly low in the dry period and higher in the wet period (Figure 8). Also, in Coccoloba, Ficus and Tabebuia peaked richness occurred at the onset of the wet period (June 2005) while in Conocarpus and Pisonia it was later in the wet season (September 2005).

Figure 7.

Mean arthropod abundance (±s.d.) (ind/m2) in each sampling date and in each fraction for the five study species. Asterisks indicate significant differences among fractions within species for the corresponding sampling date.

Figure 8.

Average number of morphotypes in each sampling date and in each fraction for the five study species. Asterisks indicate significant differences among fractions within species for the corresponding sampling date.

During the study, precipitation varied producing a wet 2004 period, a dry 2005 period and a wet 2005 period (Figure 1). Herrera [59] found that the abundance of soil fauna fluctuated seasonally influenced by the precipitation regime. Specifically, groups like ants and beetles were present during year-round but isopods and diplopods were more abundant at the end of the wet season while chilopods were present all the year but during the dry season they migrated vertically and thus were completely absent in the upper litter. On the other hand, Prather et al. [27] found that high soil moisture promoted arthropods while high temperature decreased arthropods. Our results show that arthropod abundance and richness follow a seasonal pattern tight to precipitation, and that arthropods tightly linked to litter are able to overcome drought although in such low abundance that does not support upper trophic levels [24].

Arthropod species composition was dynamic through time as it varied among plant species (Figure 9). Under wet conditions such as in November 2004, arthropod species composition was similar between Coccoloba and Conocarpus and among Tabebuia, Ficus and Pisonia who shared unique sets of associated arthropods. Similarly, in June and September 2005, a similar pattern was observed such that species composition was similar between Coccoloba and Conocarpus, and among Ficus, Pisonia and Tabebuia. On the other hand, at the onset of the dry period (February 2005) when water shortage peaked, differences among plant species were lost resulting in similar arthropod species composition among species (Figure 9). Therefore, in general, when water is available, arthropods formed two distinctive communities under different groups of plant species, while when water deficit happened, plant species shared a common set of arthropods.

Figure 9.

p-value results from MRPP (Multi-response permutation procedure) tests. MRPP evaluated whether there were significant differences in arthropod species composition among plant species in each sampling. The y-axis shows p-values, initials indicate plant species as follows Coc for Coccoloba, Con for Conocarpus, F for Ficus, P for Pisonia and T for Tabebuia. The short-dashed horizontal line indicates the critical value p = 0.05. Markers below the dashed line show significant differences among species. Significant differences are shown by circled color-coded initials.

From these data, we conclude that two major arthropod communities were formed, one with arthropods common to Coccoloba and Conocarpus, and one with arthropods common to Ficus, Pisonia and Tabebuia. The local distribution of trees shows that Coccoloba and Conocarpus are closer to the shore than the other three species, suggesting that plants near the coast share arthropods communities as do plants farther from the shore. Nevertheless, when extreme conditions happen, such as dry cool months in February 2005 (Figure 1), the arthropod community homogenizes because arthropod abundance was low and species composition becomes similar.

Both, arthropod abundance and richness (Figures 7 and 8) decreased at the onset of the dry period (February 2005) and were at a minimum in the middle of the period (April 2005), on the other hand species composition was similar at the onset but different in the middle of the dry season (Figure 9). Furthermore, litter depth was high at the onset of the dry season (Figure 5) because of new litter produced by plants as a response of water deficit (Figure 1). These results suggest that during the dry season, conditions are such that plants drop leaves and belowground arthropods associated to plants represent a shared subgroup common to all plant species, while further into the dry season, arthropod abundance and richness are at the lowest such that only arthropods specific to plant species are present. Abundance and richness are not linearly related, nevertheless an increase in abundance results in more species, for example Prather et al. [27] found that for every 16 individuals a new species appears. From these data we can expect that a decrease in abundance results in loss of species, therefore, in our study the decrease in abundance at the beginning of the dry season may have resulted in a decrease in species such that only stress-tolerant arthropod species [27] were present in all plant species producing a homogenization of the arthropod community. A further decrease in abundance led to the minimum found in mid-dry season which might have produced further species loss so that only species adapted to the specific plant microhabitat remained resulting in a differentiation in arthropod community.

Within each plant species, arthropod species composition significantly varied through time (Table 4). In Coccoloba and Conocarpus, species composition significantly changed between consecutive samplings except April to June 2005. These data suggest that for these two plants there is a high turnover of arthropod species, and that the arthropods present in April were a subset of the June arthropods. When comparing April and June 2005, in Coccoloba, abundance and species composition were similar and richness increased (Table 4, Figures 7 and 8) suggesting that at the onset of the wet season the majority of arthropods belong to the same species despite an increase in richness. In Conocarpus, when comparing April and June, arthropod abundance (Figure 8), richness (Figure 9) and species composition (Table 4) were similar suggesting that the transition between dry and wet months is dominated by a common set of arthropods.

SpeciesNov 04 – Feb 05Feb – Apr 05Apr – June 05June – Sept 05

Table 4.

Dissimilarity (%) of arthropod species composition (mean ± s.d.) associated to specific plant species among sampling dates.

Percent dissimilarity was calculated as 100 * dissimilarity. Dissimilarity was calculated using the Sorensen index (1-2 W/(A + B)). W indicates the sum of abundances of common species between sampling units, and A and B indicate the sum of abundances of all species in each sampling unit. Asterisk indicates significant differences among consecutive sampling dates (*:p < 0.05, **:p < 0.001, ***:p < 0.0001).

In Ficus, the arthropod composition was similar through time except from November to February and from April to June (Table 4), suggesting that the major shifts in arthropod species occur between seasons (end wet period: November to February, end dry period: April to June). On the other hand, arthropod abundance and richness decreased to a minimum in the middle of the dry season (April 2005) (Figures 8 and 9) when litter increased (Figure 5) because this is a deciduous plant. These data suggest that arthropods present in Ficus respond to changing conditions, such as new litter or a change in precipitation.

In Pisonia, a similar pattern as in Ficus was observed but later in the wet period (June to September), arthropod abundance and richness increased in the wet period (Figures 6 and 7), and species composition was significantly different (Table 4). In addition, Pisonia is an obligate deciduous species that produces new litter in the dry season (Figure 5). These data suggest a possible time lag tied to increasing precipitation as arthropods present in Pisonia increased and litter depth decreased (Figure 5) probably explained by decomposition of new leaves and more arthropods at the onset of the wet season.

In Tabebuia, arthropod species composition and abundance were similar through time (Figure 7 and Table 4) while richness (Figure 8) significantly varied suggesting that there is a bulk of arthropods associated to this plant species that is present yearlong although some arthropod species vary through time but not enough to produce a different composition of arthropods.

For most plant species, these data show a shared pattern for a shift in arthropod communities between wet and dry seasons (November 2004 to February 2005), then arthropods in Cocoloba and Conocarpus continued to change as the dry season strengthened while arthropods in Ficus and Pisonia (deciduous plants with more open canopies by February 2005) changed with an increase in rainfall that triggers biological activity. Tabebuia was the only plant to show a consistent group of associated arthropods.

3.5.3 Variations across fractions

Abundance and richness of arthropods were significantly affected by sampling time and fraction (interaction term Date x Fraction in Table 3), suggesting that through time there is a dynamic movement of arthropods among fractions (Figures 7 and 8). In addition, for each plant species the composition of arthropods varied across the vertical organic matter profile (Figure 10).

Figure 10.

p-value results from MRPP tests. MRPP evaluated differences among fractions in arthropod species composition, for each plant species at each sampling separately. The y-axis shows p-values, initials indicate fractions as follows L for loose litter, O for old litter and H for humus. The short-dashed horizontal line indicates the critical value p = 0.05. The long-dashed line connects consecutive samplings. Markers below the dashed line show significant differences among fractions. Symbols indicate differences, ´X´ indicates significant differences between the specified fractions, ´ ∼ ´ indicates no significant difference, and ´*´ that two fractions are similar but different from the third one.

In Coccoloba, there was a general trend for high turnover of arthropods which were more abundant and species composition was different among fractions in the wet season and similar in the dry season when abundance was lowest (Figures 810). In wet months, loose litter had 9–12 and old litter had 27–30 morphotypes and shared 1–4, while humus had 14–25 from which 12–16 occurred in old. Loose and humus fractions had 1 to none arthropods in common (Appendix I). These data show a unique arthropod community in loose litter mainly due to adult Diptera (unique to this fraction) that possibly pupated and emerged as adults at the moment of collection (e.g. in November: G-157, G-158, G-159, and in September: G-182, G-123, G-191, G-209) (Appendix I). On the other hand, humus species composition was unique in June due to a dominance of detritivore and predator species exclusive to this fraction, such as G-207, G-007, G-147, G-229 and G-263 (Appendix I). These data suggest that the beginning of the wet season triggers the biological activity in the humus fraction (as suggested by increased arthropod abundance and richness, and unique species composition), and that later in the wet season (September and November samplings) decomposer activity increases (as suggested by the presence of adult Diptera whose larvae feed on decomposing material, e.g. G-157, G-158 and G-159), and that a possible flush in aboveground biomass promotes herbivore activity that pupates in the ground (as suggested by the presence of adult Hymenoptera whose larvae are herbivores, e.g. G-168). The similarity in species composition among fractions during the dry period samplings (February and April) suggests that an increase in daily temperature (Figures 2 and 3) and drought represent stressors that mix the vertical stratification of the arthropod fauna. For example, in April loose litter had no arthropods, suggesting that remaining arthropods migrated downwards to avoid these extreme conditions.

In Conocarpus, also there was a general trend for high turnover of arthropods through time, but there was a consistent pattern for loose litter to have a low abundance arthropod community that is different from old litter and humus (Figures 7 and 8). In addition, when abundance was at a minimum in April 2005 (Figures 7 and 8), the species composition was similar (Figure 10). In November, February and September samplings, loose litter had unique species that were adult Diptera that possibly emerged at the moment of collection, and predators such as Araneae and Opiliones (i.e. G-134 and G-101) (Appendix I). In April, abundance and richness were lowest especially in loose litter, and species composition was similar among fractions suggesting again that arthropods migrated downwards to avoid extreme conditions such as drought and heat. Although the wet period began in May (Figure 1), abundance and richness continued to be low in loose litter and species composition was similar among fractions. In June, loose litter had 3 morphotypes with very low abundance, two were detritivore mites (G-147 and G-100 - Appendix I) suggesting that the increase in precipitation may have promoted detritivore abundance but still not enough to propagate into the loose litter fraction, possibly because Conocarpus is a species that grows very near the shore cliff and may receive more salt spray and less effective rainfall so that it may take longer to respond to an increase in water supply.

In Ficus, in general, arthropod species composition was different through time and across fractions while abundance was higher in wet months and also in dry months when abundance decreased (Figures 7, 8 and 10). In November and June, humus had a specific set of associated arthropods that were mainly detritivores and predators (i.e. G-007, G-092, G-207, and G-244 - Appendix I). In February and April, species composition was marginally different, possibly due to the presence of adult Diptera in loose and old litter, and large detritivores (such as Psocoptera and Blattodea G-102 and G-228 - Appendix I) in old litter, while in humus there were basically small detritivores and predators (G-207, G-003 and G-220). A possible explanation for the presence of adult Diptera in old litter is that Ficus is a facultative deciduous species that progressively drops the leaves in dry conditions so that dipteran larvae that began pupating were then covered by new litter. In September, species composition mixed among fractions possibly due to the shared presence of highly abundant morphotypes such as G-078, G-037, G-105 and G-031 (Appendix I), mainly detritivores, suggesting that in mid wet season, there has been enough detritus production so that detritivores are promoted in the whole organic matter vertical profile resulting in a similar species composition among fractions.

In Pisonia also the general trend was for a different species composition when abundance is high in the wet season and it becomes similar when abundance decreases in the dry season (Figures 7, 8 and 10). In November, February and September, abundance and richness were comparatively high, and species composition was different among fractions. In loose litter, the majority of unique morphotypes were adult dipterans (e.g. G-157 and G-139) and adult coleopterans that as larvae are herbivores (e.g. G-146 and G-148) while old litter and humus shared basically abundant detritivore mites (G-037, G-007 and G-087) (Appendix I). In April and June, there was a similar composition of arthropod across fractions, possibly due to the extremely low abundance of arthropods. This may be the result of downwards migration of arthropods and a pulse of new litter because Pisonia is an obligate. These data suggest that extreme conditions that occur during the dry season mixed the stratification of arthropods in the vertical organic matter profile.

Tabebuia had a similar arthropod community across fractions through time although abundance was similar and richness increased in the wet season (Figures 7, 8 and 10). The similarity in species composition was possibly due to the shared presence of abundant morphotypes among fractions at each sampling date (e.g. in November G-037, G-003, G-002, G-001, in February G-078, G-188, in April G-037, G-078, in June G-078, G-002, G-004, G-105) (Appendix I). In September, there continued to be abundant shared morphotypes but the appearance of unique detritivore species in humus differentiate the species composition (e.g. as G-262, G-288, G-010, G-269). These data suggest that the arthropod fauna below Tabebuia actively moves among fractions and that, at mid wet season, enough detritus has been produced in order to differentiate active arthropods in each fraction.

4. Conclusions

We found abundance and richness to be highest in humus and species composition to be different among fractions suggesting that arthropod communities are segregated among plant species and are further stratified by decomposition stage. Also, abundance and richness were lowest in the dry months when arthropod communities homogenize while in the wet season there was a pattern for arthropods to form two distinctive groups, one formed by arthropods common to Coccoloba and Conocarpus, and a second group formed by arthropods common to Ficus, Pisonia and Tabebuia. Furthermore, under plant species, the seasonal distribution of arthropods among fractions in different decomposition stages was differentially affected by variations in microclimate and organic matter depth.

We found for 4 out of 5 plant species, that arthropod communities shift at the end of the wet season when water becomes scarce, and again at the onset of next wet season when temperature daily range was largest. In addition, we identified three patterns of arthropod dynamics across fractions.

First Cocoloba and Conocarpus, two species close to the shore cliff, showed stratification of arthropod communities across fractions except in dry months when arthropods migrate downwards to deep OM. Then at the beginning of the wet season, arthropods are a subgroup of those in the dry season, and as wet seasons continues, abundance increases and arthropods stratify across fractions.

Second Ficus and Pisonia both showed a seasonal increase in litter and distinct arthropod communities in wet and dry seasons. During the wet season arthropods are abundant and effectively stratify along OM fractions while in the dry season, in general, communities continue to be different across fractions despite a decrease in abundance, and at the onset of the wet season arthropods are a subgroup of those in the dry season. Nevertheless, Conocarpus and Pisonia present a time lag as compared to Coccoloba, in Conocarpus possibly related to less effective rain (proximity to the coastal cliff) and in Pisonia possibly related to the time that new litter takes to decompose enough to promote arthropods (as an obligate deciduous species).

Third in Tabebuia, litter depth was low all yearlong and temperature range was smaller, and we found arthropod species composition to be similar and abundance to vary through time, being always lowest in loose litter, also arthropod composition was similar across fractions suggesting that arthropods actively move among fractions.


This research was partially funded by CREST-Center for Applied Tropical Ecology and Conservation of the University of Puerto Rico at Rio Piedras Campus, grant NSF-HRD-0206200 through a fellowship to MFBA. Additional funding and logistic support was provided by the CREST-Center for Applied Tropical Ecology and Conservation of the University of Puerto Rico at Rio Piedras Campus, USDA Forest Service-International Institute of Tropical Forestry and the Guanica Dry Forest, a Biosphere Reserve staff.

Appendix I: Excerpt from appendix in Barberena-Arias (2008). Abundance (ind/m2) of adult morphotypes in each fraction and species, and for each of the samplings

Nov. 2004Pseudosc.G-001700200100100300200200300200200200
Nov. 2004AcariG-00210010001004009001300
Nov. 2004AcariG-0039004004004001001100600100900600100700500
Nov. 2004AcariG-00470020010050010060010001001004001000
Nov. 2004AcariG-00725003001001100500700400
Nov. 2004IsopodaG-01010010028003001001001100
Nov. 2004AcariG-0311000
Nov. 2004AcariG-03710071001900100180023001004001800150013001002001800
Nov. 2004AcariG-078600700200200300380016001004500
Nov. 2004IsopodaG-087100400300100100
Nov. 2004ColeopteraG-092200300
Nov. 2004OpilionesG-101100100100100100100
Nov. 2004PsocopteraG-102300400100
Nov. 2004DipteraG-123100100200100
Nov. 2004AraneaeG-134200500
Nov. 2004AcariG-100100
Nov. 2004DipteraG-139100200100100100
Nov. 2004AcariG-147100
Nov. 2004DipteraG-157100100100
Nov. 2004DipteraG-182100100
Nov. 2004ColeopteraG-146100
Nov. 2004ColeopteraG-148100
Nov. 2004DipteraG-158100
Nov. 2004DipteraG-159100
Feb. 2005AcariG-002500200600100100300600900
Feb. 2005AcariG-003230040018002001006004001300
Feb. 2005AcariG-00410010010001003009001100200300
Feb. 2005AcariG-0071600600200600100500200
Feb. 2005AcariG-037100100100100300100200500500
Feb. 2005AcariG-07810017003001001004001001002900
Feb. 2005PsocopteraG-102100400100
Feb. 2005AraneaeG-134200500400300200
Nov. 2004Hymenopt.G-168100
Feb. 2005AcariG-188100100200100100100100
Feb. 2005AcariG-20710046004600
Feb. 2005IsopodaG-010200100
Feb. 2005AcariG-100100
Feb. 2005DipteraG-139100100100
Feb. 2005DipteraG-159100200
Feb. 2005DipteraG-191100100
Feb. 2005Pseudosc.G-220200100100
Feb. 2005AraneaeG-229100100
Feb. 2005Pseudosc.G-001100
Feb. 2005ColeopteraG-092100
Feb. 2005OpilionesG-101100
Feb. 2005DipteraG-209100
Feb. 2005BlattodeaG-228
Apr. 2005Pseudosc.G-001200100100
Apr. 2005AcariG-0022001002001001001700
Apr. 2005AcariG-0033001000700
Apr. 2005AcariG-00411003001001001001006005009600
Apr. 2005AcariG-007200100200
Apr. 2005AcariG-037340070014007001001001000200300300
Apr. 2005AcariG-0788004001005001003003001001200800
Apr. 2005AcariG-100100100
Apr. 2005AraneaeG-134100200200
Apr. 2005DipteraG-139100100
Apr. 2005AcariG-2071000100200100
Apr. 2005DipteraG-123100
Apr. 2005DipteraG-157100
Apr. 2005AcariG-188100
Apr. 2005Pseudosc.G-220100
Apr. 2005BlattodeaG-228100
Apr. 2005AcariG-244100
Jun. 2005AcariG-002500400200200200400150010070040020012001800
Jun. 2005AcariG-00310001001001002200100400100
Jun. 2005AcariG-0043001800400300300500100600600300100900
Jun. 2005AcariG-00715001001900100400500
Jun. 2005AcariG-0313001300100100
Jun. 2005AcariG-037490041001100220010034003005005003400
Jun. 2005AcariG-07850048005001700300570010020010001003005600
Jun. 2005AcariG-105300100200300500200100100400
Jun. 2005AraneaeG-134100100100100100
Jun. 2005AcariG-1472001003002400300600200300
Jun. 2005AcariG-20734001009003001002001002900
Jun. 2005AraneaeG-229200100100100300
Jun. 2005AcariG-2441001200100
Jun. 2005AcariG-2621400100
Jun. 2005Pseudosc.G-263100100100100100100
Jun. 2005AcariG-269300
Jun. 2005Pseudosc.G-001100
Jun. 2005AcariG-100100
Jun. 2005AcariG-188100
Sep. 2005AcariG-00280080030010020020020010030011001500
Sep. 2005AcariG-0034007001005001400300400300
Sep. 2005AcariG-0074006006005002001006008002600300300
Sep. 2005AcariG-03190040010060011002001200100050011001000300700
Sep. 2005AcariG-0374400200010021001600300600130010001600200900800
Sep. 2005AcariG-078110031004001300100400390010060022002008003900
Sep. 2005AcariG-1051001300200700140070050080017001007005500300
Sep. 2005AcariG-1471002003001003007004001300100400600
Sep. 2005AcariG-1882002004001003001001200700300300
Sep. 2005AcariG-2075004002004001003002001001005008009002100
Sep. 2005AcariG-24410010010013001002005003002800500900
Sep. 2005Pseudosc.G-001100100200
Sep. 2005AcariG-004200600100200100700
Sep. 2005IsopodaG-087100200600300
Sep. 2005AraneaeG-134100100100100
Sep. 2005AraneaeG-229100200100100100
Sep. 2005AcariG-2621100
Sep. 2005Pseudosc.G-263300100100100
Sep. 2005AcariG-288200200100
Sep. 2005IsopodaG-010
Sep. 2005PsocopteraG-102100100
Sep. 2005DipteraG-123100100
Sep. 2005DipteraG-182100100100
Sep. 2005Pseudosc.G-220100
Sep. 2005BlattodeaG-228100
Sep. 2005AcariG-269100100
Sep. 2005DipteraG-157100
Sep. 2005DipteraG-191100
Sep. 2005DipteraG-209100

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María F. Barberena-Arias and Elvira Cuevas (December 29th 2020). Vertical Arthropod Dynamics across Organic Matter Fractions in Relation to Microclimate and Plant Phenology [Online First], IntechOpen, DOI: 10.5772/intechopen.94747. Available from:

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