**1. Introduction**

Plant diseases are one of the main problems facing agricultural production worldwide, as they cause great losses to basic food crops. They may also lead to a decline in agricultural production. It is one of the plants belonging to the family Cucurbitaceae, and it is one of the summer vegetable crops grown in Iraq and that it is available throughout the year through the use of the open and covered agricultural pattern, according to the conditions that suit production [1].

5.Evaluation of the biological efficacy of medicinal plant extracts (licorice,

*The Activity of New Bio-Agent to Control* Cucumovirus Cucumber Mosaic Virus *(CMV)*

6.The effect of the complementary treatment with the treatments mentioned above on the seeds on the percentage of their transmission and germination

7.Determining the rate of transmission of the virus in seeds from different

The pathological symptoms of *Cucumovirus Cucumber Mosaic Virus* are described, which are characterized by the yellowing and mottling that spreads on cucumber leaves and also on the fruits (**Figure 1**), which is a yellow mottling that affects all leaves and after infection the leaves are deformed in addition to stunting, which is accompanied by gradual plant degradation and curling of the leaves, as well as surface roughness. The outer layer of the leaf and the appearance of small prominent growths, these growths are clearly between the small veins [11]. Mosaic cucumber is from the Cucumovirus group that works on the appearance of mosaics and mottles on their families. Gibbs and Harrison [12] stated that this virus causes green mosaic or alternating green and yellow colors with each other on cucumbers. The virus causes symptoms of bundling veins and deformation of the leaves, in addition to the appearance of mosaic and the curvature of modern leaves, and the necks of the leaves are reduced and accompanied by deformation of the fruits on

sespan, and albizia) in inhibiting CMV infection.

sources (local and international).

*DOI: http://dx.doi.org/10.5772/intechopen.96587*

strength.

**2. Review of literatures**

**2.1 Cucumber mosaic virus**

*2.1.1 Symptoms*

**FIgure 1.**

**147**

*CMV pathological symptoms.*

CMV virus is worldwide, especially in temperate regions [2], where it can infect more than 800 plant species [3] belonging to about 40 families [2]. Although the main factor that the plant takes in order not to be infected is because it has preventive means that inhibit the direction of pathogens so that the infection occurs under conditions that suit it and suit its success.

CMV belongs to the group of plant viruses to the genus Cucumovirus, as the virus particles are symmetrically spherical, not enveloped, with a diameter of 29 nm, and the virus has several strains that differ among themselves in terms of factors, symptoms of infection and methods of transmission [4].

The stimulation of induced systemic resistance (ISR) leads to the interest of many researchers. Many researches and studies have been conducted in the field of biochemical changes in the form of modulating the host's cell wall [5]. The production of phytoalexin [6]. And the manufacture of pathogen-related proteins (Pathogenesis Related Protein) [7]. It has been indicated that treatment with various factors, for example (non-pathogenic organisms, weak pathogens, chemical and industrial compounds, plant extracts, nutritional supplements) has the ability to activate plant defense mechanisms and induce systemic resistance against pathogens [8].

In the field of biological control, bacterial types have been used on many pathogens, including *Pseudomonas fluorescens* and *Bacillus subtillus*, as they have proven effective in controlling many different pathogens, and the reason is due to the ability of the bacteria to produce many growth regulators and thus stimulate resistance in plant and the production of phytotoxins are in addition to being one of the most important growth stimuli.

New methods have been used to resist viruses by using natural nutritional supplements with effective effect, because plants have defensive means, and for this reason, the use of these supplements can be stimulated in addition to the preventive aspect, a decrease in infection parameters, and an increase in growth indicators and outcome [9].

Several methods have been relied upon to diagnose viruses, the first being the symptoms of reagents, and they are of basic methods [10].

In this chapter of the book we will discuss the following topics:

*The Activity of New Bio-Agent to Control* Cucumovirus Cucumber Mosaic Virus *(CMV) DOI: http://dx.doi.org/10.5772/intechopen.96587*


**3. Spatial variation of soil salinity in Sri Lanka**

Soil salinity varies across the country and the highest salinity levels are reported in the north-central plains and along the coastal belt (**Figure 1**, **Table 1**) [15, 41–53]. Primary salinity due to natural weathering of salt-containing bedrock is restricted to isolated patches in the island. However, secondary salinity is widespread and found in inland and coastal rice farming systems. There are three main processes associated with secondary salinity in local rice farming systems *viz,* irrigation salinity, dryland salinity, and coastal salinity. Irrigation salinity arises in irrigated areas as a result of rising groundwater tables from excessive irrigation or due to the use of poor-quality water. In dryland non-irrigated landscapes, dryland salinity occurs due to the rise of the water table that drives soluble ions deposited by primary salinity to the soil surface. Coastal salinity is a result of the accumulation of salts in soil and water due to seawater intrusion from surface flow or seepage of seawater through sea level rise and tidal activity. The incidence of tides and dikes is the main reason for the intrusion of seawater into inland areas. Primary salinity, found in a few isolated sites such as the Nawagathegama soils series in the northwest part of the country [54], has a minimal economic impact on local agriculture; however, secondary salinity resulting from human activities causes significant economic losses in the local rice farming systems.

The inland salinity is a combination of both irrigation salinity and dryland salinity and has a mosaic distribution island-wide [53]. The main processes that contribute to irrigation salinity are the rise of groundwater tables resulting from poor drainage, excess irrigation, and perennial irrigation systems, which result in shallow water tables that bring salts to the upper layers of the soil profile. The problem is aggravated due to blocked drainage canals that prevent draining of salts [55]. A case study, in the largest irrigation scheme in the country the "Mahaweli," observed that soil salinity is a principal determinant of rice production in affected irrigated areas whereby yield loss of 10–15% is recorded in moderately saline areas; however, the yield was reduced by third in high saline areas [15]. In the segment "Mahaweli H" > 10% of the irrigable area is affected due to high salinity and an additional 40% is affected by moderate salinity providing a relative measure of the scale of the problem [15]. When salinity is >5dSm1 , rice yield is reduced by more than 20% in affected compared to unaffected cultivations [56]. Inland salinity, therefore, has a significant impact on mega-rice cultivation environments, causing reduced production and direct economic losses.

Multiple studies have shown high concentrations of inorganic ions and EC >2.5 dSm1 in irrigated water in the dry zone, significantly exceeding the recommended EC range in irrigation water, which is 0.7–0.75 dSm1 [18]. Despite the high EC values, natural Na+ concentrations in inland soils, including soils in the megacultivation environments, and except in a few isolated pockets, were low [55]. A significant portion of the northern half of the country recorded soil Na+ concentration > 200 mg/kg, which is below the toxic limits [57]. However, high CO3 2 , HCO3 , Ca+2, and Mg+2 concentrations in the dry zone soils increase EC [58, 59]. Anuradhapura, Puttalam, Polonnaruwa, and Kurunegala districts record soil Mg2+ and Ca2+ concentrations as high as 730 ppm (optimum level for rice production, 120 ppm, [60] and 509–3452 ppm [61], respectively). Ca2+ saturation of cation exchange capacity (CEC) of soils in the dry zone is Ca 52.5% of CEC (5 cmolc/kg), whereas the recommended Ca CEC is <20% [62]. Further, high HCO3 2 and SO4 2 are reported in water in irrigation wells in north-central areas, which can contribute to high soil EC values. In such conditions, although not subjected to Na+ toxicity, the crops are subjected to osmotic stress owing to the high ion concentration in the root zone.

*The Scale and Complexity of Salinity Impacts on Sri Lankan Rice Farming Systems:… DOI: http://dx.doi.org/10.5772/intechopen.112651*

#### **Table 1.**

*Spatial and temporal variation in salinity in Sri Lanka.*

Coastal salinity has a significant impact on the rain-fed farming systems in the southwest coastal belt [48, 63] and in the Jaffna peninsula [41–43]. Due to the low coastal slope rice farming systems in the southwest coast are frequently affected by inundation and irrigation with salty water [48, 52, 64]. An area of 0.112 million ha is affected due to coastal salinity in Sri Lanka [30], where the EC of the soil extraction can exceed 4 dSm1 [65]. In affected sites, tidal waves can range between 45 cm and 60 cm during spring tide and between 10 cm and 25 cm during neap. In dry conditions, the reduction of water levels in rivers causes backflow of seawater along rivers and intrudes the rice fields with salt water, thereby saltwater intrudes into lands up to 50 cm above mean sea level [32] in dry weather. Further, blockage of drainage canals can also cause salinization in rice fields in the coastal line. The EC range in the southwest coast varied from 0.1 to 0.4 dsm1 based on soil: water 1:5 extracts. Unlike in inland salinity, soils affected due to coastal salinity contain high concentrations of Na+ and Cl ions [66, 67]. In the southwest coastal belt (Kaluthara to Matara), Na+ levels vary from 50 to 100 ppm; however, in Jaffna and on the eastern coastline the Na+ levels are higher than 200 ppm [53]. Detailed observations were made in the Bentota river basin, where 70% of the land is affected due to coastal salinity, ranging from 8 dSm1 to 16 dSm1 along with >10% of land affected by high salinity (>16 dSm1 ) [45, 46, 53]. Agricultural production loss per year due to salinity in the area is over 3.6 million USD [45]. Rice lands are gradually abandoned in the Jaffna peninsula owing to the high levels of seawater intrusion causing salinization of soils and water in wells used for irrigation. In addition, the major irrigated farming systems in Mannar, Hambantota, and Trincomalee coastal lines are also affected due to coastal salinity, where EC values can vary between 3 to 30 dSm1 [42, 47, 48, 68]. Coastal salinity has a significant impact on semi-subsistence rice farming systems in the southwest, northwest, and eastern coastal line limiting food availability and livelihoods for the local farmers, and thereby destabilizing local food systems and increase poverty.


*3.1.1 Multi-disciplinary approach*

Taking the multiple functional issues into consideration, multi-disciplinary approach has become the trend in syndromic craniosynostosis management. A craniofacial centre or unit may consist of various specialties such as neurosurgery, oral and maxillofacial surgery, plastic surgery, otorhinolaryngology, ophthalmology, and oculoplastic, among others.

## *3.1.2 General assessment*

Patient growth progress and development should be assessed and properly documented as it provides valuable baseline and comparative data before and after surgery. This includes objective data such as head circumference, height, weight, gross and fine motor, as well as speech development, among others. These parameters are important as it may determine whether any corrective surgery should be indicated as early as possible or performed at a later stage [21].

**133**

**Figure 1.**

**Figure 2.**

*3.1.5 Ophthalmological assessment*

*Thinning of the skull bone noted from the reconstructed 3D CT scan.*

*Surgical simulation on the monobloc advancement of the frontofacial segment.*

*Craniofacial Corrective Surgery in Syndromic Craniosynostosis*

Specifically on the craniofacial region, assessment can be focused on patient's initial head shape, the degree of exopthalmos, ability for eyelid closure, nasal airflow, midfacial projection, jaw relationship and intraoral condition. Clinical picture

Imaging modalities provide valuable input in identifying a specific functional issue. Magnetic resonance imaging (MRI) or CT scan may indicate any anomaly in the brain region and the condition of skull bone, respectively. Thinning of bone or copper beaten appearance is an indication to raised intracranial pressure necessitating corrective surgery to improve the intracranial volume via procedures such as posterior vault

Specific ophthalmological assessment such as retinal camera or fundoscopy provides information of the interior surface of the eye, including the retina, vasculature, optic disc and macula. Pale disc may be an indication of increased intracranial

documentation is very useful as it can be used for serial comparison.

expansion or fronto-orbital advancement as shown in **Figures 1** and **2**.

*DOI: http://dx.doi.org/10.5772/intechopen.94584*

*3.1.3 Clinical assessment*

*3.1.4 Imaging assessment*

### *Craniofacial Corrective Surgery in Syndromic Craniosynostosis DOI: http://dx.doi.org/10.5772/intechopen.94584*


*Article* **A Midsummer Night's Diet: Snapshot on Trophic Strategy of the Alpine Salamander,** *Salamandra atra*

**Luca Roner 1, Andrea Costa 2, Paolo Pedrini 1, Giorgio Matteucci 3,4, Stefano Leonardi 5 and Antonio Romano 1,3,\***

Received: 30 March 2020; Accepted: 15 May 2020; Published: 17 May 2020

**Abstract:** Information on the trophic ecology of the Alpine salamander, *Salamandra atra,* is scattered and anecdotal. We studied for the first time the trophic niche and prey availability of a population from an area located in Italian Dolomites during the first half of August. Considering that *S. atra* is a typical nocturnal species, we collected food availability separately for diurnal and nocturnal hours. Our aims were: (i) to obtain information on the realized trophic niche; (ii) to provide a direct comparison between trophic strategy considering only nocturnal preys or considering all preys; (iii) to study trophic strategy of this species at the individual level. In two samplings nights we obtained prey from 50 individuals using stomach flushing technique. Trophic strategy was determined using the graphical Costello method and selectivity using the relativized electivity index. During the short timeframe of our sample, this salamander showed a generalized trophic strategy. The total trophic availability differed significantly from nocturnal availability. Interindividual diet variation is discussed in the light of the optimal diet theory. Finally, we highlighted that considering or not the activity time of the studied taxon and its preys may lead to a conflicting interpretation of the trophic strategies.

**Keywords:** amphibians; feeding ecology; individual specialization; resource selection; salamanders

#### **1. Introduction**

The ecological role of salamanders is often overlooked, despite the fact they can act as top predators in certain trophic webs [1]. They may represent a significant part of vertebrate biomass in North American ecosystems [2], and can reach high densities in North American [3] and European forests as well [4,5]. Salamanders mainly prey on invertebrates and play a key role in nutrient cycling [6]. Furthermore, those species characterized by a biphasic life cycle are also an important energy exchange vector among different habitat types [1]. For these reasons, dietary studies on salamanders are an indispensable tool for assessing their ecological role, and for planning future conservation measures [7]. At population level, the effect of the diet, together with other ecological factors and variables such as climate, predation, human pressure, stress, and disease (e.g., [8–11]), is considered of paramount importance to determine animals' abundance [12]. Considering the use of

the trophic resources, populations may be assessed as generalist if they are composed by individuals feeding to the environmental availability of prey proportionally or as specialist if individuals select only a limited array of the available resource categories [13]. Amphibians in general, and salamanders in particular, are often seen as generalist, opportunistic predators that feed on a large range of prey [14]. However, some studies highlighted how salamanders actively select prey and may show diet specialization, at least under particular environmental conditions [15,16]. Moreover, while the realized trophic niche may result as the outcome of a generalist feeding strategy in a given population, it could be actually composed of specialist individuals consuming different resources [13,17]. Patterns of individual specialization, or interindividual diet variation, occur in hundreds of cases and in many taxa [18]. Interindividual diet variation indeed was observed in salamanders too, both at the postmetamorphic [16,19–21] and at the larval stage [22]. Individual specialization in salamanders is usually inferred by cross-sectional data, but also with longitudinal studies [20].

The Alpine salamander, *Salamandra atra* Laurenti 1768, is a widespread terrestrial salamander occurring in the Central and Eastern Alps, and in the Dinaric Alps where some isolated populations may be found [23,24]. Salamanders are abundant vertebrates in some environments and contribute to ecosystem resilience–resistance in several ways. One of these concerns their role as predators. The goal of this paper is to elucidate some aspects of the *Salamandra atra* as a predator in an alpine environment. There are few and mainly observational studies [25–28] on the feeding habits of the focal species, which were reviewed by Kuzmin [29]. Therefore, information on its trophic strategy is scattered and anecdotal [23,25–29]. So far, no fully quantitative information on the Alpine salamander diet comes from a single study [30]; that, however, was performed without taking into account trophic availability and feeding strategies. In the present paper, although during a short time frame about in the midseason of activity, we focused on the trophic strategy of this species at three different levels. At the top level, we defined the realized trophic niche by analyzing the mutual proportion of the preyed taxa. At the second level, we studied the trophic strategy in terms of prey selection, considering relationships between preyed taxa and their environmental availability, also taking into account the mainly nocturnal behavior of the Alpine salamander. Finally, we studied the trophic strategy of this species at the individual level.

#### **2. Materials and Methods**

#### *2.1. Study Species*

The Alpine salamander, *Salamandra atra*, is a fully terrestrial and viviparous salamander [24].  Activity pattern is concentrated in the warmest months (April–October) while in the rest of the year the salamanders are inactive at the ground surface [28].

#### *2.2. Study Area*

The study area (about 4000 m2) is located in the Paneveggio–Pale di San Martino Natural Park (Northern Italy), at about 1850 m a.s.l., near the locality Malga Venegiotta (municipality of Tonadico; 46◦18'48" N, 11◦48'53" E). At macroscale, the area is characterized by open habitats (pastures, other grasslands, and rocky areas) mixed with coniferous woodland, which is dominated by European larch (*Larix decidua*) and Norway spruce (*Picea abies*). Following the fourth level Corine Land Cover nomenclature, the study area is classified as "Coniferous forests with discontinuous canopy on mire" (habitat <IP_ADDRESS>). At smaller scale, the study area, where the sampling of salamanders and invertebrates was performed, is homogeneous and characterized by some coniferous trees mixed with small open habitat, such as rocks, grass, and dwarf shrub cushions dominated by *Erica carnea*. This site is especially suitable for sampling predators since at this place the detection probability of salamanders is high, as shown by a previous study [5].

#### *2.3. Prey Availability (Potential Trophic Niche)*

Ground-dwelling invertebrates were sampled by 20 pitfall traps. Each pitfall trap (500 cm3) was partially filled with a killing/preserving solution (salty water with and addition of 500 mg of benzoic/acetic acid) [31,32]. Pitfall traps were active for four days immediately after salamander sampling. They were divided in two typologies: 10 diurnal traps (DT, which were active from 7:00 a.m. to 7:00 p.m.) and 10 nocturnal traps (NT; which were active from 7:00 p.m. to 7:00 a.m.). DT and NT were inactivated by covering them with a plastic lid when they did not have to capture invertebrates. Pitfall traps are widely used to measure diversity and abundance of ground-dwelling invertebrates, e.g., [33]. These traps, although they may overestimate mobile fauna [33,34], it is reasonable to assume that such bias would not be misleading in the assessment of prey availability since prey mobility increases the detection probability by amphibians [35,36]. Traps were placed in 10 different trapping points within the salamander sampling area separated by a minimum distance of 30 m. In each trapping point, a pair of two pitfall traps (one DT and one NT) was placed at 20–50 cm from each other. Thus, in each trapping point we were able to sample diurnal and nocturnal invertebrate separately. To prevent the accidental fall of the salamanders in the traps, a 20 mm-mesh rigid plastic net was placed at the entrance of the traps when they were activated. Invertebrates obtained from environmental sampling and from stomach contents were sorted, identified, and counted using a dissecting microscope and taxonomic keys. Since invertebrates obtained with stomach flushing are partly digested, all invertebrates, both from stomach contents and from environmental sampling, were generally determined at the Order level or higher, annotating the life stage (i.e., we distinguished larvae from adults) and radical differences in locomotion type (e.g., flying Hymenoptera were distinguished from walking ones, e.g., ants).

#### *2.4. Sampling Predators*

Sampling of salamanders occurred within an area of about 4000 m2 in the first half of August 2018. Salamanders were sampled following rain and while active on the floor during two consecutive nights. They were transported to the laboratory, 5.5 km from the sampling site. Stomach contents were obtained by stomach flushing [37,38] performed by a single person using a 5 mL syringe [one injection per salamander] and a flexible soft plastic tube and preserved in 70% ethanol. Since there is a significant increase in digestion rate with increasing temperature [39], salamanders were stored at 5 ◦C in a refrigerator and they were flushed within three hours from capture [40,41]. A removal approach was used to avoid recaptures of the same individuals. Salamanders were photographed with a digital camera situated perpendicular to the dorsal surfaces of the animals. Digital photographs of salamanders were imported into the ImageJ® software program to measure their total length (TOTL, distance from the tip of the snout to the end of the tail). Sexes were distinguished by analysis of external secondary sexual characters; adult males have a prominent, swollen cloaca and are more slender than females. According to Klewen [27], we considered as "juveniles", which were excluded in the present study, those individuals without evident external secondary sexual characters and a TOTL smaller than 90 mm. All salamanders were returned to their original site within a maximum of 30 h from their capture.

#### *2.5. Data Analysis*

#### 2.5.1. Realized Trophic Niche

The sex differentiation in diet was analyzed by means of analysis of similarity [ANOSIM], based on Bray–Curtis distance [42]. The diversities of prey taxa in salamander stomachs and in the environment, as well as the diversity of prey taxa in salamander stomachs of our population and the two populations studied by Fachbach et al. [30], were estimated through Simpson's index [1-D] and 95% confidence limits calculated by bootstrapping [43]. In fact, although our method (stomach flushing) and that used by Fachbach et al. ([30], stomach dissection) are different, these two methods provide comparable results [44]. Analyses were performed in the statistical package PAST [45]. Considering

that prey availability is generally calculated on invertebrates captured within 24 hours for a few days, we compared the results of diurnal and nocturnal traps and the results of nocturnal traps versus the pooled results (i.e., diurnal plus nocturnal preys) by means of the diversity permutation test [9999 permutations].

#### 2.5.2. Trophic Strategy

The use of prey types in relation to their abundance in the environment was estimated by means of the Vanderploeg and Scavia [46] relativized electivity index (*E\**), which is strongly supported by comparative evaluations [47]:

$$E^\* = (\mathsf{W}\_i - 1/n)/(\mathsf{W}\_i + 1/n)^2$$

where *Wi* = *(ri*/*pi)*/*(*Σ *ri*/*pi*) − 1, *ri* is the relative abundance of prey *i* in the diet, *pi* is the relative abundance of prey *i* in the environment, and *n* is the number of prey types. This index ranges from +1 (positive selection) to −1 (avoidance), while *E\** = 0 indicated that prey items were consumed according to their availability. Since the index is particularly sensitive to the categories of prey with reduced environmental availability, and considering the low number of individuals within each prey taxon in our samples, the threshold electivity value (*u*) was considered only for prey type with more than three trapped individuals, calculating the fifth percentile of the absolute values of E\* [15,48]. The trophic strategy of the Alpine salamander was also analyzed with a modification of Costello's graphical representation [49,50]. According to this method, each prey type is plotted on a graph in which the x-axis is the prey frequency of occurrence (FO) in the predators' stomachs, and the y-axis is the prey-specific abundance (*Pi*), defined as the proportion of prey items (i), considering only all the prey items found in the individuals that consumed that specific prey type [50]. This graphical approach gives insights on the population feeding strategy: specialized (when some prey taxa have high *Pi* values and are projected in the upper part of the plot) vs. generalist (when all prey taxa have low *Pi* values and are projected in the lower part of the plot).

#### 2.5.3. Interindividual Diet Variation

Interindividual diet variation for the study population was assessed by means of network analysis [18,51]. Within this approach, the interactions between individuals and resources are interpreted as a bipartite network where two sets of nodes, one representing individual salamanders and one representing prey types, are connected by links reflecting the utilization of each prey type by individuals [52–54]. Individual specialization with bipartite network is often investigated with qualitative data [52,54] that only represent the use of a resource. The use of weighted networks where the frequency of use of each resource is retained, however, may give better estimates of some network metrics; therefore, we decided to use this approach [53,55]. Within our network approach, we employed the degree of diet variation *E*, as proposed by Araujo et al. [51] to quantify the presence of interindividual diet variation. This index is based on the pairwise diet overlap between individuals and increases from 0 to 1 in presence of individual specialization [18,51]. Two other network metrics were calculated: nestedness and modularity. Nestedness is observed when individuals with the narrowest trophic niche consume a subset of the prey types used by the more generalist individuals. The latter is recorded when, within a population, it is possible to segregate some individuals in groups (modules) that share the same resources. We used a metric of nestedness based on overlap and decreasing fill (NODF; [56]), which ranges from 0 to 100 (minimum and maximum nestedness, respectively). Modularity Q was measured and modules within the population were identified (function computeModules in the R package Bipartite) using the Beckett's algorithm [57], which ranges from 0 to 1 (minimum and maximum modularity, respectively). Since some level of both specialization degree, nestedness and modularity may arise from stochastic processes and sampling bias, significance of these metrics was tested by comparing the observed value against the simulated distribution obtained from a null model with 9999 (999 for modularity) resamplings.

#### **3. Results**

#### *3.1. Prey Availability (Potential Trophic Niche)*

During this study, 19 taxa of invertebrates were captured for a total of 650 individuals, of which 395 and 255 individuals were captured in the diurnal and nocturnal traps, respectively (Table 1).

**Table 1.** Environmental availability of invertebrates and number of invertebrates preyed by *Salamandra atra* in the study site.

Diversity index (Table 2) did not differ significantly between diurnal and total prey availability (i.e., DT + NT) (diversity permutation test, *p* = 0.91) while the difference between nocturnal and total prey availability was highly significant (diversity permutation test, *p* < 0.01). The analysis of similarities (ANOSIM) between total and nocturnal prey availability also showed significant differences (R = 0.29; *p* < 0.01).

**Table 2.** Diversity index of the available prey.

#### *3.2. Sampling Predators*

Fifty adult salamanders (i.e., with total length longer than 90 mm) were captured (26 females, 24 males) and stomach flushed with 41 positive, 4 individuals without prey in stomach, and 5 individuals with only indeterminate items (portions and fragments of prey unrecognizable, which were not attributable to an exact number of prey). By sorting of stomach contents, 176 invertebrates were obtained with a total of 139 analyzable items (*n* = 26 indeterminate, *n* = 11 parasite nematode) with an average of 2.78 ± 5.05 preys/stomach (mean ± s.d.; *n* = 45; range 0–34. Parasite nematodes were excluded) (Table 1).

#### *3.3. Realized Trophic Niche*

There was no overall difference in the diet composition between the sexes (ANOSIM, *n* = 41; global *R* = −0.049, *p* = 0.882). The analysis of the trophic niche, using the modification of Costello's graphical method [49,50], showed that *Salamandra atra* exhibited a generalized trophic strategy (Figure 1). Almost all prey categories are located in the left lower part of the graph, with both FO and *Pi* < 0.50, with only Diptera (fly) larvae located in the upper left quadrant (Figure 1). Fly larvae are the most abundant prey category in the diet, but they are eaten by a low number of individuals (FO = 0.097), thus suggesting a generalized trophic strategy at the population level.

**Figure 1.** Modified Costello's plot [49,50], describing the trophic strategy of *Salamandra atra.* Legend: Arac = Aracnidae, Chilop = Chilopoda, Coleop = Coleoptera adults, Coleop\_L = Coleoptera larvae, Coll = Collembola, Dipt = Diptera adults, Dipt\_L = Diptera larvae, Hemip = Hemiptera, Hym\_F = flying Hymenoptera, Isop = Isopoda, Lep\_L = Lepidoptera larvae, Moll = Mollusca, Oligo = Oligochaeta.

The comparison of prey percentage in stomach of Alpine salamanders from our population and those of Fachbach et al. [30] is reported in Figure 2. Although some prey taxa were exclusive of a given population (e.g., Balttoidea, Dermaptera, Rhynchota), in the whole these three populations showed very similar diversity indices of the prey taxa (Simpson's index, 1-D, with 95% confidence limits, CL. Population 1: 1-D = 0.86, CL = 0.86–0.90; Population 2: 1-D = 0.85, CL = 0.83–0.90; Population 3: 1-D = 0.86, CL = 0.83–0.90).

#### *3.4. Analysis of Trophic Strategy*

Electivity index *E\** was calculated using both nocturnal (Figure 3) and total (Figure 4) trophic availability. In both cases, *E\** was negative for Hemiptera, Isopoda, Diptera, Aracnida, and Collembola, positive for Chilopoda, and proportional to environmental availability for Coleoptera larvae. Conversely, Mollusca resulted positively selected considering the total trophic availability, but they were randomly selected in the nocturnal availability. Worth noting is that E\* provided completely opposite results for Coleoptera, which were negatively selected considering total availability but positively considering only nocturnal traps. Therefore, the two trophic availabilities (nocturnal and diurnal), when analyzed separately provided different or even an opposite pattern of prey's selectivity. These different results arised also considering the two main prey types, as shown by the realized trophic niche. Finally, salamanders operated a strong negative selection on the more representative categories in the trophic availability, which are Collembola, Diptera, and Formicidae.

**Figure 2.** Comparison of prey percentage in stomach of Alpine salamanders from our population (Population 1, *n* = 41) and those of Fachbach et al. [30] (Population 2, from Gleinalmspeik, Germany, *n* = 15; Population 3, from Grimming, Germany, *n* = 26) [30]. The suffix L indicates the larval stage of the taxa; the suffix F (flying) indicates the winged taxa or the winged stage of the taxa.

**Figure 3.** Relativized electivity index E\* based on nocturnal trophic availability. Within the red vertical lines values are not statistically significant. The suffix L indicates the larval stage of the taxa.

**Figure 4.** Relativized Electivity index E\* based on total trophic availability. Within the red vertical lines values are not statistically significant. The suffix L indicates the larval stage of the taxa; the suffix F (flying) indicates the winged taxa or the winged stage of the taxa.

#### *3.5. Analysis of Interindividual Diet Variation*

The weighted bipartite network of individuals and resources is presented in Figure 5. The degree of diet variation *E* = 0.87 result was statistically highly significant (*p* < 0.001) and indicated a high variation among diets of individuals. The NODF = 22.4 metric indicated a moderate but significant (*p* < 0.001) nestedness in the network. At the same time, a high and significant modularity (Q = 0.58; *p* < 0.001) was present, and seven modules were identified (Figure 6).

**Figure 5.** Bipartite network of salamanders and resources. Edge width represents frequency of use. The suffix L indicates the larval stage of the taxa; the suffix NF (not flying) indicates the not winged taxa or the not winged stage of a taxon.

**Figure 6.** Adjacency matrix, with individual salamanders as rows and prey types as columns. Cell color represents frequency of use, red boxes indicate modules. The suffix L indicates the larval stage of the taxa; the suffix NF (not flying) indicates the not winged taxa or the not winged stage of a taxon.

#### **4. Discussion**

#### *4.1. Prey Availability*

Data about potential trophic niche highlighted rather low food availability, particularly in comparison with other studies about trophic strategy of other Italian salamanders carried out in similar periods with a similar number of pitfall traps [15,16,18]. These differences are not surprising due to the high-altitude alpine environment. Indeed, several studies confirm that conditions in mountains affect the species richness, composition, and density of different inver tebrates (e.g., [58–60]). Consequently, the low density of soil macroinvertebrates leads to a highly reduced amount of prey found in salamander's stomachs, in comparison with other species of terrestrial or semiterrestrial salamanders (e.g., ranging from 4.37 to 13.09 prey/stomach, [15]). The limited number of food items in the salamander stomachs may be due not only to the low prey availability, but also by the dimensional selectivity related to morphological characteristics of the predators and their predation strategy (see Section 4.2. and Section 4.3). ANOSIM and the diversity permutation test showed significant differences between total and nocturnal trophic availability, which strongly suggest that the actual daily activity pattern of the studied species must be taken into consideration because analyses that do not consider this factor can lead to misleading conclusions.

#### *4.2. Realized Trophic Niche*

There were no differences between males and females, according to previous studies [15,61,62]. The graphical Costello's method modified by Amundsen [48,49] identified a generalized trophic strategy in which Coloeptera and Mollusca represent the most valuable taxa of the diet (Figure 1). Diptera larvae is the only prey's category located in the upper-left quadrant of graph, indicating specialization. However, a low number of individuals preyed on this taxon (*n* = 4), and for only one of them, Diptera larvae represented 82% (31 of 38) of the diet. In this case, the high level of individual specialization, as exhibited by a single salamander, plays a marginal role in the overall picture of the population trophic strategy. The graphical plot showed that Alpine salamander avoided the use of small-sized taxa like Collembola, Diptera, and Formicidae, even if they are the dominant ones in the environment. The avoidance of small-sized taxa can be explained by considering the low number of preys/stomach (2.78) and the big size of this salamander. In fact, consumption of preys in amphibians is largely bound by morphological and physiological characteristics [63]. Furthermore, for Urodela, a positive correlation between dimension of the predator and prey has long been known [64]. Probably, predation strategy, not particularly refined and efficient to catch skillful prey, can also represent an important limit to usage of small-sized preys. Trophic strategy of *S. atra* pointed out by our study only partially agrees with observational information obtained in another Italian population [23], which exhibited a preference for Mollusca and Oligochaeta, followed by Orthoptera, Homoptera, Dermaptera, Isopoda, and Arachnida. Unfortunately, we could not perform an accurate comparison due to an absence of numerical data. Conversely, the study carried out by Fachbach et al. [30] on two German populations, although it did not report data for inferences on trophic strategy and selectivity, it provided numerical data on ingested preys. From the comparison of our data to those of the German populations (Figure 2), our population differed in the proportion of the prey taxa while the two German populations were more similar to each other (Figure 2). However, the overall diversity index of preyed taxa among the three populations was extremely similar. The most used categories in Fachbach and coauthors' study [30] were Coleoptera, Arachnida, and Coleoptera larvae, while few taxa seem to characterize just one population (e.g., Collembola in Population 2). In contrast, our study highlighted a preference for Diptera larvae, Mollusca, Chilopoda, and Coleoptera. Although the sampling methods to obtain information on preyed taxa were different (Fachbach and coauthors euthanized the salamanders), both methods collected preys directly by the stomach and may be considered comparable. Sample sizes were also comparable, although our salamanders belonged to the same population while the German ones were sampled from two different and distant populations. Consequently, differences

among these populations in the ratio of preyed taxa may depend either on ecological variables in different environments (e.g., food availability) or on different behavioral traits at interpopulation level, or on both. The two German salamander populations [30] seem to occur in similar habitat (1200 m and 1600–1800 m a.s.l., calcareous geological substrate, open habitats mixed with coniferous woodland). Unfortunately, Fachbach et al. [30] reported only simple frequencies of preyed taxa for each population, but he did not report the number and type of prey per salamander and did not include trophic availability. Consequently, comparisons of electivity index or Amundsen method are inapplicable. However, the similar low average number of prey/salamander (2.78 from our study and 2.58 from Fachbach et al. [30]) suggest that the low number of ingested preys may be regarded as a characteristic trait of this species rather than that of a given population.

#### *4.3. Trophic Selectivity*

Definition of potential trophic niche is an indispensable tool to study trophic strategy and selectivity. Therefore, it is crucial to obtain data that are a good representation of the real trophic availability accessible to a given species. Dietary studies on other salamanders tried to achieve this target by using different types of traps that can collect samples from different habitats, like pitfall, sticky traps, and Berlese–Tullgren extractor [15,16]. Such methods are really effective to obtain data about the potential trophic niche, but should be used considering the activity period of the target species. Our results, for the first time, demonstrated that taking into account the temporal activity pattern of a predator may lead to a different interpretation of the trophic strategy. In literature, all studies about trophic strategy of salamanders perform arthropod sampling continuously for several days without considering the daily activity (and foraging) pattern of the studied species. However, we demonstrated that significant differences between total and nocturnal trophic availability could lead to different or even opposite interpretation of selectivity, which may also involve the most relevant taxa in the realized trophic niche. Electivity index, calculated with nocturnal trophic availability, shows a positive selection for Coleoptera and Chilopoda, highlighting the important role in summer's trophic strategy of these taxa. Mollusca and Arachnida were also identified as relevant preys. The first was selected almost in relation to their abundance in the environment (they are just above the significance threshold, Figure 3), the second were exposed to a counter selection. Generally, relativized electivity index showed a negative selection for Collembola, Formicidae, and Diptera, which are, however, the most abundant in trophic availability. Therefore, selectivity results were essentially in agreement with tropic strategy defined by Amundsen's method. As reported in Section 4.2, selection of prey type could be explained by dimension and hunting strategy of the Alpine salamander. Small number of preys per stomach could indicate a dimensional selectivity; for a large species such as *Salamandra atra,* foraging on few but large preys seems more worthwhile than eating many little ones, considering that a predator tends to maximize the energy intake consuming preys that give the best energy gain per time unit [65,66]. Given that selection seems to operate more on the prey's size than on taxonomic categories, it will be useful to analyze trophic selectivity of Alpine salamander using volumetric classes that represent a possible measure of energy-intake [15], which can show a more detailed overview of trophic strategy of *Salamandra atra*.

#### *4.4. Interindividual Diet Variation*

The presence of interindividual diet variation in *Salamandra atra* is consistent with what was observed in other salamander species (e.g., [16,18]). Patterns of interindividual diet variation can be explained by optimal diet theory (ODT) [67,68], since individuals may have different traits affecting their ability to capture or handle different prey types and they will rank prey preferences accordingly. In this context, ODT theory accounts for three distinct patterns that may cause individual specialization [69]. Individuals may have different rank preferences and therefore specialize on different prey types. When competition is present, individuals should include new resources in their diet and increase overlap; we call it the distinct preferences model. Alternatively, under the competitive refuge model,

individuals may share the first ranked prey and have different rankings for the less preferred ones. It implies that, under intraspecific competition, individuals will broaden their trophic niche, including different prey types according to their ranking preferences. The third model, the shared preferences model, assumes that individuals have identical preferences but differ in the willingness to include lower ranked prey in their diet. Consequently, concerning network analysis, the shared preferences model suggests the presence of significant nestedness [18] under intraspecific competition, but no modularity could be observed. Modularity in turn, should be present in case of the distinct preferences model, when resources are unlimited and competition is low, or by contrast, in the competitive refuge model when resources are limited and competition is high [53]. In our case study, the observed emergence of a significant modular network may be explained by both the competitive refuge model and the distinct preferences model. If the available resources are low, intraspecific competition is present and the preferred shared resource becomes scarce or less profitable, driving the individuals to the inclusion of different prey types, and to the emergence of several modules. The observed modules, however, could also arise from a distinct preferences model, where resources are unlimited and individuals, released by competition, specialize on their preferred prey items.

#### **5. Conclusions**

The diet of Salamandra atra, a fully terrestrial salamander, is investigated in depth for the first time, although during a short timeframe in the first half of August (i.e., about in the midseason of activity [23]). It showed a generalist trophic strategy; not all prey were consumed proportionally to their environmental availability. Considering diet variation at the individual level, the competitive refuge model and the distinct preferences model, within optimal diet theory, seem to equally fit our results. However, given the low trophic availability, the former is more supported. As a general rule, if the sampling period was short, it may not describe exhaustively the feeding habits of a given population. Although it is true that cross-sectional studies may overestimate individual specialization, in particular when prey distribution reflects a patchy environment [13,17], but our sampling was performed in a relatively small and homogenous area and this should reduce the bias. Furthermore, cross-sectional design represents the majority of data-type used in the analysis of interindividual diet variation (see reviews by [17,18]). Considering the lack of knowledge about trophic habits of Alpine salamander, the study of the trophic strategy is the first crucial step to understand the ecological role of this species. Further research will be necessary to investigate any possible variations in diet during the whole activity season and the functional relationship existing between predator and its prey, in terms of relative abundance and biomass. Finally, as a methodological contribution, we highlighted that considering the daily activity pattern of a species is a fundamental requirement for correct data collection and resulting interpretation. Our study was performed on a population of the nominal subspecies. Further investigations on the others, three subspecies with two of them extremely localized and Italian endemic [70,71], may elucidate any feeding differences among these taxa.

**Author Contributions:** Conceptualization, A.R; Methodology and sampling, L.R. and A.R.; Formal analysis, L.R., A.R., and A.C.; Data curation, L.R. and A.R.; Writing—original draft preparation, A.R., L.R., and A.C.; Writing—review and editing, A.R., L.R., A.C., P.P, S.L., and G.M.; Coordinator of the project R2000 monitoring, P.P. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Acknowledgments:** This paper is the result of the master thesis of Luca Roner. The study received funding within the agreement 2017–2019 between MUSE and CNR-ISAFOM. Experimental protocols were approved by the Italian Ministry of Environment (authorization PNM-EU-2017-005370). This study was supported by Servizio Aree Protette e Sviluppo Sostenibile PAT (Piano di monitoraggio fauna vertebrata Direttiva Habitat; Progetto LIFE11/NAT/IT/000187 "TEN"–Trentino Ecological Network) and by the Paneveggio–Pale di San Martino Natural Park, which also provided logistic facilities close to the sampling site. In particular, we are grateful to the Technical Coordinator Piergiovanni Partel, and to the Director Vittorio Ducoli.Thanks to Michele Chiacchio, Francesco Maria Romano, and the park guard Gilberto Volcan, who contributed to the field sampling.

**Conflicts of Interest:** There are no conflicts of interest associated with this publication and there was no significant financial support for this work that could have influenced its outcome.

#### **References**

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).


*1.3. Trampling*

Another explanation for the increased abundance of woody vegetation is the effect of trampling. Trampling from the high frequency and density of pastoral farming causes significant declines in cyanobacterial soil crust [32,33]. Savannas are characterized by low soil nutrient content [34–36], although many areas have biological soil crusts that increase soil surface stability, thereby reducing nutrient loss by erosion and atmospheric nitrogen fixation [33]. Studies have found that the soil crust is greatly influenced by this pastoral trampling within 2 to 8 km of boreholes [37], and that *Acacia* (new *Senegalia* and *Vachellia* classifications) species are often found in higher abundances within areas closer to boreholes, due to their low palatability and the positive species-specific association between canopy and soil crust development [38]. Boreholes are narrow shafts drilled into the ground in order to extract water and are the primary source of water for livestock farmers in southern Africa. Furthermore, cattle rarely stray more than 13–18 km from these water sources in Africa [39], meaning areas closer to boreholes may have increased woody vegetation cover.

## *1.4. Fire*

Fire is a factor that restricts woody vegetation diversity and abundance, preventing the formation of canopies [40–42] as well as removing seedlings and subsequently preventing the establishment of new trees [43]. Furthermore, for certain species fire can also kill the larger trees [44,45].  In unmanaged areas, the build-up of large quantities of grass biomass in the understory results in high-intensity fires that are capable of destroying juvenile trees [46]. For example, Sankaran et al. [11] studied the effect of fire return intervals on the percentage of woody cover in African savannas and found that a shorter return interval reduced established woody cover, which kept the community in a juvenile state by 'top-killing' seedlings. In managed landscapes, fires are not as frequent or intense enough to have a discernible impact on mature trees [40], and a common feature of savannas is the reduction of fires due to mitigation strategies [47]. However, Joubert et al. [48] note that fire is crucial to disrupt transition from grassy savanna to thicket, and that managers who prevent fires at this stage are likely to experience bush thickening in the future.

#### *1.5. Research Gap and Questions*

Variation in species characteristics is fundamental to understanding biogeographic patterns [49]. One reason for the possible lack of conclusive evidence explaining the main drivers of di fferent woody vegetation patterns in previous research is the variation in how vegetation has been measured (e.g., single species, multiple species, richness, percent woody cover), as well as the di fferences in spatial scales of the previous studies (ranging from garden experiments to coarse continental extents). Assessing diversity as total species richness does not always adequately characterize the way in which species di ffer from each other, and it is these di fferences in traits, which often indicate that species respond in di fferent ways to changes in the environment [50,51]. Alternatively, studying only one species in isolation could lead to species-specific results that are not generalizable to the larger system or to other species. Several mechanisms (outlined above) have been invoked to explain the processes responsible for woody vegetation composition; however, these are often investigated separately at scales not best suited to land-managers, thereby impeding the evaluation of their relative importance.

Subsequently, this study focuses on the vegetation composition of the Botswana Kalahari, with the aim to investigate the relative influence of the environmental drivers of woody vegetation at a regional scale. By classifying species into morphological groups based on shared physiological traits, the drivers of woody vegetation richness and abundance can be interpreted more meaningfully at a regional scale that is more appropriate for landscape management decisions. This study will explore three main questions: (1) what is the woody vegetation composition of the Kalahari in western Botswana? (2) What are the environmental drivers of woody species richness? and (3) what are the environmental drivers of woody species abundance?

#### **2. Materials and Methods**

#### *2.1. Study Area*

We conducted our research in western Botswana between 2009 and 2011 (Figure 1). We created a 950 km transect following the observed rainfall gradient along the western part of the Kalahari. This transect ran from Shakawe in the northwest of the country to Bokspits in the southwest of the country. Rainfall along the transect decreases from the north to south, ranging from a MAP of 550 mm to 350 mm [52]. Along this transect, we identified 15 regions (Figure 1) where we conducted multiple vegetation surveys. We selected regions on their accessibility and a minimum distance of 75 km to the previous region.

**Figure 1.** Location of the 15 regions along the Kalahari Transect where fieldwork was undertaken.

#### *2.2. Data Collection*

Vegetation was surveyed using the line interception transect (LIT) method. Within each region, we fixed six 100 m transects radially from a center point. For the dry season, the direction of the first transect was determined by a random number (between 0 and 360), and the further two transects were offset by 120 degrees. Transects of the wet season were spaced exactly between dry season transects, resulting in an offset of 60 degrees from the very first transect laid. Transects were placed 200 m from the center point to avoid over-sampling a small area. See Krebs [53] for a further description of the LIT methodology. We recorded all woody vegetation that was taller than 25 cm following the nomenclature provided by Palgrave [54], whereby average height, distance covered over the transect line, and distance and direction of the stem(s) were documented. Species richness and abundance were recorded at all transects, and species identity were recorded at all sites, with the exception of the wet season transects at Sites 1, 3, 4, and 5 due to uncertain species identification resulting from missing leaves. The results of the vegetation survey meant we had data from 78 transects for use in the statistical analysis.

Species were categorized into five morphological groups based on the classification guidelines outlined by Meyer et al. [55]. Morphological group I consisted of species characterized by bipinnate leaf structures and growth form ranging from multi-stemmed shrub like appearance to single-stemmed trees. Morphological group II included broad leaf species forming dense canopy structures where the majority of the growth form is either multi-stemmed (generally less than five stems) or single-stemmed. Morphological group III contained multi-stemmed broad leaf shrubs with closed canopies, seldom exceeding 2 m in height. In contrast, morphological group IV contained shrub species characterized by open canopies. Morphological Group V included relatively short shrub species (<1.5 m) with small, open canopies (<0.5 m in diameter). We also obtained data on precipitation, fire frequency, cattle density, and borehole locations that represent the possible drivers of diversity and abundance of woody vegetation (Table 1).

**Table 1.** Description of the environmental drivers used to explore the diversity and abundance of woody vegetation in western Botswana.

#### *2.3. Data Analysis*

We performed regression analysis in order to explore the environmental drivers of woody species richness and species abundances. Environmental variables were checked for multicollinearity using variance inflation factor, then standardized using z-scores in order to compare their relative influence on the ecological indicators. We performed all regression analyses using R 3.3.0. [61]. We selected regression analyses based on a preliminary evaluation of the data and their error distribution. Histogram exploration identified a mixture of Poisson and censored Gaussian distributions. We subsequently used a combination of generalized linear models with Poisson error distributions and Tobit regression models to analyze our data. For data that had a Poisson distribution, a Generalized Linear Model procedure with a Poisson error distribution and a log link function was used:

$$\log(y) = \beta\_0 + \beta\_1 X\_1 + \dots + \beta\_n X\_n \tag{1}$$

where *y* is the abundances, *Xn* is the *n*th predictor, and β*n* is the Poisson regression coefficient.

A censored Gaussian distribution represents a dataset that has a normal error distribution, but has some limit, either from below or above. Ecological data is often collected with a large proportion of the observations just above zero, while data cannot extend below zero or above certain thresholds (e.g., percentage cover). Tobit regression overcomes this bias and has been shown to perform better than ordinary least squares (OLS) (e.g., [62]) and is widely used in criminology (e.g., [63]) and land use change research (e.g., [64]). Species richness of woody vegetation is censored at zero (i.e., there cannot be a species richness of −1), and so any parameter estimates obtained by conventional OLS would be biased. Developed by Tobin [65], the Tobit regression model fits a set of parameters to where the dependent variable is left-censored at zero:

$$y\_i^\* = x\_i \beta + \varepsilon\_i \tag{2a}$$

$$y\_i = \begin{cases} \ 0 \text{ if } y\_i^\* \le 0 \\ \ y\_i^\* \text{ if } y\_i^\* > 0 \end{cases} \tag{2b}$$

where the subscript *i* = 1, 2, 3 ... *n*, indicates the observation, *y*∗*i* is an unobservable variable, *xi* is a vector of explanatory variables, β is a vector of unknown parameters, and ε*i* is the error term. To estimate the censored regression models, we used the censReg [66] and MaxLik [67] packages. Final models were selected based on Akaike Information Criterion (AIC) using both forwards and backwards stepwise selection of models. We investigated third and fourth order interactions, but these did not improve the final models. Therefore, our final models only include main effects and second order interactions.


*Article* **Swimmers with Down Syndrome Are Healthier and Physically Fit than Their Untrained Peers**

**Ana Querido 1,2,3,\*, Mário J. Costa 2,3 , Dália Araújo 2,3, António R. Sampaio 1,2,3 , João P. Vilas-Boas 2,3 , Rui Corredeira 4 , Daniel J. Daly 5 and Ricardo J. Fernandes 2,3**

**Abstract:** While there are positive benefits from physical activity participation for individuals with Down syndrome, little is known about the effects of swimming training. The aim of this study was to compare the body composition and physical fitness profile of competitive swimmers and moderately active (untrained) individuals with Down syndrome. The Eurofit Special test was applied to a group of competitive swimmers (*n* = 18) and a group of untrained individuals (*n* = 19), all with Down syndrome. In addition, measurements were taken to determine body composition characteristics. The results showed differences between swimmers and untrained subjects in height, sum of the four skinfolds, body fat %, fat mass index and all items of the Eurofit Special test. Swimmers with Down syndrome exhibited physical fitness levels near to the Eurofit standards, although lower fitness levels were attained by these persons when compared to athletes with intellectual disability. It can be concluded that the practice of competitive swimming seems to counteract the tendency for obesity in persons with Down syndrome and also helps to increase strength, speed and balance.

**Keywords:** physical fitness; body composition; health; measurement; Down syndrome

#### **1. Introduction**

Down syndrome, a form of intellectual disability, is a genetic disorder caused by the presence of the whole (or part) of an extra copy of chromosome 21, with a global incidence estimation of 1 in 1000 to 1 in 1200 live births [1]. These individuals, present distinctive physical features, are predisposed to a higher incidence of cardiovascular disease [2], diabetes [3], osteoporosis and obesity, and more susceptible to a premature and significant decline in function with age [4]. Despite this, the infant Down syndrome survival rate, as well as life expectancy in general, continues to increase [5,6].

Lack of regular physical activity has been identified as one of the most significant health risks and people living with chronic conditions or disability are now being given recommendations for the first time [7]. This inactivity results in an increased threat of chronic conditions (e.g., cardiovascular disease and type 2 diabetes) [8] and is considered as a predictor of mortality in the Down syndrome population [9]. Literature indicates low fitness levels in these individuals [10–12], which may be related to sedentary lifestyles [13], limited social and recreational opportunities [14] and/or low motivation to be physically active [15]. Nevertheless, several studies have indicated positive benefits from physical activity participation for these individuals [16–18] and evidence suggests that physical activity can increase their physical fitness [19]. Specifically, aquatic exercise has been

**Citation:** Querido, A.; Costa, M.J.; Araújo, D.; Sampaio, A.R.; Vilas-Boas, J.P.; Corredeira, R.; Daly, D.J.; Fernandes, R.J. Swimmers with Down Syndrome Are Healthier and Physically Fit than Their Untrained Peers. *Healthcare* **2023**, *11*, 482. https://doi.org/10.3390/ healthcare11040482

Academic Editors: Herbert Löllgen and João Paulo Brito

Received: 27 December 2022 Revised: 3 February 2023 Accepted: 4 February 2023 Published: 7 February 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

shown to offer benefits for people with intellectual disabilities in terms of cardiorespiratory endurance, muscular endurance, speed, static balance and agility [20,21].

There has been an increasing interest from people with Down syndrome in competitive swimming, with the participation of ~200 swimmers at the 2022 World Championships held in Portugal. Even if physical activity and sport are meaningful to many people, including those with intellectual disabilities, research in this topic has focused mainly on inactive participants [22], while trained individuals are scarcely studied [23]. In the specific case of competitive swimming, one study showed that an 18 week training intervention did not promote clear changes in jumping performance or body composition in swimmers with Down syndrome [24]. In a contrasting study, 33 weeks of swimming training lead to improved health status and swimming skills [25]. So, some mixed findings exist on how swimmers with Down syndrome may react to swimming training and exhibit improved physical form in comparison to their non-swimmer peers. This lack of evidence makes us question if swimmers with Down syndrome, even those at the top level, still remain healthier, or show better physical condition than their peers who are not involved in any intensive sport participation.

Thus, the aim of this study was to assess the body composition and the physical fitness profile of competitive swimmers with Down syndrome and to compare them to untrained peers. It was hypothesized that: (i) swimmers would present lower values than untrained individuals for Body Mass Index (BMI), percentage of total body fat (fat%) and Fat Mass Index (FMI), and higher values for Lean Body Mass (LBM); and (ii) swimmers would also present higher physical fitness values than untrained counterparts.

#### **2. Materials and Methods**

#### *2.1. Participants*

To be elegible for this study, trained participants had to be, as minimum, national competitive swimmers, being involved in a minimum of 6 h of swimming training per week, for at least 3 years. To be a part of the control group, participants could not be involved in any kind of competitive sport practice for the last 3 years.

Thirty-three individuals with Down syndrome participated in this study: 18 were national or international level trained swimmers and 19 were untrained persons with Down syndrome. Trained swimmers were 22.2 ± 5.4 years, practiced 7.4 ± 0.8 h per week over the entire year and had been involved in swimming training for several years. More than half of the participants were part of the National Federation Swimming Team and participated in DSISO International Championships. Untrained individuals were 26.6 ± 8.2 years and were involved in two 45 min sessions a week of general physical activity. All individuals, or their parents, gave written informed consent to participate in this study, which was approved by the local ethics committee (under the code: CEFAD 19.2020) and carried out according to the Declaration of Helsinki.

#### *2.2. Procedures and Measures*

The body measurements included height, weight and four skinfolds (triceps, biceps, subscapular and supra-iliac, using a Harpender skinfold caliper). BMI (in kg/m2 ) was defined as body mass (in kg, measured using an electronic weighing scale) divided by height squared (in m). The fat% and LBM were derived from the measured skinfolds, using the equation proposed by Durnin & Wommersley [26] and the FMI was calculated as fat mass/height2 . All measurements were made on the right side of the body by the same evaluator and were repeated three times, with the mean values being used [27]. The classification of obesity was made according to the World Health Organization technical report as follows [28]: underweight if BMI ≤ 18.4, normal weight if BMI 18.5–24.9, overweight if BMI 25–29.9, obesity if BMI 30–39.9 and morbid obesity if BMI ≥ 40.

To evaluate physical fitness, the Eurofit Special test was used, as follows [29]: (i) explosive lower limb strength was determined with a standing broad jump; (ii) upper limb strength was determined using a 2 kg medicine ball push performed with the preferred

arm—from a standing position, the ball was placed in the palm, supported by the second hand and pushed forward in a shot put like action; (iii) local muscle endurance was determined by the number of correctly completed sit-ups in 30 s; (iv) speed was measured for a 25 m run from a standing start measured to 0.1 s using a manual stopwatch; (v) flexibility was measured with the sit-and-reach test; and (vi) balance was determined by walking on a bench. Two test trials (Test A and Test B) were performed without shoes. In test A, the participant approached the bench, stepped onto it and walked forward. If Test A was successful, Test B is attempted. For test B the same process applies, with the bench in the upside-down position. Each test had to be completed in 30 s, with points recorded on the following scale: 1 point if the participant responds to the instructions; 2 points if the participant approaches the bench; 3 points if the participant walks 2 m without support or the entire bench with support (Test A); 4 points if the participant walks along the entire bench without support (Test A); 5 points if the participant walks 2 m without support or the entire bench with support (Test B); 6 points if the participant walks along the entire bench without support (Test B). A familiarization for all tests was allowed two weeks before the data collection to guarantee that the physical fitness tests were fully understood by the subjects and could therefore be carried out properly.

The results from the body mass, height and all the Eurofit items were then converted to percentile scores. This facilitated merging of gender groups for this study (14 males and four females swimmers and nine males and 10 females untrained). The norm scales for severe intellectual disabled individuals of 20 years old (top off age for the scale) were used [30]. This table was chosen for two main reasons: (i) most raw scores measured fit this table, particularly the control group, and (ii) 20 years was the closest age to the sample studied. Scores outside the scale were given the maximum or minimum points, as appropriate.

#### *2.3. Statistical Analysis*

Descriptive statistics were calculated for all the variables (raw values as well as percentile scores) and all data were checked for normality and homogeneity of variance using Shapiro-wilk and Levene tests, respectively. Mean and SD for all variables are presented. An independent sample t-test was used to verify if there were differences between groups on performance and body composition (independently of the sex). Cohen's d was calculated for effect size and interpreted as: small if d ≤ 0.2, moderate if d between 0.2 and 0.5, and large if d ≥ 0.5). The statistical significance was set at *p* ≤ 0.05. Procedures were performed with SPSS Statistics (v. 27, IBM, SPSS Inc., Chicago, IL, USA).

#### **3. Results**

Table 1 presents the comparison of body composition and all variables of the Eurofit Special Test between trained swimmers and untrained subjects. The swimmers presented higher values for height and lower values for the sum of the skinfolds, BMI, fat% and FMI, all with large effect sizes.  In the swimmers group, 10 persons were of normal weight, six were overweight, and two were obese whereas in the untrained group, seven were of normal weight, four were overweight, six were obese, and two were morbid obese. Thus, 44.4 and 52.6% of the participants were overweight or obese in swimmers and untrained individuals, respectively and 10.5% of the untrained group showed morbid obesity.

**Table 1.** Mean (± SD) and effect size values for the body composition and all variables of the Eurofit Special Test for trained swimmers and untrained subjects.

BM = body mass, SS = skinfolds sum, BMI = body mass index, Fat % = percentage of total body fat, LBM = lean body mass, FMI = fat mass index, LJ = long jump, MB = medicine ball, Flex = flexibility, Bal = balance. Differences between genders in swimmers are identified by a, differences between genders in untrained individuals are identified by b and differences between swimmers and untrained individuals are identified by c (*p* ≤ 0.05).

Swimmers with Down syndrome also scored better with large effect for long jump, medicine ball, sit-ups, speed and balance, while for flexibility there was a moderate effect.

Table 2 presents the percentile scores that are based on norms for severe intellectual disability without Down syndrome. Swimmers presented higher scores for all variables (groups were not different in body mass), with large effect sizes, except for the medicine ball throw with a moderate effect. Despite those differences, both swimmers and untrained subjects showed low percentile scores for height (28.8 ± 14.5 for swimmers), and medicine ball (36.7 ± 25.3 for swimmers) on the scale used.

**Table 2.** Mean (± SD) and effect size percentile values for all variables of the Eurofit Special Test, body mass and height for trained swimmers and untrained subjects.

Differences between groups are identified by a (*p* ≤ 0.05).

#### **4. Discussion**

The purpose of this study was to assess the body composition and the physical fitness profile of competitive swimmers with Down syndrome and to compare them with untrained individuals with the same condition. It was found that swimmers with Down syndrome present a healthier body composition and a higher physical fitness score than untrained individuals with Down syndrome.

#### *4.1. Body Composition*

Epidemiological studies often use BMI as a measure of weight status [31] since it is a good indicator of body fatness and easily calculated [32]. Nevertheless, several authors reported that BMI is not sufficient to describe the body composition of individuals [33–35].  Pitetti et al. [37] pointed out that ambiguous evidence exists regarding body composition in persons with Down syndrome and that the lack of consistency may involve methodological issues in measuring body composition or in the comparison of the weight status using different methods. Therefore, caution is urged when interpreting global statements on body composition in Down syndrome.

Numerous studies have reported that the prevalence of overweight and obesity are substantially higher in individuals with Down syndrome compared to their age-matched peers without disability, as well as those with intellectual disability but not related to Down syndrome [37]. Prasher [38] reported that ~48% of adults with Down syndrome were obese with ~27% being overweight and Rubin et al. [39] found ~48% of men and ~56% of women to be overweight or obese. In the present study, according to the BMI criteria, 55.6% of the swimmers and 36.8% of the untrained were normal weight, 33.3% of the swimmers and 21.1% of the untrained were overweight, 11.1% of the swimmers and 31.6% of the untrained were obese and 10.5% of the untrained were on the morbid obesity range. According to the fat% criteria, a much larger percentage of swimmers (83.3%) and a smaller percentage of untrained (31.6%) were considered normal. A much smaller percentage of swimmers were considered overweight (11.1%), while for the untrained the percentage rose to 63.2%, while 5.6% of the swimmers and 5.2% of the untrained were on the borderline range.

Although the equation described by Durnin & Womersley [26] to estimate body fat is not specific for Down syndrome, it was nevertheless previously used with this population [40]. These authors calculated the fat% in male adolescents with Down syndrome before and after a 12-week moderate aerobic training program. These adolescents decreased their fat mass percentage after the program (31.8 ± 3.7% pretest and 26.0 ± 2.3% posttest). We should note that male swimmers from our study presented much lower fat% than these adolescents (18.8 ± 3.7%).

In the current study, swimmers presented higher values for height and lower values for the sum of skinfolds, BMI, fat% and fat mass index than untrained peers. There was a large effect for all of the variables above, indicating that, not only swimmers are different from untrained, but that those differences are great. Gonzalez-Aguero et al. [19] stated that body composition in this specific population is, in general, poorer than what is observed in their peers without Down syndrome, as proven by higher BMI, lower levels of lean mass and reduced bone mass-related parameters. Bertapelli et al. [41] reported several causes for the augmented obesity in persons with Down syndrome, such as genetic, physiological, and environmental factors. However, as mentioned previously, the common term "obesity" used to describe physical characteristics in individuals with this condition might not always be valid [42]. For instance, a review by Gonzalez-Aguero et al. [19] reported mixed results and, if some studies indicate higher fatness values for people with Down syndrome [41,43–46], others present similar levels for persons with Down syndrome relative to persons without [42,43,47]. Despite these uncertainties, people with Down syndrome seem capable of improving their body composition values with training [40,44].

In athletes with Down syndrome or intellectual disability, systematic training seems to lead to healthier body composition, and, consequently, a better quality of life [19]. In a study from Aleixo et al. [48], with a small number of individuals with Down syndrome, differences in BMI between swimmers (24.3 ± 4.1) and untrained (36.8 ± 5.3) were observed, with swimmers being placed on the normal weight range and the untrained individuals at the obesity level. On the other hand, Balic et al. [16] analyzed 13 trained individuals who participated at the Special Olympics Games and seven sedentary adults, all with Down syndrome, and found no differences between both groups in BMI, height, weight and fat%. We may argue that, at the time of the study of Balic et al. [16] (approximately 20 years

ago), the training for the Special Olympics started to be more demanding and not such an oriented and occupational practice. This training effect may also be the deciding factor for the differences between swimmers from our study and Climstein et al. [49]. These authors evaluated one group of 15 individuals with Down syndrome and one group of 17 non-Down syndrome and most of the subjects were actively involved in the Special Olympics program. When compared to the present study, Down syndrome individuals from Climstein et al. [49] presented higher values for fat% (26.1 ± 5.3%).

#### *4.2. Physical Fitness*

Swimmers presented better results for all the test items, indicating their better physical fitness profile. This higher score was confirmed when examining the percentile scores (Table 2) as swimmers with Down syndrome are more fit than untrained counterparts, presenting higher levels of strength, balance and flexibility.

Despite the fact that physical fitness is an important contributor to health in adults and youth, less is known in persons with disabilities, such as Down syndrome [42]. In a review on physical fitness and physical activity in children and adolescents with Down syndrome, Pitetti et al. [37] point out that peak aerobic capacity (VO2peak) in both youth and adults with Down syndrome is reduced in comparison to their peers without disability and with other intellectual disabilities. The same authors highlight the fact that these persons can be responsive to aerobic endurance training, particularly with improvements in work capacity. Muscular strength is also lower in persons with Down syndrome when compared to their peers with normal development or with other intellectual disabilities [11,17,37,50–53]. According to Shields et al. [54], an improved strength in persons with Down syndrome has been associated with higher levels of physical activity. Muscular strength is a fundamental ability needed by persons with disabilities (with Down syndrome included) especially because: (i) their workplace activities typically emphasize physical rather than cognitive skills [54]; (ii) muscle weakness can impact their ability to perform everyday activities, including walking, eating, dressing and rising from a chair [17,51]; (iii) life expectancy is increasing for persons with Down syndrome [55] and maintenance of muscle strength is important to lead productive lives [56]; (iv) improving muscle strength may be important in controlling the high tendency for osteoporosis that persons with Down syndrome often demonstrate [50].

Hypotonia and hyper-flexibility, two characteristics of Down syndrome [57,58], have an impact on bone mass, muscular strength and power, gait and motor development [59]. All of these factors lead to the lower strength levels of persons with Down syndrome, but at the same time accent the importance of physical activity. For instance, Daly et al. [60] found that the strength differences between athletes with and without intellectual disabilities are in the range of 4–14% for male and 11–27% for female, being inferior for the athletes with intellectual disabilities. Despite this, much more specific data is needed on highperformance athletes with intellectual disability [23].

Swimming can be one of the activities that can make such a difference. According to Ylmaz et al. [21], aquatic exercises can be a good way of developing physical fitness and motor skill development for children with intellectual disabilities, as aquatics provide a very unique environment for these children. Perán et al. [61] stated that participating in competition is fundamental for individuals with Down syndrome. Although there is research on the effects of aquatic exercises on persons without disabilities, little has been done on persons with intellectual disabilities [62] and more specifically concerning competitive swimming for persons with Down syndrome, so the present study adds new evidence on what physical aspects may distinguish the Down syndrome subjects who are engaged in swimming programs from those who are not.

Comparing the results from the present study with those of Daly et al. [60] with highperformance athletes with intellectual disability, male swimmers with Down syndrome only scored better in the sit-and-reach test (cm) (39.0 ± 8.3 versus 34.0 ± 15.8 finalists and 35.7 ± 7.4 non-finalists) while low scores for the long jump (cm) and sit-ups were obtained

(123.3 ± 40.7 for long jump and 17.4 ± 3.8 for sit-ups versus 197.3 ± 26.3 and 23.0 ± 6.8 finalists and 181.9 ± 39.5 and 20.7 ± 4.7 non-finalists). Flexibility was slightly higher for female swimmers with Down syndrome (42.9 ± 4.4 versus 41.0 ± 8.9 finalists and 38.5 ± 8.0 non-finalists) but for the long jump and the sit-ups low scores can be observed (91.0 ± 10.6 and 16.0 ± 2.8 versus 154.6 ± 20.2 and 21.5 ± 6.1 finalists and 157.1 ± 24.9 and 18.2 ± 4.3 non-finalists).

As swimmers with Down syndrome from our study present higher levels of strength than untrained individuals with Down syndrome, we are led to conclude that swimmers have increased muscular hypertrophy, which in turn can reduce hypotonicity and balance dysfunctions and increase bone-mass related parameters [19]. Little is known about the effect of specific strength training in this population. Until recently, swimmers with Down syndrome rarely participated in specific dryland strength training. Van de Vliet et al. [23] studied elite athletes with intellectual disability and pointed out that good levels of fitness seem to be possible for these athletes, and it is likely that the training effect influenced the data. Likewise with athletes, Balic et al. [16] found that the active group of Special Olympians with Down syndrome exhibited significantly higher isometric strength than the sedentary group, also with Down syndrome. They suggested that long term exercise training may enhance physical fitness in individuals with Down syndrome.

Balance in people with Down syndrome is also a component of physical fitness that is usually inferior to the general population or individuals with intellectual disability without Down syndrome [18,63]. Muscle hypotonia may be responsible for balance problems that individuals with Down syndrome usually demonstrate [64]. The delay of maturation of the cerebellum and the relatively small size of cerebellum and brain stem in persons with Down syndrome may also be responsible for the disturbance of balance [65]. Despite these characteristics, individuals with Down syndrome seem capable of improving their balance through physical activity participation, and with this improve their well-being and the quality of life [18]. The swimmers from the current study presented good balance scores and were exceedingly better than the non-swimmers group.

As this was not a training study it is hard to conclude that differences in physical fitness are an outcome of the swimming training. Nevertheless, a study from Querido et al. [66] with six swimmers with Down syndrome evaluated for body composition and physical fitness in 2011 and 2014, found that in 3 years of training, swimmers with Down syndrome improved their physical fitness profile (especially strength) and their body composition characteristics.

In summary, it can be said that: (i) swimmers with Down syndrome present a healthier body composition than untrained individuals with Down syndrome, confirming the first hypothesis; (ii) swimmers with Down syndrome present higher physical fitness values than untrained individuals with Down syndrome, confirming the second hypothesis. This means that swimming educators, parents and/or institutions should see swimming as a sport that can take body composition and physical fitness of Down syndrome subjects to acceptable standards.

#### *4.3. Limitations and Suggestions for the Future*

We may point out several limitations to the present study. Larger sample sizes are needed (multi-center), if possible. Information about training characteristics should be more specific (volume, intensity, dry land training), and food intake characterized. Due to the lack of specific equations to estimate fat% and LBM for persons with Down syndrome, general equations were used. Although the participants had previous familiarizations with the tests, future studies should be carried with a test–retest procedure, to ensure that the physical fitness tests are completely understood. In the future it would also be important to perform an intervention program so it would be possible to conclude the effectiveness of swimming training on physical fitness, body composition and other complementary tests. **Author Contributions:** Conceptualization, A.Q., D.J.D. and R.J.F.; methodology, D.A., J.P.V.-B. and R.J.F.; formal analysis, R.C. and D.J.D.; investigation, A.Q., M.J.C., D.A. and A.R.S.; writing—original draft preparation, A.Q. and M.J.C.; writing—review and editing, D.J.D. and R.J.F.; funding acquisition, A.R.S., J.P.V.-B. and R.J.F. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was founded by the Portuguese Foundation for Science and Technology (FCT), under the grant SFRH/BD/78513/2011.

**Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki, and approved by the local ethics committee (under the code: CEFAD 19.2020).

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

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