Neotropical seasonally dry forests (NSDF) are widely distributed across Latin America and the Caribbean. They possess important levels of species richness and endemism but few studies have assessed the diversity patterns and ecological relationships between the entire avifauna of these threatened forests. Thus, in order to analyse the macro-ecological patterns and the community structure of NSDF avifaunas, we generated species distribution models describing the current geographical distribution of 1,298 bird species inhabiting NSDF. We assessed species richness gradients in terms of distance from the Equator using both linear and polynomial regressions. Then, based on a matrix composed of the presence or absence of species in 563 quadrants, we performed cluster analyses (considering the Simpson dissimilarity index [βSIM] as a distance measure) to identify the main NSDF regions and describe the avifaunal affinities among them. For the identified groups, we estimated the dissimilarity values, using both an ANOSIM test and the βSIM index. Overall, we observed the lack of an equatorial peak for species diversity of NSDF avifauna in the latitudinal gradient and identified 12 avifaunistic groups. The βSIM index among the NSDF avifaunal groups ranged from 0.05-0.73, showing statistically significant differences (R = 0.894, p = 0.001) in species composition among them. Species shared between two or three NSDF groups comprised a higher proportion (∼38%) than those exclusive to each group (∼23%). Only 35 species were shared between the 12 groups. This information supports a separation of the NSDF avifauna into two major groups (northern and southern), as well as the idea of connections during recent geological time among the NSDF in southeastern South America (the so-called Pleistocene Arc Hypothesis). We provide a scientific framework to contextualise the importance of each NSDF nuclei in terms of their avifauna, supplying an ecological basis for future conservation decisions in order to protect their diversity. —Prieto-Torres, D.A., Rojas-Soto, O.R., Santiago-Alarcón, D., Bonaccorso, E. & Navarro-Sigüenza, A.G. (2019). Diversity, endemism, species turnover and relationships among avifauna of neotropical seasonally dry forests. Ardeola, 66: 257-277.
Introduction
Studies of bird assemblages have contributed significantly to the understanding of community ecology and biogeography, but not all bird communities have been equally well studied (Stotz et al., 1996; Herzog & Kessler, 2002; Weir & Hey, 2006; Rodríguez-Ferraro & Blake, 2008). In particular, the temporal and spatial diversification patterns for some bird communities associated with endangered ecosystems, such as the Neotropical seasonally dry forests (NSDF), remain only partially understood at both regional and continental scales (Weir & Hey, 2006; Wiens & Donoghue, 2004). Although NSDF are widely distributed and hold high levels of species richness and endemism, these forests have received relatively little attention from ecologists and conservationists (Miles et al., 2006; Sánchez-Azofeifa et al., 2013). Overall, both sample sizes and sampling effort for NSDF bird communities are often unsatisfactory (e.g., Ríos-Muñoz & Navarro-Sigüenza, 2012; Prieto-Torres et al., 2018b). For this reason, many researchers have supported the idea of maintaining an information network, such as DRY-FLOR (see www.dryflor.info/), to aid in the protection of NSDF by standardized protocols, combining ecological research, remote sensing and social sciences, that allow comparisons between different areas. Evidence of this fact is the growing interest in identifying NSDF regions that may have high conservation priority (Miles et al., 2006; Portillo-Quintero & Sánchez-Azofeifa, 2010; Albuquerque et al., 2012; Banda et al., 2016; Prieto-Torres et al., 2016, 2018a; Escribano-Ávila et al., 2017).
Considering that the financial resources available for biodiversity conservation are limited, information on species richness and endemism levels, including species turnover and the historical relationships among regions, have been identified as important metrics for estimating the conservation value of different areas (Myers et al., 2000; Gordon & Ornelas, 2000). Theoretically, regions or ecosystems containing the largest numbers of ecologically restricted species are the most sensitive to ecological disturbance and alteration; consequently, they must be considered as conservation priorities (Gordon & Ornelas, 2000; Duckworth & Altwegg, 2018). However, and despite there being numerous studies of NSDF plants (e.g., Pennington et al., 2000; 2006; Linares-Palomino et al., 2011; Banda et al., 2016), few studies have assessed the diversity patterns and historical relationships among the entire NSDF avifauna across the Americas (Ceballos, 1995; Stotz et al., 1996; Prieto-Torres et al., 2018b). Most of the information collected in NSDF avifaunal studies only applies to a small number of areas, which makes it difficult to formulate generalisations on biota dynamics due to the lack of replication. Therefore, many questions have yet to be answered regarding the biogeography and macro-ecological patterns of the NSDF avifauna.
Several studies provide contrasting perspectives of the historical distribution of NSDF, casting doubts on the value of biological comparisons among different NSDF areas in order to understand their biogeography, in particular when considering NSDF to be a single and widely distributed biogeographical unit (e.g., Becerra, 2005; Werneck et al., 2011; Côrtes et al., 2015; de Melo et al., 2016; Prieto-Torres et al., 2018b). This unified interpretation is important for both biogeographical inference and setting conservation priorities in NSDF because, based on the idea of connections during recent geological time, we could expect to find high species similarity, including low levels of endemism and species turnover, between the NSDF areas (Linares-Palomino et al., 2011). Nevertheless, recent avifaunal studies show high levels of endemism among NSDF in the Caribbean Islands, the Mesoamerican, and the South American NSDF as consequences of independent evolutionary processes and diversification patterns among them (e.g., Cracraft, 1985; Herzog & Kessler, 2002; Porzecanski & Cracraft, 2005; Ríos-Muñoz & Navarro-Sigüenza, 2012; Prieto-Torres et al., 2018b). In fact, some authors suggest that no single NSDF formation contains even a third of the total NSDF bird species pool (Ceballos, 1995; Stotz et al., 1996; Prieto-Torres et al., 2018b). These characteristics illustrate the possible distinctiveness of the species pools found within NSDF across the Americas, and the need to define the avifaunal relationships of such disjunct NSDF areas. In addition, even though it is known that evolutionary processes such as selection, genetic drift, and gene flow vary in strength and importance over latitude (Martin & Tewksbury, 2008), the consequences of this geographical variation on the NSDF biota are poorly understood (Gentry, 1995; Banda et al., 2016). It is thus important to identify the areas of greatest species diversity and endemism –i.e., unique biodiversity areas–providing a framework to define the conservation significance of each separate NSDF region, as has been done for other threatened ecosystems, for example the South American Atlantic forest (Cardoso da Silva et al., 2004).
In this study, we applied a macro-ecological approach to analyse the spatial variation of the bird assemblages inhabiting NSDF, partly employing the methodology used by Banda et al. (2016) for woody plants. We aimed to: (i) quantify and map the current avian diversity in NSDF, (ii) assess species richness patterns in terms of distance from the Equator (i.e. latitudinal gradient); and (iii) characterise NSDF areas in terms of species composition (i.e. patterns of species richness and endemism, as well as species turnover among regions). Our results offer a better understanding of the macro-ecological distribution patterns of NSDF avifauna, which will aid future ecological studies and conservation efforts in these highly threatened forests. This type of information is particularly critical in ecosystems such as the NSDF, where strong anthropogenic disturbances (e.g., agriculture, cattle ranching, mining, and urbanization) are dramatically decreasing biodiversity via loss of original vegetation, reduction of species ranges and the imposition of low connectivity or even complete isolation among populations (Miles et al., 2006; Portillo-Quintero & Sánchez-Azofeifa, 2010; Sánchez-Azofeifa et al., 2005).
Methods
Study area
We defined NSDF as ecosystems typically dominated (>50%) by deciduous trees, which are present in frost-free areas, with a mean annual temperature > 25°C, a total annual precipitation of 700-2,000mm and at least three or more dry months (precipitation < 100mm) per year (Murphy & Lugo, 1986; Pennington et al., 2000; Sánchez-Azofeifa et al., 2005; 2013). The vegetation is heterogeneous, including formations ranging from tall forests to cactus-dominated scrub, but mostly dominated by semi-deciduous to deciduous trees (Murphy & Lugo, 1986; Pennington et al., 2000, 2006; Sánchez-Azofeifa et al., 2005). This endangered ecosystem, which encompasses 42 ecoregions according to Olson et al. (2001), is discontinuously distributed in 18 countries across Meso- and South America (Figure 1). In Mesoamerica, NSDF are located in Mexico, Guatemala, El Salvador, Honduras, Nicaragua, Costa Rica and Panama. In South America, NSDF are distributed in Venezuela, Colombia, Ecuador, Peru, Bolivia, Argentina, Paraguay and Brazil. Important NSDF fragments are also located in the Caribbean islands in Cuba, the Dominican Republic and Haiti.
Species occurrence records and distribution models
Analyses were based on records of 1,298 terrestrial native bird species inhabiting NSDF, which were obtained from ornithological collections ( see Supplementary Material, appendix 1 (Rp_1_Prieto Torres et al_ Supplementary Material.pdf)) and online databases (i.e., the Global Biodiversity Information Facility [GBIF], eBird and the SiB Colombia). The complete checklist of bird species inhabiting NSDF is provided in Prieto-Torres et al. (2018b). For the final species list, all names follow those proposed by Gill and Donsker (2015) for Mesoamerica, as well as the South American Classification Committee (Remsen Jr. et al., 2017) and the Clements Checklist (Clements et al., 2015) for the birds of South America. In addition, the conservation status of each species follows IUCN (2014).
Fig. 1.
Spatial patterns of species richness and endemism for 1,298 bird taxa throughout the Neotropical seasonally dry forest (NSDF) distribution. Geographical species richness patterns across NSDFs were estimated in two ways: (a) all species vs. (b) only NSDF-restricted species. Acronyms in (c) correspond to countries that have expanses of NSDF: Argentina (Arg), Bolivia (Bol), Brazil (Bra), Colombia (Col), Costa Rica (CR), Cuba (Cu), Dominican Republic (Dom), Ecuador (Ecu), El Salvador (ES), Guatemala (Gua), Haiti (Hai), Honduras (Hon), Mexico (Mex), Nicaragua (Nic), Panama (Pan), Paraguay (Par), Peru (Per) and Venezuela (Ven).
[Patrones espaciales de riqueza de especies y endemismos para 1.298 taxones de aves a lo largo de la distribución de los Bosques Secos Estacionales del Neotrópico (BSEN). Los patrones geográficos de riqueza de especies en el BSEN se estimaron considerando dos enfoques: todas las especies (a) vs. “solo especies restringidas” (b). Las siglas en (c) corresponden a países con extensiones de NSDF: Argentina (Arg), Bolivia (Bol), Brasil (Bra), Colombia (Col), Costa Rica (CR), Cuba (Cu), República Dominicana (Dom), Ecuador (Ecuador), El Salvador (ES), Guatemala (Gua), Haití (Hai), Honduras (Hon), México (Mex), Nicaragua (Nic), Panamá (Pan), Paraguay (Par), Perú (Per), y Venezuela (Ven).]
For most species, few observations and/or specimens are available, and where they exist data are generally biased by site accessibility (Peterson, 2001; Peterson et al., 2018). Thus, such techniques as species distribution models (SDMs) have been developed to obtain more accurate species distribution maps, based on identifying environmentally suitable areas. Using SDMs has the advantage of minimising the spatial biases inherent to species distribution information, filling in gaps in poorly known and/or non-surveyed areas (Peterson, 2001; Soberón & Peterson, 2005; Peterson et al., 2018). Thus, SDMs have become a widely used tool in ecology, evolution, conservation and management (e.g., Soberón & Peterson, 2005; Ortega-Andrade et al., 2015; Prieto-Torres & Pinilla-Buitrago, 2017; Peterson et al., 2018).
The SDMs were obtained with MaxEnt 3.3.3 (Phillips et al., 2006), which uses the maximum entropy principle to calculate the most likely distribution of focal species as a function of occurrence localities and environmental variables (Elith et al., 2011). To characterise the potential distribution –based on ecological niche modelling–, we downloaded interpolated climate data (30”-reso-lution: ∼1km2 cell size) from the WorldClim project 1.4 (Hijmans et al., 2005). All models were run with no extrapolation to avoid artificial projections of extreme values of ecological variables (Elith et al., 2011; Owens et al., 2013). Other MaxEnt parameters were set to default. In addition, for each species we used a geographical clip based on the intersection of Terrestrial Ecoregions (Olson et al., 2001) and the Biogeographical Provinces of the Neotropic (Morrone, 2014) in order to create an area for model calibration by species (or M sensu BAM diagram; see Soberón & Peterson, 2005; Barve et al., 2011). Such considerations were based on the assumption that these regions may define the accessible historical area and specific restriction region for each species.
We converted the obtained logistic values of suitability (continuous probability from 0 to 1; Phillips et al., 2006) into a binary presence/absence map by setting the “tenth percentile training presence” as the decision threshold. We decided to use this threshold because it allows reducing commission errors (i.e., overprediction of areas) in our final binary maps, recovering more conservative species distributional ranges (Liu et al., 2013). The performance of the MaxEnt models for species that had between five and 20 records (n = 56 spp.: 4.31% of database) was developed using all presence data and assessed with a Jackknife test (Pearson et al., 2007). For species with more than 20 records (n = 1,242 spp.: 95.69%), performance was evaluated by calculating the commission and omission error values and the Partial-ROC curve test (Peterson et al., 2008). For this last case, it is important to note that SDMs were generated using a random sampling of 90% of the locality records for model training and the remaining 10% for internal model evaluation (i.e. testing data).
Finally, based on the obtained individual SDMs, we determined a species ecosystem specificity value in order to define those NSDF-restricted species. This step was based on two approaches (see Prieto-Torres et al. [2018b] for more details): (1) calculating the degree of coverage of the geographical distribution for each species across the Neotropical ecosystems; and (2) using the index of restriction (IR) proposed by Sánchez-González and Navarro-Sigüenza (2009), which corresponds to a modification of published endemicity indices (Crisp et al., 2001; Linder, 2001). NSDF-restricted species were defined ( Supplementary Material, appendix 2 (Rp_1_Prieto Torres et al_ Supplementary Material.pdf)) herein as those that had at least 33% of their distribution within the NSDF and an IR ≥ 0.33.
Richness, endemism and species turnover amongst NSDF
We summed all species binary maps in order to obtain the potential species richness patterns across NSDF considering two approaches: all species vs. NSDF-restricted only. In order to highlight geographical differences throughout the Neotropics, these maps employ a colour key to indicate species richness levels. In addition, given that we obtained a species list for each grid cell, we performed both linear and polynomial regressions between the absolute latitude, the total number of species and NSDF-restricted species. These analyses allowed us to evaluate the richness gradients (i.e., all species, NSDF-restricted) in terms of distance to the Equator (Banda et al., 2016).
Subsequently, we divided the study area into 563 grid cells (1°× 1°; Figure 1) and constructed two binary species matrices (i.e., all species vs. NSDF-restricted only), coding presence as “1” and absence as “0” for each site based on the individual SDM maps obtained. We used these matrices to identify the main NSDF regions and describe the avifaunal affinities between them. However, when we compared results from the all species and NSDF-restricted analyses, we did not observe differences in the major NSDF regions identified (see below). Thus, the results below are based only on a strict consensus dendrogram of analyses using all-species information. We used this approach because it allows assessing and comparing the distributional patterns of NSDF avifauna considering different but parallel histories, providing a better understanding of the historical relationships between geographical areas (Ricklefs, 1987; Wiens & Donoghue, 2004; Weir & Hey, 2006).
To identify the main NSDF regions and describe the avifaunal affinities among them, we first performed ordination and classification analyses of sites using the Simpson dissimilarity (or βSIM) index as a distance measure and the Unweighted Pair-Group Method with Arithmetic mean (UPGMA) as the linkage method. We decided to use the βSIM to calculate pairwise avifaunal distances because this index is less affected by variations in species richness (Baselga, 2010; Colwell et al., 2012). To avoid the effects of the order of the sites in the matrix (especially when pairwise distance values are equal), we used the Recluster package (Dapporto et al., 2016) performing 10,000 random site re-orderings. All analyses were run in the R software (R Core Team, 2018).
We then described the relationships amongst the NSDF identified groups based on a UPGMA hierarchical clustering using the Pvclust package (Suzuki & Shimodaira, 2006). This allowed us to assess the support for each node in the dendogram, calculating probability values (p-values) based on 1,000 bootstrap resamplings. For this classification, we pooled the species lists for each grid cell into a single list and conducted clustering analyses on a species (rows) × NSDF group (columns) matrix. We calculated expected species accumulation curves (to assess how well the bird assemblage is captured in our dataset) for each NSDF group using a sample-based rarefaction method (Colwell et al., 2012) from the species accumulation (specaccum) function in the vegan library (Oksanen et al., 2018). Finally, in order to determine the degree of differences among the bird assemblages identified, we estimated the dissimilarity values (including the number of shared and exclusive species) amongst the NSDF regions based on the βSIM dissimilarity index and the ANOSIM test (i.e., Analysis of similarities; Clarke, 1993) in the R software. This last step allowed us to test statistically whether there is a significant difference between two or more groups of the identified NSDF region.
Results
Species distribution modelling and species richness patterns
Of 1,298 bird species –belonging to 24 orders, 78 families and 511 genera– inhabiting NSDF ( Supplementary material, appendix 2 (Rp_1_Prieto Torres et al_ Supplementary Material.pdf)), we found that ∼21% (n = 275) are widely distributed across the Neotropical region, while ∼55% (n = 707) are geographically restricted to South America, ∼20% (n = 260) to Mesoamerica, and only ∼4% (n = 56) are restricted to Caribbean Islands. For these species, we observed that ∼25% (n = 323 spp.) have at least 50% of their distribution within NSDF, while ∼42% (n = 545) of species have between 25-50% of their distribution within NSDF.
Other ∼33% (n = 430) species have ranges that overlap by 10-25% with NSDF areas. Likewise, the species ecosystem specificity values showed that only 13.7% (n = 178) of species are present in only one (i.e., NSDF) or two ecosystems (IR > 0.5), whereas most (45.4%; n = 589 spp.) are distributed in one to three ecosystems (IR ≥ 0.33). An additional 40.9% (n = 531) of species tended to be distributed in more than three ecosystems (IR < 0.25). Based on these patterns, we determined that ∼43% (n = 557) of species were highly associated with, and/or restricted to, NSDF.
From a national boundaries perspective, we found that the countries with the highest species richness were Brazil (n = 630 spp.), Peru (n = 549), Bolivia (n = 525) and Colombia (n = 493). The number of NSDF-restricted species was also higher in Brazil (n = 195) and Peru (n = 171), as well as in Mexico (n = 172) and Argentina (n = 146). The lowest values for both species richness and endemism were reported in Haiti (n = 87 spp.: 9 NSDF-restricted), the Dominican Republic (n = 87: 10), Cuba (n = 94: 34) and Panama (n = 336: 59). According to Figure 1, we observed that species richness patterns in the NSDF avifauna tended to increase in some areas considered boundaries (i.e., ecotones) with other species-rich environments (e.g., montane forests, tropical rainforests), whereas the lowest species richness values were observed in ecotones with other dry ecosystems, such as savannahs (the Llanos in Venezuela and Colombia) and the Cerrado (in northeastern Brazil). Although 51.1% (n = 663 spp.: including 181 NSDF-restricted spp.) and 31.1% (n = 404: 94) of the NSDF avifauna is shared with the Chaco and savannas, respectively, the highest proportion (85.7%; n = 1,112: 410) is shared with ecosystems that are altitudinally higher than NSDF.
Fig. 2.
Fitted line plots for linear (grey line) and polynomial (black line) regression of absolute latitude (i.e., distance from the Equator) versus total number of species for the avifauna associated with Neotropical seasonally dry forests (NSDF).
[Gráfico de dispersión de puntos y líneas ajustadas para los valores de regresión lineal (línea gris oscura) y polinomial (línea negra) entre los valores de latitud absoluta (es decir, distancia desde el ecuador) versus el número total de especies para la avifauna asociada a los Bosques Secos Estacionales del Neotrópico (BSEN).]
Interestingly, both line plots for linear and polynomial regression models showed a lack of an equatorial peak in the latitudinal pattern (Figure 2). Overall, both regression models showed that species richness increases directly and significantly (p < 0.05) with distance from the Equator, explaining between 13-15% of the variation in richness patterns. In fact, the southernmost NSDF in Bolivia, Argentina, Paraguay and Brazil (which reach the Tropic of Capricorn), as well as the northernmost Mexican NSDF (that reach the Tropic of Cancer) have higher species numbers than most of Caatinga and the Pacific Equatorial areas (close to the Equator; see Figure 1). These results suggest, as observed recently for NSDF plants (Banda et al., 2016), the existence of a “reverse latitudinal gradient” throughout the distribution of the NSDF biota.
Relationships and species turnover amongst NSDF
The average number of species per NSDF quadrats (i.e., 1°× 1°grid cells) was 259 (SD = 91, median = 276; range: 50-448). Based on the classification analyses of the 563 grid cells, we recognised 12 main groups from the species pool ( Supplementary Material, appendix 3 (Rp_1_Prieto Torres et al_ Supplementary Material.pdf)) most of them encompassing more than one country (Figure 1). Species accumulation curves (Figure 3) reached an asymptote for the 12 NSDF groups, indicating that very few new species remain to be recorded in all areas. Thus, the avifaunistic inventories are sufficiently complete for these areas. Tables 1 and 2 show the detailed description of taxonomic richness (including the percentage of exclusive and shared species) for each NSDF group, as well as the dissimilarity indexes values among them. Overall, the βSIM dissimilarity index among these 12 avifaunistic groups ranged from 0.05-0.73 (Table 2), showing statistically significant differences (R = 0.894, p = 0.001) among the avifaunal composition of NSDFs.
Despite the clear distinction between the compositions of avian assemblages among different NSDF groups (Table 2, Figure 4), we observed that species shared between two and three NSDF groups represented a higher proportion (∼38%; n = 491 spp.) than those restricted or exclusive to each NSDF group (∼23%; n = 294 spp.). The proportion of species shared among at least half (six) of the groups was ∼23% (n = 303 spp.), whereas only ∼20% (n = 259 spp.) were shared among seven or more groups. We found only 35 (2.69%) species that were shared among all 12 NSDF regions ( Supplementary Material, appendices 2 (Rp_1_Prieto Torres et al_ Supplementary Material.pdf)). Similar patterns were observed for the NSDF-restricted species, with ∼44% (n = 244) of species shared among two and three NSDF groups and ∼38% (n = 210 spp.) restricted/exclusive to one NSDF group. Approximately 7% (n = 38) of NSDF-restricted species are shared by at least six NSDF groups and only two species are distributed among the 12 groups.
Table 1
Description of avifaunistic groups identified for Neotropical seasonally dry forests (NSDF): number of sites (grid cells) and taxonomic diversity (including number of species, the percentage of widely-distributed species and those exclusive to the NSDF group, as well as the number of threatened species [including those in the Data Deficient, Vulnerable, Endangered and Critically Endangered categories]).
[Descripción de los grupos avifaunisticos identificados para los Bosques Secos Estacionales del Neotrópico (BSEN): número de sitios (celdas) y diversidad taxonómica (incluido el número de especies, el porcentaje de especies ampliamente distribuidas y aquellas exclusivas dentro del grupo de BSEN), así como el número de especies amenazadas [considerando las categorías: Datos Deficientes, Vulnerable, Amenazada y Críticamente Amenazadas]).]
Table 2
Dissimilarity values based on Simpson index (below diagonal) and shared species (above diagonal) of bird among Neotropical seasonally dry forests (NSDF) groups. Deeper grey shade indicates greater numbers of shared species, corresponding to line widths in Figure 4.
[Valores de disimilitud, basados en el índice de Simpson (debajo de la diagonal), y número de especies de aves compartidas (arriba de la diagonal) entre los grupos de Bosques Secos Estacionales del Neotrópico (BSEN). Tonos grises oscuros indica los más altos valores de especies compartidas entre grupos, los cuales son correspondientes a los anchos de línea representados en la Figura 4.]
The UPGMA hierarchical clustering showed that relationships amongst the 12 NSDF groups involved two well-resolved higher-level clusters (Figure 4). The first major group, corresponding to the northern cluster, involved the NSDFs located in three clearly differentiated and well-supported regions: (1) the Caribbean islands, (2) northwestern Mexico and (3) Central America (from southeastern Mexico to Panama). The second higher-level group, corresponding to the southern cluster, comprised areas located in South America, which were subsequently divided in two well-supported sub-clusters. One sub-cluster comprised the grid cells of northern South America (including the Caribbean coast from Colombia and Venezuela, as well as the inter-Andean valleys from Colombia) and those grid cells that included the NSDFs of central-western South America (from western Ecuador to northwestern Peru). The second sub-cluster is formed by the forests located throughout southeastern South America, which is further divided in other two well-supported groups: one contains the NSDFs located at higher altitudes (i.e. Sub-Andean Piedmont; from Apurímac-Mantaro and Tarapoto-Quillabamba forests to western Bolivia), and the other is formed by the Chiquitano forests-Central Brazil, the Misiones province, and the Brazilian Caatinga (Figure 4).
Fig. 3.
Species accumulation curves for each avifaunistic group calculated using a sample-based rarefaction method. Gray shade represents the 95% confidence intervals for the best fit line estimate of species numbers.
[Curvas de acumulación de especies para cada grupo avifaunístico calculadas utilizando el método de rarefacción basado en muestreos. El tono gris representa los intervalos de confianza del 95% para la estimación de línea de mejor ajuste de números de especies.]
Discussion
Community structure of the NSDF avifauna: history meets ecology
Our study provides a clear picture of the uniqueness of the NSDF regional avian groups across the Neotropics, supporting the idea of independent evolutionary histories for bird assemblages at different NSDF nuclei despite their environmental similarities (as proposed by Porzecanski & Cracraft, 2005; Ríos-Muñoz & Navarro-Sigüenza, 2012; Prieto-Torres et al., 2018b). In fact, the identified spatial richness and endemism patterns were similar to those found for other NSDF taxa (e.g., woody plants, herpetofauna, mammals) either at regional or continental scales (Ceballos, 1995; Linares-Palomino et al., 2011; Banda et al., 2016). This concordance among biogeographic patterns of NSDF biota supports the existence of unique associations (with high levels of geographical restriction and faunal turnover) across the NSDF distribution (Becerra, 2005; Côrtes et al., 2015; de Melo et al., 2016; Prieto-Torres et al., 2018b), leading to different time-events of adaptation to dry environments –in the case of birds– within northern and southern ranges (see Barker et al., 2015). These results are likely since birds are following historical patterns of NSDF plant species distributions (see Banda et al., 2016; Prieto-Torres et al., 2018b).
Fig. 4.
Geographical patterns for the hierarchical classification and ecological relationships of the twelve faunistic groups identified based on bird species associated with Neotropical seasonally dry forests (NSDF). Classification of groups was based on the UPGMA clustering of 563 grid cells using the Simpson dissimilarity index as a measure of distance ( see supplementary material appendix 3 (Rp_1_Prieto Torres et al_ Supplementary Material.pdf)). Size of the circles in the map is proportional to the number of species per group (Table 1), while the number of species shared among areas (Table 2) is described by line widths. Black circles in the dendrogram correspond to probability values (p-value ≥ 80%; for each node based on 1,000 bootstrap repetitions). The higher-level clusters are indicated by the rectangles.
[Patrones geográficos para la clasificación jerárquica de los doce grupos faunísticos identificados considerando las especies de aves asociadas a los Bosques Secos Estacionales del Neotrópico (BSEN). La clasificación de los grupos se basó en el agrupamiento UPGMA de 563 celdas utilizando el índice de disimilitud de Simpson como una medida de la distancia (véase Material Suplementario, apéndice 3). El tamaño de los círculos en el mapa es proporcional al número de especies por grupo (Tabla 1), mientras que el número de especies compartidas entre áreas (Tabla 2) se describe por el ancho de línea. Los círculos negros en el dendrograma corresponden a valores de probabilidad (p ≥ 80%; con base a las 1.000 repeticiones de bootstrap). Los grupos de nivel superior se indican con los rectángulos.]
Our data confirmed that the degree of isolation and historical colonisation patterns play an important role in shaping bird assemblages in NSDF. We observed that few species are widespread and shared across diverse NSDF groups. Most species shared among at least half of the groups involved birds considered as widespread ecological generalists, such as the Black Vulture Cathartes aura, Burrowing Owl Athene cunicularia and Tropical King-bird Tyrannus melancholicus (Stotz et al., 1996; Gill & Donsker, 2015). This low number of shared species among more than three NSDF groups strongly argues against the idea of connections during recent geological time throughout the Neotropics for a widespread NSDF formation (Pennington et al., 2009; Linares-Palomino et al., 2011; Prieto-Torres et al., 2019). Hence, based on current evidence, we rather suggest a mixed evolutionary history across the NSDF distribution, with ecological similarities among some NSDF nuclei arising from convergence and dispersal limitations. As suggested by Werneck et al. (2011), the climatic regimes during the LGM were probably too dry and cold to support large tracts of NSDF –connecting all regions or favouring the long-distance dispersal of taxa. This idea is supported by the important geographical and ecological barriers circumscribing NSDF groups, such as the Leading edge of the Caribbean plate, the Tehuantepec Isthmus, the Polochic-Motagua fault, the Nicaraguan Depression, the Chocó forest, the Amazon basin and the Andean Cordillera (Porzecanski & Cracraft, 2005; Ríos-Muñoz & Navarro-Sigüenza, 2012; Banda et al., 2016; Prieto-Torres et al., 2018b).
Although there is no support for historical interconnection of NSDFs across their entire geographical distribution, it is important to note that avifaunal affinities found between two NSDF regions in southeastern South America (Figure 4) support the so-called Pleistocene Arc Hypothesis as originally proposed by Prado and Gibbs (1993). In this respect, our results and those obtained for plant taxa (Banda et al., 2016) suggest that these regions were probably once interconnected within recent time (i.e., the Late Pleistocene; Werneck et al., 2011). As a consequence, the long-distance dispersal hypothesis of NSDF in southeastern South America, proposed by Mayle (2004), remains a feasible scenario that requires further study.
Geographical distribution of species richness
Compared to other Neotropical ecosystems such as montane forests and tropical rainforests, species richness and endemism in the NSDF is low (Gordon & Ornelas, 2000; Porzecanski & Cracraft, 2005; Rodríguez-Ferraro & Blake, 2008; Ríos-Muñoz & Navarro-Sigüenza, 2012). This avifauna is composed of a mixture of NSDF-restricted endemics, lowland taxa and species that are more common at higher elevations (Stotz et al., 1996). In fact, most of the bird species (∼57%, n = 741) inhabiting NSDF are primarily associated with other ecosystems (Stotz et al., 1996). However, NSDF bird assemblages also have a unique composition as a result of a diverse array of ecological conditions across the surrounding ecosystems (e.g., montane cloud forests, rainforests, savannas, and dunes) and the altitudinal gradient involved across the forests distribution (e.g., García-Trejo & Navarro-Sigüenza, 2004; Ríos-Muñoz & Navarro-Sigüenza, 2012). This scenario of mixed community structure (e.g., the five Andean avifaunistic groups in Figure 4) reflects the great heterogeneity of regions and confirms the existence of complex transition zones (i.e., ecotones) among the ecosystems surrounding NSDFs (Prieto-Torres & Rojas-Soto, 2016; Dexter et al., 2018).
Evidently, avian species richness and endemism patterns are also tightly associated with floristic composition and seasonality of precipitation across the distribution of NSDFs (Olmos et al., 2005; Martin & Tewksbury, 2008; Albuquerque et al., 2012). For instance, during the rainy season the phenology of NSDF resembles that of montane and rainforests, which favours the movements of animal species into NSDF, increasing richness values in bordering areas (Stoner & Timm, 2004; 2011). Therefore, ecotones frequently represent heterogeneous environments that are considered reservoirs of biological diversity (Búrquez & Marínez-Yrízar, 2010). Likewise, the notable “reverse latitudinal gradient” of species richness –originally suggested by Gentry (1995) for woody plants– could also be related to differences in ecological conditions along the gradient, which enhance the coexistence of highly variable climatic areas with more stable ecoclimatic regions (e.g., Sánchez-González & Navarro-Sigüenza, 2009). In fact, most NSDF regions differ greatly –along their latitudinal gradient– in term of surface area, climate, topography and geological history. This complex geological history, with extensive environmental changes in the Neotropics, may have promoted the local evolution or persistence of high species diversity and the retention of ancient lineages (see Pearson & Dawson, 2003; Bush et al., 2004); consequently, producing a higher concentration of species in certain places that are currently reflected in the different NSDF groups or nuclei (Rodríguez-Ferraro & Blake, 2008; Ríos-Muñoz & Navarro-Sigüenza, 2012; Banda et al., 2016; Prieto-Torres et al., 2018b). For instance, larger areas (such as western Mexico and Misiones Province) that are surrounded by other ecosystems, harboured more species than isolated areas such as the Caribbean islands and the northern Peru valleys. Likewise, this pattern probably reflects different post-glacial sources for recolonising formerly glaciated regions at low latitudes, as well as environmental barriers that limit the ranges of species from both northern and southern extremities to the equator (e.g., Qian et al., 2009).
Alternatively, results can be explained by an integrated time and area-size factor that involved low niche differentiation and strong dispersal limitation of organisms from the other NSDF nuclei (Pennington et al., 2009; Banda et al., 2016). Thus, high diversity of NSDF-restricted species is distributed as patches along NSDF distribution (Figure 1). This last idea, suggests ecological and geographical stability for several NSDF patches (i.e. refuges), as well as older clades, favouring the highest values of species diversity and endemism (see Barker et al., 2015). For instance, although the age of Mesoamerican NSDF is not known, Becerra (2005) suggested a Miocene-Pliocene age for northwestern Mexico forest based on dated phylogenies of the genus Bursera. Thus, it would be important to incorporate more molecular phylogeographic information and paleo-distribution models (e.g., Côrtes et al., 2015; de Melo et al., 2016), dating divergence times among NSDF endemic lineages and their close relatives. We expect oldest divergence times and stronger phylogeographic structure for these refuge areas, which would be associated with the latitudinal patterns observed here: higher species richness values for both extremes of distribution than for Equatorial areas.
Conservation implications
Our results show strong avifaunal differentiation between the 12 identified NSDF regions, the high levels of geographically restricted species (up to 23%) and the high species turnover (beta diversity) among most groups supporting their taxonomic uniqueness (Table 2). Thus, failure to protect each of the identified NSDF groups would result in major losses of unique species diversity and evolutionary history. An example of this scenario are the Andean dry forests (divided into five avifaunistic groups and with 44 threatened species; Table 1), where current protection falls short of expectations (i.e., Aichi target; UNEP, 2010), in particular given current levels of habitat transformation and destruction by human activities (Miles et al., 2006; Lessmann et al., 2014; Banda et al., 2016).
Considering that NSDF is one of most threatened ecosystems globally, we argue that the IUCN red list shows a worrying (and maybe outdated) perspective on the conservation status of the birds that inhabit this ecosystem (Table 1; Supplementary Material, appendix 2 (Rp_1_Prieto Torres et al_ Supplementary Material.pdf)). We note that only 9.6% of species are considered threatened (these including 87 NSDF-restricted species), while 4.8% are listed as “Near Threatened” (NT). Currently, most species (85.6%) are considered to be of ‘Least Concern’ (LC) by the IUCN. In fact, considering that current protected areas cover only 8.4% of NSDF and represent on average ∼10% of the total distribution of the avifauna inhabiting these forests (Prieto-Torres et al., 2018a), it is clear that the level of protection for unique NSDF biotas is woefully inadequate. Much biodiversity still remains unprotected. Thus, studies describing basic diversity patterns are urgently required for the Neotropics because they provide baseline information that is relevant for both in-depth ecological studies on ecosystem dynamics and conservation planning (Rodríguez-Ferraro & Blake, 2008; Duckworth & Altwegg, 2018). From this perspective, contextualizing the avifaunistic relevance of each separate NSDF nucleus, we hope that our results become a basis for future ecological studies and conservation decisions that take into account continental-level faunistic patterns, in order to protect the maximum possible diversity of these highly threatened forests.
Acknowledgements.
We appreciate the efforts of museums ( see Supplementary Material, appendix 1 (Rp_1_Prieto Torres et al_ Supplementary Material.pdf)) and their curators, as well as of the field collectors and institutions that made our study possible. DAP-T extends his gratitude to the Universidad Nacional Autónoma de México (Dirección General de Asuntos del Personal Académico, DGAPA-UNAM, México) for a Postdoctoral scholarship. The Rufford Foundation (DAP-T 16017-1; DAP-T 20284-2), Idea Wild (DAP-T), Consejo de Desarrollo Científico, Humanístico y Tecnológico at Universidad del Zulia (CONDES projects CC-0247-13 and CC-0497-16; DAP-T), the CONACyT project 152060 (AGNS and ORRS), and the CONABIO project JM071 (AGNS) provided financial and logistical support. J.C. Catarí, P.J. Campos and Z. Pérez-Durán (Bolivia), as well as J.A. Delgado (Peru) helped during fieldwork. A. Gordillo, C. MotaVargas and I. Perozo helped in georeferencing bird locality data. We appreciate the comments and technical support provided by Kyle Dexter, Karina Banda, Toby Pennington and Marcos Ortiz-Ramírez during the analyses performed to assess richness, endemism and species turnover. The authors confirm that co-author Elisa Bonaccorso is an Ardeola Editorial Board member and that this does not alter the authors' adherence to the journal's Editorial policies and criteria.
Author contributions.
DAP-T designed the study. DAP-T, ORS, EAB, AGN-S performed the species' selection. DAP-T and AGN-S conducted data collection. DAP-T performed the ecological niche and species distribution modelling. DAP-T, ORS, DS-A performed data analyses. All authors drafted the manuscript.
References
Appendices
Supplementary Electronic Material
Additional supporting information may be found in the on-line versión of this paper. See volume 66(2) on www.ardeola.org
Appendix 1. Ornithological collections of international museums that kindly provided data analysed in this study.
Appendix 2. Bird species of the 12 avifaunistic groups identified here for Neotropical seasonally dry forests (NSDF).
Appendix 3. Fifty percent majority rule consensus tree based upon 10,000 random site order-addition hierarchical clustering analyses of 563 grid cells corresponding to distribution of Neotropical seasonally dry forests (NSDF).
