The Ecuadorian Tapaculo, also known as El Oro Tapaculo, is an insectivorous bird endemic to a small range (~ 1100 km²) on the western slopes of the Andes in southwestern Ecuador, at an elevation of 850–1500 m (Hermes et al. in press). The species was only discovered in 1990 (Krabbe and Schulenberg 1997) and is so far not well studied. It occurs in the undergrowth of mature forests and is very reluctant to cross even small areas of open habitat (Krabbe and Schulenberg 1997). Being practically unable to fly longer distances, Tapaculos move around by hopping or walking (Reid et al. 2004, Castellón and Sieving 2006). The IUCN classifies the Ecuadorian Tapaculo as endangered. Global population size is estimated to range between 1900 and 4600 mature individuals (Hermes et al. in press). It is feared that the species’ requirements for high-quality habitat and presumed susceptibility to forest degradation and fragmentation have led to a severe population decline, which might still be ongoing (Hermes et al. in press).

The only protected site within the range of the Ecuadorian Tapaculo is the private Buenaventura reserve in the canton Piñas (3.655°S, 79.744°W), established in 1999 by the Ecuadorian NGO Fundación Jocotoco. This reserve covers an area of 2300 ha in an elevation of 400–1500 m (Figure 1). The predominant vegetation types within the reserve are secondary forests in various successional stages, which are separated by areas of abandoned pasture. Outside the reserve, deforestation is intense, with mostly only forest patches smaller than 100 ha remaining. The main causes for the logging of forests are intensification of agriculture and forest clearance for livestock. Natural forests mainly persist in areas which are not suitable for conversion into cattle pasture or cropland, like steep slopes or river banks (Best and Kessler 1995).

Figure 1.

Map of the study area in southwestern Ecuador. Forested areas are shaded grey, whereas white areas represent non-forested areas (mainly cow pastures). The Buenaventura reserve is circled by the dashed line. The bolt black line represents a highway cutting the reserve into a northern and a southern part, while minor roads are indicated by the thin lines.

Field work was carried out between December 2013 and May 2014 and between November 2014 and January 2015 in the Buenaventura reserve, and near Ñalacapa, about 5 km south of Buenaventura (Figure 1). The study area is located at the southern end of the Ecuadorian Tapaculo’s distribution range, covering about 5% of the total range. The size of forest fragments was assessed in ARCMAP 10.2 using satellite images of the area as a template (Hermes et al. in press). Forest areas ranged from about 15 ha to 900 ha. The northern and southern part of the study area was divided by a highway and a valley with an altitude of about 400 m.

For bird capturing, we used mist-nets and tape recordings of the song of male Ecuadorian Tapaculos as a decoy. If an individual approached the playback, observers herded it into the net. The Ecuadorian Tapaculo’s secretive behavior, very good vision and excellent maneuverability made the capturing very challenging. We captured 28 males. Birds were ringed with a standard aluminum ring and color-banded individually. Then, individuals were weighed and the lengths of tail, tarsus, wing, primary feathers and the first secondary feather were measured. From each individual, we took a blood sample from the brachial vein. To minimize stress, birds were handled within less than 10 minutes of capture and released unharmed to the same sites. Blood samples were stored in 99.8% ethanol and transferred into a -20 °C freezer.

To obtain an index for body size, we carried out a principal component analysis (PCA) for all the morphological variables that we recorded. As variables differed in their numerical range, they were z-standardized prior to the analysis. A second PCA for the variables wing length and length of the feathers P9 to S1 provided an index for the wing shape. For both PCAs, missing values (e.g., caused by feather molt) were replaced by the mean. Additionally, we quantified the body condition of each individual using the scaled mass index (Peig and Green 2009). Then, we tested for relationships between an individual’s morphology and the size of the forest where it was captured. To this aim, we carried out Kendall correlations between the fragment area and the first principal components of the PCAs for body size and wing shape as well as the body condition index. The statistical analysis was carried out in R 3.3.0 (R Development Core Team).

We extracted DNA from the blood samples and compiled a set of 10 species-specific microsatellite primers (for a description of the primer development see Suppl. material 1). Two individuals had to be excluded from the analysis due to failure of amplification during PCR in two loci. Then, we applied a Bayesian clustering method using the program STRUCTURE 2.3.4 (Pritchard et al. 2000) to explore the genetic population structure of the individuals (n = 26) caught in different fragments. This program uses a Markov chain Monte-Carlo (MCMC) approach to compute the probability of the sampled individuals belonging to a given number K of discrete genetic subpopulations. An admixture model with correlated allele frequencies was used. We pre-defined the parts north and south of the highway as distinct sampling locations (Figure 1). We set K from 1 to 8 and carried out 10 runs for each K, with 10⁶MCMC iterations and 500,000 burn-in iterations for each run. We determined the best value for K by analyzing the probability scores in the program STRUCTURE HARVESTER (Earl and vonHoldt 2012).

Landscape barriers disrupting or decreasing connectivity between individuals or populations can be quantified and qualified by the creation of a resistance map, which allocates a specific resistance value to each cell of the land cover grid according to the mobility of the species (Adriaensen et al. 2003). To evaluate landscape permeability for Ecuadorian Tapaculos in the study area and to assess least-cost paths (LCPs) between the individuals, we created a resistance map accounting for species-specific demands and landscape features. We produced a map (cell size 30 × 30 m) for the parameters ‘forest cover’, ‘elevation’, and ‘roads’ by assigning them different weights in the RASTER CALCULATOR tool of ARCMAP. Weights were determined on expert-opinion based on literature review and observations of the behavior of individuals. As Ecuadorian Tapaculos are reluctant to cross open habitats without forests (Krabbe and Schulenberg 1997), we assumed the costs for crossing open areas to be 100-fold higher than for dispersing through forests. Roads are known to represent a strong dispersal barrier for understory birds; even narrow, unpaved roads significantly reduce dispersal, while highways can even entirely block movement (Laurance et al. 2004). We created a buffer zone with a radius of 15 m around the roads in the study area in order to obtain a continuous reproduction of the roads on the 30 × 30 m resolution of the map. We assigned a 200-fold weight to the highway dissecting the northern and southern part of the study area, while the less frequented country lanes only obtained a 100-fold weight. For the resistance values of the elevation, we considered the mean altitude of territories to be the optimum for Ecuadorian Tapaculos, with dispersal costs being zero. There are hints that the Ecuadorian Tapaculo is sensitive to elevation. Presumably, the species has shifted its distribution range uphill within the last decades and now avoids areas of lower elevation (Hermes et al. in press). Therefore, higher or lower elevations were assigned costs equaling the difference in altitude to the mean altitude of territories. Merging of the layers generated the resistance map. The LINKAGE MAPPER tool of ARCMAP was then used to identify the LCP between territories by detecting cells with the lowest costs while avoiding cost-intensive cells.

To assess isolation by distance, we tested for relationships between the genetic and the geographic distances between the individuals. Geographic distance was expressed by Euclidian distance between territories as well as by LCP length and LCP cost. In GENALEX 6.5 (Peakall and Smouse 2012), we carried out Mantel tests with 999 permutations for each of the three parameters separately.

Using the R package HIERFSTAT, we tested whether genetic diversity has been reduced since the species was discovered in 1990. Accounting for the difference in sample size between the two groups, we compared allelic richness of the 26 samples we took in 2013–2015 with those of seven museum specimen collected in 1990–1991 in the same area (from the tissue collection at the Zoological Museum Copenhagen; sample numbers 125057, 125070, 125071, 125072, 126057, 126058 and 126167).

Additionally, we tested for a potential decline in the effective population size in the past with the program MSVAR 1.3 (Beaumont 1999, Storz and Beaumont 2002). This program was shown to be particularly powerful at detecting severe and ancient population declines (Girod et al. 2011), and can deal with small sample and population sizes (Beaumont 1999). Using multilocus microsatellite data, MSVAR applies a Bayesian coalescent-based hierarchical model to estimate the current population size as well as the ancestral population size, the time since a potential population decline or expansion started and the mutation rate of loci. With MCMC simulations, the program quantifies the likelihood of observing the allele frequencies in a sample, given a pre-defined demographic and mutational model. The simulation then produces probability estimates for the above-mentioned parameters by maximizing the likelihood of the observed data. We ran the model four times. To avoid a bias on the posterior distribution, each time we used different prior information assuming different scenarios of past population size change. We ran each chain with 10⁹ iterations and a thinning interval of 100,000. Thus, we obtained an output of 20,000 iterations for each run and dismissed the first 5,000 iterations as burn-in. The output was analyzed using the R packages CODA, BOA and LOCFIT. We checked the output chains for convergence using the Gelman-Rubin analysis (Gelman and Hill 2007) and calculated modes and 95% highest probability density (HPD) intervals for each parameter. Parts of the R script were taken from Paz-Vinas et al. 2013.

The first four principal components (PCs) of the PCA of body size accounted for 71.92% of the variance of 14 morphological variables (Table 1). We assigned loadings above a threshold of 0.35 to the respective PC. PC 1 described the length of the inner primary feathers P5, P3, P2 and P1, while PC 2 described the outer primary feathers P9, P8 and P7. PC 3 characterized the length of tarsus and the P4 feather, and PC 4 described the length of wing and tail. The PCA for the wing shape yielded similar results, with the first three PCs accounting for a total of 74.82% of the variance (Table 2). Here, PC 1 also represented the primary feathers P5, P3, P2 and P1. PC 2, accordingly, described the feathers P9, P8 and P7. PC 3 characterized the total wing length, as well as the length of the feathers P5 and S1. We concluded that, for both PCAs, individuals with high values for PC 1 have shorter inner primaries, i.e., a narrow wing, and individuals with high values for PC 2 have shorter outer primaries, i.e., a less pointed wing.

For both PCAs, we detected a marginally significant relationship between PC 1 and forest size (PCA of body size: P = 0.057; tau = -0.273; and PCA of wing shape: P = 0.063; tau = -0.267; Kendall correlation). None of the other PCs correlated with forest size (all P > 0.12; Kendall correlation). Similarly, there was no relationship between the body condition of birds and the size of the forest fragments (P = 0.76; tau = 0.044; Kendall correlation).

Allelic richness of the museum samples was 3.80 ± 0.75, while that of the recently collected samples was 3.59 ± 0.57. Therefore we concluded that genetic diversity has not changed within the last ~ 25 years. The STRUCTURE analysis showed no clear population substructure. K = 1 yielded the highest probability, indicating that most likely all samples belonged to the same population. However, error bars were highly overlapping amongst the estimates for different numbers of clusters (Figure 2).

Figure 2.

Mean ± SD of the log-likelihood for K = 1 to 8 distinct genetic populations. Strong support for K = 1 indicates that most likely all the samples stem from the same genetic group.

With the least-cost path analysis, we could identify a dispersal corridor for Ecuadorian Tapaculos across the study area, which circumvented the valley between the northern and southern part (Figure 3). Mantel tests indicated clear evidence for isolation by distance. Euclidian distance showed the strongest relationship to the genetic distance (R_xy = 0.418; P = 0.001), followed by LCP length (R_xy = 0.399; P = 0.001) and LCP cost (R_xy = 0.319; P = 0.001).

Figure 3.

Resistance map with least-cost path. The cost of movement is visualized by the color gradient from black to white, with black indicating higher costs and white lower costs. The bolt black lines show the least-cost paths between 26 Ecuadorian Tapaculo territories in the study area.

Modelling the population demography yielded evidence of a severe population decline in the past. All potential scale reduction factors were < 1.1, so we concluded that chains converged well (Gelman and Hill 2007). Modal values (and 95% HPD intervals) indicated a current effective population size of 770 individuals (150–2,820). Ancestral population size was 26,000 (5,275–171,400), suggesting an approximately 30-fold population decline. Time since the population started decreasing was estimated to about 7000 years (870–52,000) and the mutation rate to 1.42e^-4 (1.32e^-6–7.23e^-3). However, the large probability density intervals for the parameter estimates indicate a high level of uncertainty in the simulation.

Throughout the study area, we found no genetic structuring among Ecuadorian Tapaculos, indicating that a substantial amount of gene flow is still maintained. Even though the Ecuadorian Tapaculo is a bad disperser, migration between different forest fragments seems not to be blocked. We expected that habitat fragmentation in the range of the species produced genetically distinct populations in different forest fragments, as it was shown for a similar species (Woltmann et al. 2012). Our study area was disrupted by a valley and a highway, which we expected to act as barriers to dispersal. Besides, the distances between the different forest fragments following the least-cost path ranged between 10 m and 400 m and were thus partly larger than the mean dispersal distances observed for Ecuadorian Tapaculos (80 m; Hermes et al. in press). Nevertheless, we did not detect genetic structuring in distinct sub-populations. Similarly, a study analyzing genetic differentiation in White-ruffed Manakins (Corapipo altera) at a comparable scale than our study did not detect genetic structuring either, although the habitat was highly fragmented (Barnett et al. 2008). However, fragmentation does not necessarily lead to reduced gene flow and genetic differentiation (Galbusera et al. 2004). Even a species with strong dispersal limitation can show low levels of differentiation across a highly fragmented landscape (Callens et al. 2011). In the case of the Ecuadorian Tapaculo, even the high degree of forest fragmentation in the northern part of the study area is not sufficient to cause genetic structuring.

Gene flow across the study area is not impeded by barriers and Mantel-tests between genetic and geographic distances suggest isolation by distance (IBD). In theory, IBD can lead to considerable genetic differentiation even at small scales (Wright 1943). In the most extreme dispersal event observed in the Ecuadorian Tapaculo, an individual crossed 245 m of un-forested habitat to establish a territory in a remote forest fragment (Hermes et al. in press). The IBD detected here is therefore likely a consequence of the generally low dispersal capacities and mean migration distances of the species, which are reinforced by forest fragmentation.

Genetic diversity of Ecuadorian Tapaculos in the study area remained constant between 1990 and 2015. On a global scale however, population size and most likely also genetic diversity still decrease. Even though the result of the analysis of past population demography yielded a high level of uncertainty and should therefore be treated with caution, it gave evidence of a severe population decline. The Buenaventura reserve remains until now the only protected site within the distribution range of the Ecuadorian Tapaculo. Around the reserve, forests are heavily fragmented and degraded; mostly, patches are smaller than 100 ha and consist of young secondary stands. The constant level of genetic diversity in the study population over 25 years, which is presumably attributable to the establishment of the reserve, shows that a negative population trend can be stopped. However, in order to achieve a change for the better on the scale of the global population of Ecuadorian Tapaculos, it would be necessary to protect remaining forests throughout the entire distribution range, which, in view of ongoing deforestation, seems implausible. In general, Tapaculos are among the understory species most sensitive to fragmentation and are therefore seen as umbrella species for conservation planning (Willson et al. 1994, Reid et al. 2002, Castellón and Sieving 2007). The fact that we found population connectivity and a constant high level of genetic diversity in the Ecuadorian Tapaculo gives hope that other understory birds and dispersal-limited mammals in the area show similar population trends.

While several studies have already addressed the effects of forest fragmentation on the genetic structure of a population, its effects on individual morphology are far less examined. However, the degree of habitat fragmentation can cause different morphological adaptations in birds (Desrochers 2010). Increasing distance between forest fragments exerts a selective pressure for enhanced mobility and flight ability, i.e., more pointed wings, in order to enable migration between remote fragments (Fahrig 2003, Fiedler 2005, Desrochers 2010). While the studies of Fiedler (2005) and Desrochers (2010) were carried out at a much larger spatial scale (several 1000 km), we found effects on wing morphology already at a distance of less than 15 km.

Ecuadorian Tapaculos have short, round wings and only limited flight capacities; they do rarely fly distances longer than 3 m and move mainly by walking or hopping (Krabbe and Schulenberg 1997). In this study, we found wing shape to be related to forest size. Individuals in small patches had narrow wings, which can be seen as an adaptation to enhanced mobility and better flight capacity, which probably allowed colonization in the first place. In larger fragments, on the other hand, selection pressure for increased mobility is absent. Dispersing individuals do not face the necessity to cross habitat gaps before establishing their territories. For movement within large fragments, round wings enabling good maneuverability are advantageous. Alternatively, the differences in wing shape could be caused by different structural characteristics of the understory layer in relation to fragment size, with pointier wings facilitating flights in search for food. However, microhabitat structure of the understory in the forest fragments was assessed in a previous study (Hermes et al. in press), but had no influence on wing shape. Therefore, we conclude that the morphological differences are most likely caused by the fragmentation of forests and not by the degradation within forests.

Wing morphology is highly heritable in birds (Boag and van Noordwijk 1987). In this study, we detected effects of forest fragmentation on the morphology of the species already in a small population and at a small spatial scale. This implies that habitat fragmentation exerts considerable selective pressure favoring adaptive divergence of wing morphology. However, the morphological variability of the Ecuadorian Tapaculo gives evidence of the species’ potential to rapidly adapt to environmental changes. This potential can possibly mitigate the risk of local extinction of the Ecuadorian Tapaculo due to human-caused forest loss and fragmentation.

In the study population, phenotypic divergence in wing shape could arise in sympatry. Even though the individuals in the study area were not genetically differentiated at neutral markers, the morphological changes are likely promoted by the isolation by distance we discovered over the study area. Moreover, the differences in the level of forest fragmentation likely exert a selective pressure, which is strong enough to produce distinct phenotypes despite the homogenizing effect of gene flow. If the diverging selective pressures are high, a new beneficial allele can fix quickly and affect the genome (Crisci et al. 2016). In the case of the Ecuadorian Tapaculo, morphological adaptations have possibly arisen rapidly after the onset of intense forest fragmentation at the beginning of the 20^th century. Similar to our results, a study on Wedge-billed Woodcreepers (Glyphorynchus spirurus) found considerable morphological differences, although the level of gene flow was high (Milá et al. 2009). Generally, gene flow is assumed to constrain adaptive divergence by homogenizing the gene pool (Hendry and Taylor 2004, Räsänen and Hendry 2008). However, adaptive divergence caused by environmental differences can also constrain gene flow by the evolution of reproductive isolation over a few generations, i.e., ecological speciation (Schluter 2000, Carroll et al. 2007, Hendry et al. 2007). Moreover, in case that the adaptive divergence reduces the fitness of migrants between different environments, a negative feedback loop can be initiated: Reduced fitness of migrants reduces dispersal between the different environments, which in turn reduces gene flow. This can lead to a further increase in adaptive divergence and a further reduction in dispersal and gene flow (Räsänen and Hendry 2008). It is possible that the Ecuadorian Tapaculos are currently beginning a similar loop. Their flight apparatus is adapted to the specific level of habitat fragmentation and can be disadvantageous in different conditions. Therefore, the fitness of birds migrating to forests with a differing degree of fragmentation is likely reduced. Even though gene flow is not diminished at present, it is possible that it will decrease in future under ongoing diverging selection, forming genetically and morphologically distinct sub-populations. Nevertheless, this is not the only possible future scenario. Throughout the study area, considerable reforestation efforts have been made within the last 20 years. Forest regrowth increases habitat availability and homogeneity for the Ecuadorian Tapaculo. Thus, assuming far-reaching reforestation programs, the selective pressure for adaptations to enhanced mobility might disappear, reducing the divergence in wing morphology and increasing gene flow.