Translator Disclaimer
28 June 2017 The establishment threat of the obligate brood-parasitic Pin-tailed Whydah (Vidua macroura) in North America and the Antilles
Author Affiliations +
Abstract

The Pin-tailed Whydah (Vidua macroura) is a generalist obligate brood parasitic bird native to Africa, frequently found in the pet trade, which has successfully established exotic populations in 2 biodiversity hotspots in the Americas. We analyze the species' potential future distribution by identifying key locations in the continental United States, Hawaii, and the Antilles that contain suitable climatic characteristics, host species, and habitat requirements. We used species distribution modeling (MaxEnt) to depict the geographic patterns of possible Pin-tailed Whydah establishment and compared the predictive power of models that included combinations of climatic data (“climate”), land cover (“habitat”), and localities of historical and one known novel host (“hosts”). The preferred model, the “hosts” model, was the highest performing. The most important variable characterizing Pin-tailed Whydah distribution in the preferred model was the presence of a frequent historical host that is also established in the Americas, the Common Waxbill (Estrilda astrild), followed by a less frequent historical host, the Bronze Mannikin (Spermestes cucullata). Our research demonstrates that in the continental United States, Hawaii, and the Antilles, there are locations that possess the needed exotic host species that may facilitate further invasion by the Pin-tailed Whydah. Given that Pin-tailed Whydahs are known to exploit >20 host species from 4 families of birds, clear next steps include assessing their ability to parasitize novel, native species within the highly suitable areas identified in this research.

INTRODUCTION

Several obligate avian brood parasites have expanded their North American and Antillean range in recent decades and now pose a potential conservation threat to putative native host species in novel areas of their distribution (Pérez-Rivera 1986, Ortega 1998, Dinets et al. 2015). Foster parents raising brood-parasitic young typically experience losses in their own reproductive success, and some also have reduced annual survival (Rothstein 1990, Hauber 2003, Davies 2010). If these hosts coevolved with the brood parasite, they may have behavioral or life-history countermeasures that can reduce these costs of parasitism (De Mársico and Reboreda 2014). However, when the host is a novel species (i.e. has no coevolutionary history with the brood parasite), it typically experiences the full (if not increased) costs of parasitism. Some of the range-expanding brood parasites in the Americas involve icterid cowbirds (Molothrus spp.), which self-colonized new locations in the wake of, and likely as a result of, massive land-use transformations (Post and Wiley 1977, Pérez-Rivera 1986, Rothstein 1994, Ortega 1998). Here, we explore the potential for a new addition to this group, the Pin-tailed Whydah (Vidua macroura), a species heavily sold in pet markets (Raffaele 1989a, Moreno 1997) and one of the few obligate brood-parasitic finches in the genus Vidua that is also a host generalist (Lowther 2016) (Figure 1).

FIGURE 1.

Pin-tailed Whydah (Vidua macroura). Tibati, Cameroon. Photo credit: Eric Fishel

i0010-5422-119-3-449-f01.tif

The Pin-tailed Whydah (hereafter “whydah”) most commonly parasitizes the Common Waxbill (for scientific names of this and other host species, see Table 1) but also uses a variety of finch-like birds as hosts in its native range of central and southern Africa (Payne 2005, Lowther 2016). Of the 23 documented hosts in the native range (hereafter “historical hosts”), nearly half have been part of the pet trade, and 5 of them presently occur as established (locally breeding) exotic populations in North America and the Antilles (Table 1; Friedmann 1960, Payne 2005, Lowther 2016). These circumstances greatly increase the likelihood that the whydah can become an established exotic member of the avifauna in these regions. Whydahs have already established breeding and viable exotic populations in Puerto Rico (Raffaele 1989b) and, most likely, in southern California (Garrett and Garrett 2016). In Puerto Rico, whydahs were introduced during the 1960s and 1970s through accidental releases as part of the pet trade (Moreno 1997, Raffaele 1989a). On the island, they parasitize mostly exotic Orange-cheeked Waxbills, proving that the sequence of events necessary for successful invasion, from transportation to parasitizing hosts in a novel range, is possible. Additionally, whydahs have the ability to host-switch and parasitize novel species, even when the whydah has not evolved nestling mimicry toward these new hosts (Schuetz 2005a, Hauber and Kilner 2007, Lansverk et al. 2015). This trait increases the likelihood that whydahs will find, as suitable hosts, other non-African finches also established as exotics in North America and the Antilles. For example, in California, whydahs appear to parasitize the exotic Scaly-breasted Munia, an estrildid species native to Asia (Garrett and Garrett 2016).

TABLE 1.

Historical hosts and one known novel host of the Pin-tailed Whydah, with their frequency of parasitism, occurrence in the pet trade, and whether they have records in North America (Long 1981, Moulton and Pimm 1986, Lever 2005, Payne 2005, Schuetz 2005a, Pyle and Pyle 2009, Aagaard and Lockwood 2016, Birds Express 2016, Finch Farm 2016, Lowther 2016). Occurrence information was downloaded from the Global Biodiversity Information Facility (data accessed July 20, 2016). Species are ordered according to the Clements Checklist (Clements et al. 2016) and are separated by family (Chesser et al. 2016). Bold indicates species included in species distribution model(s).

i0010-5422-119-3-449-t01.tif

Species distribution models (SDMs) correlate species occurrence records with environmental attributes (e.g., climate, vegetation structure) to create a map depicting relative habitat suitability (Anderson et al. 2002, Rodríguez et al. 2007, Peterson et al. 2011). MaxEnt is a machine-learning SDM that has been used extensively to estimate habitat suitability for critically endangered species (Ibáñez et al. 2009), the potential geographic range of exotic species (Fernandez and Hamilton 2015), and the extent of suitable habitat in a future altered climate for whole taxonomic groups (Monterrubio-Rico et al. 2015). We used MaxEnt to create a map of relative habitat suitability for whydahs in the continental United States, Hawaii, and the Antilles. We consider such a map a necessary first stage in gauging the threat that whydahs pose to native birds in these locations. The maps are not sufficient, by themselves, to fully calculate invasion risk (see below). However, they do provide a way to quantitatively express where conditions are favorable for whydah occurrence and thus help prioritize monitoring and research efforts that can fully gauge risk (Stohlgren and Schnase 2006).

Initially, we used a standard approach to SDMs to identify locations where the basic climatic requirements of the whydah are met. This map is a baseline for where whydahs can reasonably be expected to occur in the future, based purely on abiotic requirements. We then extended the SDM to include a Land Use Land Cover (LULC) habitat covariate as a proxy for seeding grasses critical for the whydah's granivorous diet (Raffaele 1989b). We consider these abiotic and biotic suitability maps as conservative representations of where whydahs are likely to (at least) establish nascent populations. We then explored SDMs that explicitly recognize that, for the parasitic whydah, even highly suitable habitat will not be occupied if they cannot complete their life cycle by finding suitable host species (Friedmann 1960). We posit that the presence of any host individuals within suitable habitat is sufficient to create an initial bridgehead population of whydahs at that location. This bridgehead population provides the whydah the opportunity to expand to utilize other hosts (if needed) and subsequently grow its population and geographic range. For these SDMs, we assumed that whydahs will parasitize the 5 historical hosts and the 1 known novel host, the Scaly-breasted Munia, that occur in North America and the Antilles (Table 1).

METHODS

Species distribution models, in general, require known occurrence localities of a focal species and quantitative estimates of environmental conditions at those locations. Models are then fitted to environmental conditions at points where the species has been observed. These models can then be used to predict the habitat suitability of unsampled locations in geographic space, based on abiotic conditions in the model's environmental space (Peterson et al. 2011).

MaxEnt is a distribution modeling algorithm that requires geographic coordinates where the focal species is known to be present (Phillips et al. 2006, Merow et al. 2013). These points are then assigned a variety of attributes, which are provided by the model-builder and are thought by the latter to reflect environmental factors that dictate habitat suitability. From the attributes of these known-occurrence locations, MaxEnt builds a series of functions that quantitatively create a spatially interpolated map depicting where the species is likely to be found (Elith et al. 2011, Peterson et al. 2011, Merow et al. 2013). Suitability functions are evaluated by the model's ability to differentiate between the locations of known occurrence vs. a series of randomly selected other locations (i.e. pseudo-absences; Phillips et al. 2009). The key inputs to MaxEnt are the occurrence records for the focal species and the factors considered to be relevant attributes that define suitable locations. We describe this modeling approach below for our study species, the Pin-tailed Whydah, an invasive brood parasite.

Pin-tailed Whydah Occurrence Data

We sourced 39,279 georeferenced occurrences of whydahs worldwide from the Global Biodiversity Information Facility (GBIF;  http://www.gbif.org/species). We removed duplicate records and those with missing longitude and/or latitude. Outliers were eliminated through 2 distinct steps: We confirmed that (1) all records were located within boundaries of the country indicated by the database and not associated with errors in coordinate transcription or museum specimens, and (2) data were filtered by retaining only those points that corresponded to countries with exotic breeding populations of whydahs identified through a literature search (Long 1981, Lever 2005, Garrett and Garrett 2016). Occurrence records were spatially thinned to a nearest-neighbor distance of 3 km using spThin (Aiello-Lammens et al. 2015). We selected a 3 km thinning distance based on the home range of the Shaft-tailed Whydah (V. regia), a congener of the Pin-tailed Whydah (Barnard 1990). In the absence of information on the dispersal range of the Pin-tailed Whydah, we used this information on its congener to estimate a distance beyond which observers are unlikely to record the same individual. Thinning the occurrence records also reduced the possibility that areas with a high density of occurrences due to biased sampling effort contributed a disproportionate amount to model training (see below). Our final input data on whydah occurrence included 2,329 points (Figure 2).

FIGURE 2.

Georeferenced occurrence data for Pin-tailed Whydahs, used for generating species distribution models. All localities displayed here (n = 2,329) are human observations as reported to the Global Biodiversity Information Facility in both the native range (purple dots) and exotic range (green dots). We processed all occurrences by removing duplicates, spatially thinning, and excluding countries without established populations (that had no reports of breeding activity). We show the native distribution of the Pin-tailed Whydah in Africa (A) and exotic populations in California (B) and the Antilles and Florida (C).

i0010-5422-119-3-449-f02.tif

Attribute Variables

In an effort to characterize the abiotic conditions conducive to whydah occurrence, we included 19 WorldClim rasters that interpolate climate data for temperature and precipitation at a 5 arc-minute resolution worldwide (Hijmans et al. 2005). We used this set of climate variables to depict locations where abiotic conditions are conducive to whydah occurrence.

We recognize that whydahs, being granivorous finches, will be unable to survive in locations without seeding grasses to forage (Raffaele 1989b). Thus, we utilized the LULC database GlobCover to depict the location of vegetation that is likely to contain suitable forage (Arino et al. 2012). This LULC layer has 22 categories of land use, including 18 that quantify vegetation cover and 4 that indicate water (i.e. coastal habitat or river systems), artificial surfaces, ice, and barren areas. We used all 22 categories to identify vegetation and other land cover classes that may be important for predicting whydah distribution. We projected this layer to match WorldClim's 5 arc-minute resolution.

Finally, we included spatially explicit information on the localities of the 6 known historical or novel host species that occur as exotic populations in the continental United States, Hawaii, or the Antilles (Table 1). We had 2 options for doing so (Anderson 2017). The first was to generate SDMs for each host species and use these maps as input into the whydah SDM. The second was to use only the known point-occurrence records for host species as input into the whydah SDM. Both approaches were tested in situations similar to ours, where another species' presence is necessary for defining suitable habitat for the focal species (Hof et al. 2012, Giannini et al. 2013). In these prior studies, the latter approach (using only known point-occurrence records) consistently produced a better-performing model for the focal species (Giannini et al. 2013). For the whydah, its co-introduced hosts may be far from distributional equilibrium (Elith et al. 2010). Because the whydah is an obligate brood parasite, the species requires the presence of hosts; thus, realistic models should include only areas of known host occurrence.

Model Set and Selection

Using the above location attributes, we constructed 7 SDMs. The simplest were those that used only climate, habitat, or host information (“climate,” “habitat,” and “hosts” models, respectively; Table 2); we then created 4 additional models that represented all possible permutations of those 3 (Table 2). Once the model set was delineated, we evaluated the ability of each to predict whydah occurrence and used this information to select the single model that provided the most robust predictions (preferred model).

TABLE 2.

Model selection statistics for combinations of the covariates “climate,” “hosts,” and “habitat.” We provide statistics for the models based on a split of occurrence records into training (75%) and testing (25%). Model fit in relation to the preferred model is shown by ΔAICc. K indicates the number of parameters used by MaxEnt to fit models. The preferred model has AICc = 57,231. Omission error rates assess the frequency at which preferred models incorrectly classified known Pin-tailed Whydah occurrences.

i0010-5422-119-3-449-t02.tif

We selected the preferred model by using the sample-size-corrected Akaike's Information Criterion (AICc; Burnham and Anderson 2002, Warren and Seifert 2011). Model selection with AICc calculates the likelihood of estimated habitat suitability based on occurrence data. The AICc approach penalizes more complex models by accounting for the number of parameters used by each MaxEnt model (Swets 1988, Franklin 2009). We selected the preferred model that had the lowest AICc score. The preferred model's AICc score was subtracted from the AICc scores of all other models to indicate model relative fit (ΔAICc). We identified the most important covariates explaining variation in relative habitat suitability by determining the covariates that had the highest percent contribution.

For all 7 models, we applied a 10% minimum training presence threshold to MaxEnt's continuous output to produce a binary depiction of habitat suitability (Radosavljevic and Anderson 2014). Models were evaluated by testing their performance against 10,000 randomly sampled background points from 5-degree buffers around training occurrences (VanDerWal et al. 2009, Elith et al. 2011). Background points should be drawn from an extent potentially occupied by the focal species (Barve et al. 2011). The whydah is presently expanding its range, so we followed the work of Elith et al. (2010), who created SDMs for other invasive species and used a buffer to approximate a “reachable” extent within which we sampled background locations. Next, we created confusion matrices for each binary suitability map to assess their accuracy in terms of correctly identifying suitable vs. unsuitable habitat. We calculated omission rates to identify how often models labeled known suitable habitat as “unsuitable.” To calculate omission rates, occurrence records were randomly partitioned into calibration (75%) and evaluation (25%) sets. We did not calculate commission rates because background points should not be used to evaluate model fit (Peterson et al. 2008).

Methods for incorporating biotic interactions as model covariates have been previously developed (Anderson 2017). These often involve consensus among several models (Giannini et al. 2013). If whydahs, as host generalists, can ultimately exploit a greater range of hosts than they currently do, a model based solely on known-host presence will underestimate their potential distribution and, thus, invasion risk. We may also underestimate whydah invasion risk if the parasite maintains the same suite of exotic hosts that it does now, but those hosts themselves expand their ranges. Several co-introduced historical hosts used by the whydah are indeed predicted to do so (Stiels et al. 2011, 2015). Recognizing the potential for changing biotic interactions, we created a congruence map that relaxes the assumption of host specificity and host range stability by combining suitability predictions for the strict “hosts” model and the more relaxed “climate” model. This approach follows previous work that has used 2 distribution models to portray variability in the importance of a biotic covariate (Pidgeon et al. 2015). We refrain from interpreting poorly performing models, but rather present the congruence map as a potentially more relevant risk map. We obtained a quantitative measure of the whydah's potential distribution by first converting suitability maps to the North America Albers Equal Area Conical projection. We then vectorized each map and used the QGIS (QGIS Development Team 2016) field calculator to determine areas indicating suitability (in meters squared, which we converted to kilometers squared).

RESULTS

Our preferred model for predicting the potential distribution of the whydah is the “hosts” model (Table 2). This model had the lowest AICc across all permutations of model attributes. In this model, Common Waxbill and Bronze Mannikin had the highest percent contributions (48% and 43%, respectively; Table 3). The Scaly-breasted Munia, a known novel host of the whydah, ranked third in percent contribution (3%). Two less frequently used hosts, the Orange-cheeked Waxbill and Black-rumped Waxbill, ranked fourth and fifth in percent contribution (3% and 2%, respectively).

TABLE 3.

Variable contribution of the 5 most important host covariates in the “hosts” model. A stochastic process determines the initial variable contribution, potentially masking variable importance if covariates are highly correlated; thus, we provide average values across 5 model iterations. For scientific names of species, see Table 1.

i0010-5422-119-3-449-t03.tif

Our preferred model (“hosts”) had a relatively high omission error rate (0.26; threshold = 0.035). Inspection of misclassified points indicated that these points were often near correctly classified locations. Moreover, the model misclassified only 2 points in the nonnative distribution, one in California and one in Florida. The remaining points in the nonnative range were correctly classified by our preferred model. The continuous habitat suitability map (Figure 3) demonstrates that the preferred model indicated high suitability in areas where there are one or multiple host species present: the West Coast of the United States, much of Hawaii, small sections of Texas and Florida, and many islands in the Antilles.

FIGURE 3.

Geographic projection of relative suitability for Pin-tailed Whydah occurrence from the preferred MaxEnt model (“hosts”) for (A) southern Texas, (B) southern California, (C) Hawaii, (D) the Greater Antilles, (E) southern Florida, and (F) the Lesser Antilles. Warmer colors indicate greater relative suitability for Pin-tailed Whydah occurrence, given presence of hosts.

i0010-5422-119-3-449-f03.tif

We created a congruence map (Figure 4) using the conservative “hosts” model and the liberal “climate” model to present a risk map showing where the whydah could potentially occur should it begin to exploit hosts other than the 6 we included here, or if the hosts themselves expand their geographic range. The “climate” model's highest contributing covariates were maximum temperature in the warmest month (31%) and precipitation in the driest month (17%). As expected, predictions in geographic space were more circumscribed for models built with only host information (27,184 km2) when compared to the “climate” model (58,430 km2). The congruence map further highlighted the high potential of the Hawaii archipelago and islands in the Antilles to be occupied by whydahs (Figure 4).

FIGURE 4.

Congruence map showing agreement of two MaxEnt models of suitability for the Pin-tailed Whydah: “hosts” (our preferred model) and “climate.” The more conservative geographic extent outlined in red (indicating areas identified as suitable by the “hosts” model) hinges on the assumption that the Pin-tailed Whydah will parasitize only the suite of historical hosts and one known novel host that we included in the “hosts” model, and that the hosts will not expand their range. The larger area outlined in blue (indicating areas identified as suitable by the “climate” model) is a more liberal projection that does not include host information. However, this more liberal predicted area may be relevant as a risk map if Pin-tailed Whydahs begin to use a greater range of hosts, or if the present suite of hosts expand their range in our focal area. Locations were classified as suitable by a 10% minimum training threshold. We highlight predictions for (A) the continental United States, (B) the Lesser Antilles, (C) the Greater Antilles, and (D) Hawaii.

i0010-5422-119-3-449-f04.tif

DISCUSSION

Whether the whydah becomes the newest range-expanding brood parasite of conservation concern in North America, Hawaii, and the Antilles hinges primarily on the presence of suitable host species at the time of their initial establishment. Host information restricts our model to high suitability predicted in southern California, Hawaii, and several islands in the Caribbean. Contrary to previous work suggesting that climate and host information provides the best model fit for modeling parasite distribution (Giannini et al. 2013), our work suggests that for an obligate generalist brood-parasitic bird, host information alone provides the best fit.

Of the high-suitability areas we identify in the preferred model, only Puerto Rico and a small fraction of southern California are currently home to established whydah populations. We suggest that Hawaii is poised to harbor a robust whydah population if released (again) in large enough numbers. Whydahs were regularly observed as free-living on Oahu during the 1960s through to the 1980s, indicating that they had the opportunity to escape captivity and become free-living in the past. These released individuals either failed to breed at all or the nascent population they established eventually became locally extinct, possibly due to co-occurring declines in populations of estrildid hosts on Oahu (Pyle and Pyle 2009). Nevertheless, our results show that all main islands in the Hawaii archipelago have large tracts showing suitable climatic conditions for the whydah, with historical host species now well established within several of these locations (Table 1).

If whydahs can switch to parasitizing other exotic finch hosts on Hawaii (e.g., Chestnut Munias [Lonchura atricapilla]; Pyle and Pyle 2009) or in Florida and the Antilles (e.g., Black-headed Munia [Lonchura malacca]; Lever 2005), their potential for establishment or range expansion may increase, provided that the climatic and habitat conditions of the region are suitable for occupancy (Duncan et al. 2001, Tatem and Hay 2007, Blackburn et al. 2009). By contrast, our preferred model may produce unrealistically high suitability estimates if biotic interactions (e.g., novel host species or food sources) are identified as important for whydahs at a smaller scale or extent than our attribute layers can portray (Wisz et al. 2013). Given a lack of consensus on methods for incorporating biotic covariates into SDMs (Anderson 2017), we believe that our congruence map best portrays the risk of whydah occurrence by depicting the difference between a highly circumscribed model taking account only of known host locations (“hosts”) and a maximally liberal model constrained only by climatically suitable locations (“climate”).

Given that recent whydah introductions are likely due to accidental releases of captive birds in the pet trade (Raffaele 1989b, Acevedo and Restrepo 2008), whether this species establishes within these locations depends on the number and spatial location of release events (Lockwood et al. 2005). Here, we utilize abiotic and biotic predictor variables to estimate potentially suitable habitat, but we have no way of depicting the probability of pet owners releasing whydahs and thus no suitable proxy for propagule pressure. As a step in this direction, future efforts to quantify establishment probability could estimate import or direct sales data for whydahs within our focal regions and use this as a proxy for propagule pressure (Blackburn et al. 2009).

Finally, in evaluating the risk of whydahs to native birds in our focal regions, we must address their ability and propensity to use native species as their hosts. Whydahs are remarkably flexible in the hosts they use in their native range (Payne 2005, Lowther 2016) and show the ability to successfully reproduce when expanding their host range (Lansverk et al. 2015). In order to reduce the possibility of a foster parent rejecting their young, whydahs mimic the mouth markings of the nestlings of their most frequent host species (Friedmann 1960, Payne 2005, Schuetz 2005b). This brood-parasitic adaptation initially would argue that whydahs are likely to be slow (or incapable) of switching to hosts with substantially different nestling gapes. However, mouth markings that are somewhat mismatched to hosts are clearly not prohibiting whydahs from experiencing reproductive success in the native (Payne 2005, Lansverk et al. 2015) and exotic range (Garrett and Garrett 2016). For example, Lansverk et al. (2015) provide evidence that whydahs experience similar reproductive success when parasitizing a novel host in Africa as they do with their more common hosts.

Despite the flexibility of host use in the whydah, we suggest that there are notable barriers for whydahs to surmount if they are to exploit as hosts the native avifaunas in our focal regions. It is not clear if the whydah is capable of making the jump from their usual small finch hosts to the suite of native species found in the West Coast of the United States, Florida, Hawaii, or the Antilles. So far, there have been no reports of whydahs parasitizing native species in Puerto Rico or southern California, although we found no evidence that anyone has been systematically looking for such events. We suggest that the most likely novel native hosts would be within the fringillid finches (Jetz et al. 2012). However, whydahs rarely exploit hosts with the open-cup nests typical of fringillids (Friedmann 1960); furthermore, hosts would need to accept the eggs and have a diet similar to that of whydahs (Davies 2010). In the Antilles, native finches (Euphonia spp.) build dome-shaped nests that may be more readily parasitized by whydahs, though their frugivorous (rather than granivorous) diet presents another barrier to parasitism (Raffaele 1989a).

The question of risk to native avifaunas is perhaps better posed as one of addressing how different gape markings, nest construction, and host diet must be before either (1) whydahs will not lay eggs in a host's nest or (2) whydah nestlings will be rejected by the host parents (sensu Schuetz 2005b). Posing this question in the context of the suite of possible native hosts within suitable whydah habitat we have identified here can substantially inform our understanding of the risk whydahs pose to the North American and Antillean avifaunas. The locations identified here as being suitable for whydah occupancy are native homes to a wide variety of bird species, many of which are threatened with extinction and are naive to brood parasitism. If whydahs were to successfully utilize them, the negative conservation and management impact on these new host species could be quite high.

ACKNOWLEDGMENTS

We thank C. Balakrishnan, N. Burg, P. García-Díaz, M. Louder, T. Manna, J. Schuetz, and the anonymous reviewers for helpful discussions and/or comments.

Funding statement: We thank the National Geographic Society (to M.E.H., J.L.L., and P.C.) and the National Science Foundation (NSF IOS-145624 to M.E.H).

Ethics statement: This research used georeferenced occurrence points obtained from databases online. No aspects involved wild birds, and thus an animal-use ethics protocol was not required.

Author contributions: R.C.-O., J.L.L., P.C., and M.E.H. conceived the idea, design, and analyses and wrote the manuscript. R.C.-O. and J.L.L. analyzed the data.

LITERATURE CITED

1.

Aagaard, K., and J. L. Lockwood (2016). Severe and rapid population declines in exotic birds. Biological Invasions 18:1667–1678. Google Scholar

2.

Acevedo, M. A., and C. Restrepo (2008). Land-cover and land-use change and its contribution to the large-scale organization of Puerto Rico's bird assemblages. Diversity and Distributions 14:114–122. Google Scholar

3.

Aiello-Lammens, M. E., R. A. Boria, A. Radosavljevic, B. Vilela, and R. P. Anderson (2015). spThin: An R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography 38:541–545. Google Scholar

4.

Anderson, R. P. (2017). When and how should biotic interactions be considered in models of species niches and distributions? Journal of Biogeography 44:8–17. Google Scholar

5.

Anderson, R. P., A. T. Peterson, and M. Gómez-Laverde (2002). Using niche-based GIS modeling to test geographic predictions of competitive exclusion and competitive release in South American pocket mice. Oikos 98:3–16. Google Scholar

6.

Arino, O., J. J. Ramos Perez, V. Kalogirou, S. Bontemps, P. Defourny, and E. Van Bogaert (2012). Global Land Cover Map for 2009 (GlobCover 2009). European Space Agency, Paris, France. Google Scholar

7.

Barnard, P. (1990). Male tail length, sexual display intensity and female sexual response in a parasitic African finch. Animal Behaviour 39:652–656. Google Scholar

8.

Barve, N., V. Barve, A. Jiménez-Valverde, A. Lira-Noriega, S. P. Maher, A. T. Peterson, J. Soberón, and F. Villalobos (2011). The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecological Modelling 222:1810–1819. Google Scholar

9.

Birds Express (2016). African birds for sale.  http://www.birdsexpress.net/africa-birds.html Google Scholar

10.

Blackburn, T. M., J. L. Lockwood, and P. Cassey (2009). Avian Invasions: The Ecology and Evolution of Exotic Birds. Oxford University Press, Oxford, UK. Google Scholar

11.

Burnham, K. P., and D. R. Anderson (2002). Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach, second edition. Springer, New York, NY, USA. Google Scholar

12.

Chesser, R. T., K. J. Burns, C. Cicero, J. L. Dunn, A. W. Kratter, I. J. Lovette, P. C. Rasmussen, J. V. Remsen, Jr., J. D. Rising, and D. F. Stotz (2016). Fifty-seventh Supplement to the American Ornithologists' Union Check-list of North American Birds. The Auk: Ornithological Advances 133:544–560. Google Scholar

13.

Clements, J. F., T. S. Schulenberg, M. J. Iliff, D. Roberson, T. A. Fredericks, B. L. Sullivan, and C. L. Wood (2016). The eBird/Clements checklist of birds of the world: v2016.  http://www.birds.cornell.edu/clementschecklist/download Google Scholar

14.

Davies, N. B. (2010). Cuckoos, Cowbirds and Other Cheats. T & AD Poyser, London, UK. Google Scholar

15.

De Mársico, M. C., and J. C. Reboreda (2014). High frequency but low impact of brood parasitism by the specialist Screaming Cowbird on its primary host, the Baywing. Emu 114:309–316. Google Scholar

16.

Dinets, V., P. Samaš, R. Croston, T. Grim, and M. E. Hauber (2015). Predicting the responses of native birds to transoceanic invasions by avian brood parasites. Journal of Field Ornithology 86:244–251. Google Scholar

17.

Duncan, R. P., M. Bomford, D. M. Forsyth, and L. Conibear (2001). High predictability in introduction outcomes and the geographical range size of introduced Australian birds: A role for climate. Journal of Animal Ecology 70:621–632. Google Scholar

18.

Elith, J., M. Kearney, and S. Phillips (2010). The art of modelling range-shifting species. Methods in Ecology and Evolution 1:330–342. Google Scholar

19.

Elith, J., S. J. Phillips, T. Hastie, M. Dudík, Y. E. Chee, and C. J. Yates (2011). A statistical explanation of MaxEnt for ecologists. Diversity and Distributions 17:43–57. Google Scholar

20.

Fernandez, M., and H. Hamilton (2015). Ecological niche transferability using invasive species as a case study. PLoS ONE 10:e0119891. Google Scholar

21.

Finch Farm (2016). Pet finches for sale.  http://www.thefinchfarm.com/ Google Scholar

22.

Franklin, J. (2009). Mapping Species Distributions: Spatial Inference and Prediction. Cambridge University Press, Cambridge, UK. Google Scholar

23.

Friedmann, H. (1960). The Parasitic Weaverbirds. Smithsonian Institution, Washington, DC, USA. Google Scholar

24.

Garrett, J. F., and K. L. Garrett (2016). The Pin-tailed Whydah as a brood parasite of the Scaly-breasted Munia in southern California. Western Birds 47:314–320. Google Scholar

25.

Giannini, T. C., D. S. Chapman, A. M. Saraiva, I. Alves-dos-Santos, and J. C. Biesmeijer (2013). Improving species distribution models using biotic interactions: A case study of parasites, pollinators and plants. Ecography 36:649–656. Google Scholar

26.

Hauber, M. E. (2003). Hatching asynchrony, nestling competition, and the cost of interspecific brood parasitism. Behavioral Ecology 14:227–235. Google Scholar

27.

Hauber, M. E., and R. M. Kilner (2007). Coevolution, communication, and host chick mimicry in parasitic finches: Who mimics whom? Behavioral Ecology and Sociobiology 61:497–503. Google Scholar

28.

Hijmans, R. J., S. E. Cameron, J. L. Parra, P. G. Jones, and A. Jarvis (2005). Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25:1965–1978. Google Scholar

29.

Hof, A. R., R. Jansson, and C. Nilsson (2012). How biotic interactions may alter future predictions of species distributions: Future threats to the persistence of the Arctic fox in Fennoscandia. Diversity and Distributions 18:554–562. Google Scholar

30.

Ibáñez, I., J. A. Silander, Jr., A. M. Wilson, N. LaFleur, N. Tanaka, and I. Tsuyama (2009). Multivariate forecasts of potential distributions of invasive plant species. Ecological Applications 19:359–375. Google Scholar

31.

Jetz, W., G. H. Thomas, J. B. Joy, K. Hartmann, and A. Q. Mooers (2012). The global diversity of birds in space and time. Nature 491:444–448. Google Scholar

32.

Lansverk, A. L., J.-B. Dongmo, J. G. Schuetz, and C. N. Balakrishnan (2015). Parasitism of the Black-crowned Waxbill (Estrilda nonnula) by the Pin-tailed Whydah (Vidua macroura): Implications for host-specific adaptation by a generalist brood-parasite. The Wilson Journal of Ornithology 127:733–739. Google Scholar

33.

Lever, C. (2005). Naturalised Birds of the World. T & AD Poyser, London, UK. Google Scholar

34.

Lockwood, J. L., P. Cassey, and T. Blackburn (2005). The role of propagule pressure in explaining species invasions. Trends in Ecology & Evolution 20:223–228. Google Scholar

35.

Long, J. L. (1981). Introduced Birds of the World. David and Charles, London, UK. Google Scholar

36.

Lowther, P. E. (2016). Host list of avian brood parasites (Viduidae).  https://www.fieldmuseum.org/sites/default/files/plowther/2016/01/21/vidua-hosts-19jan2016.pdf Google Scholar

37.

Merow, C., M. J. Smith, and J. A. Silander, Jr. (2013). A practical guide to MaxEnt for modeling species' distributions: What it does, and why inputs and settings matter. Ecography 36:1058–1069. Google Scholar

38.

Monterrubio-Rico, T. C., J. F. Charre-Medellin, and C. Sáenz-Romero (2015). Current and future habitat availability for Thick-billed and Maroon-fronted parrots in northern Mexican forests. Journal of Field Ornithology 86:1–16. Google Scholar

39.

Moreno, J. A. (1997). Review of the subspecific status and origin of introduced finches in Puerto Rico. Caribbean Journal of Science 33:233–238. Google Scholar

40.

Moulton, M. P., and S. L. Pimm (1986). Species introductions to Hawaii. InEcology of Biological Invasions of North America and Hawaii ( H. A. Mooneyand J. A. Drake, Editors). Springer, New York, NY, USA. pp. 231–249. Google Scholar

41.

Ortega, C. P. (1998). Cowbirds and Other Brood Parasites. University of Arizona Press, Tucson, AZ, USA. Google Scholar

42.

Payne, R. B. (2005). Nestling mouth markings and colors of Old World finches Estrildidae: Mimicry and coevolution of nesting finches and their Vidua brood parasites. Museum of Zoology, University of Michigan, Miscellaneous Publications 194. Google Scholar

43.

Pérez-Rivera, R. A. (1986). Parasitism by the Shiny Cowbird in the interior parts of Puerto Rico. Journal of Field Ornithology 57:99–104. Google Scholar

44.

Peterson, A. T., M. Papeş, and J. Soberón (2008). Rethinking receiver operating characteristic analysis applications in ecological niche modeling. Ecological Modelling 213:63–72. Google Scholar

45.

Peterson, A. T., J. Soberón, R. G. Pearson, R. P. Anderson, E. Martínez-Meyer, M. Nakamura, and M. B. Araújo (2011). Ecological Niches and Geographic Distributions. Princeton University Press, Princeton, NJ, USA. Google Scholar

46.

Phillips, S. J., R. P. Anderson, and R. E. Schapire (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling 190:231–259. Google Scholar

47.

Phillips, S. J., M. Dudík, J. Elith, C. H. Graham, A. Lehmann, J. Leathwick, and S. Ferrier (2009). Sample selection bias and presence-only distribution models: Implications for background and pseudo-absence data. Ecological Applications 19:181–197. Google Scholar

48.

Pidgeon, A. M., L. Rivera, S. Martinuzzi, N. Politi, and B. Bateman (2015). Will representation targets based on area protect critical resources for the conservation of the Tucuman Parrot? The Condor: Ornithological Applications 117:503–517. Google Scholar

49.

Post, W., and J. W. Wiley (1977). The Shiny Cowbird in the West Indies. The Condor 79:119–121. Google Scholar

50.

Pyle, R. L., and P. Pyle (2009). The birds of the Hawaiian Islands: Occurrence, history, distribution, and status (version 1). B.P. Bishop Museum, Honolulu, HI, USA. Google Scholar

51.

QGIS Development Team (2016). QGIS Geographic Information System. Open Source Geospatial Foundation Project.  http://www.qgis.org Google Scholar

52.

Radosavljevic, A., and R. P. Anderson (2014). Making better MaxEnt models of species distributions: Complexity, overfitting and evaluation. Journal of Biogeography 41:629–643. Google Scholar

53.

Raffaele, H. A. (1989a). A Guide to the Birds of Puerto Rico and the Virgin Islands. Princeton University Press, Princeton, NJ, USA. Google Scholar

54.

Raffaele, H. A. (1989b). The ecology of native and introduced granivorous birds in Puerto Rico. InBiogeography of the West Indies: Patterns and Perspectives ( C. A. Woodsand F. E. Sergile, Editors). CRC Press, Boca Raton, FL, USA. pp. 541–566. Google Scholar

55.

Rodríguez, J. P., L. Brotons, J. Bustamante, and J. Seoane (2007). The application of predictive modelling of species distribution to biodiversity conservation. Diversity and Distributions 13:243–251. Google Scholar

56.

Rothstein, S. I. (1990). A model system for coevolution: Avian brood parasitism. Annual Review of Ecology and Systematics 21:481–508. Google Scholar

57.

Rothstein, S. I. (1994). The cowbird's invasion of the far west: History, causes and consequences experienced by host species. Studies in Avian Biology 15:301–315. Google Scholar

58.

Schuetz, J. G. (2005a). Low survival of parasite chicks may result from their imperfect adaptation to hosts rather than expression of defenses against parasitism. Evolution 59:2017–2024. Google Scholar

59.

Schuetz, J. G. (2005b). Reduced growth but not survival of chicks with altered gape patterns: Implications for the evolution of nestling similarity in a parasitic finch. Animal Behaviour 70:839–848. Google Scholar

60.

Stiels, D., B. Gair, K. Schidelko, J. O. Engler, and D. Rödder (2015). Niche shift in four non-native estrildid finches and implications for species distribution models. Ibis 157:75–90. Google Scholar

61.

Stiels, D., K. Schidelko, J. O. Engler, R. van den Elzen, and D. Rödder (2011). Predicting the potential distribution of the invasive Common Waxbill Estrilda astrild (Passeriformes: Estrildidae). Journal of Ornithology 152:769–780. Google Scholar

62.

Stohlgren, T. J., and J. L. Schnase (2006). Risk analysis for biological hazards: What we need to know about invasive species. Risk Analysis 26:163–173. Google Scholar

63.

Swets, J. A. (1988). Measuring the accuracy of diagnostic systems. Science 240:1285–1293. Google Scholar

64.

Tatem, A. J., and S. I. Hay (2007). Climatic similarity and biological exchange in the worldwide airline transportation network. Proceedings of the Royal Society B 274:1489–1496. Google Scholar

65.

VanDerWal, J., L. P. Shoo, C. Graham, and S. E. Williams (2009). Selecting pseudo-absence data for presence-only distribution modeling: How far should you stray from what you know? Ecological Modelling 220:589–594. Google Scholar

66.

Warren, D. L., and S. N. Seifert (2011). Ecological niche modeling in MaxEnt: The importance of model complexity and the performance of model selection criteria. Ecological Applications 21:335–342. Google Scholar

67.

Wisz, M. S., J. Pottier, W. D. Kissling, L. Pellissier, J. Lenoir, C. F. Damgaard, C. F. Dormann, M. C. Forchhammer, J.-A. Grytnes, A. Guisan, R. K. Heikkinen, et al. (2013). The role of biotic interactions in shaping distributions and realised assemblages of species: Implications for species distribution modelling. Biological Reviews 88:15–30. Google Scholar
© 2017 Cooper Ornithological Society.
Robert Crystal-Ornelas, Julie L. Lockwood, Phillip Cassey, and Mark E. Hauber "The establishment threat of the obligate brood-parasitic Pin-tailed Whydah (Vidua macroura) in North America and the Antilles," The Condor 119(3), 449-458, (28 June 2017). https://doi.org/10.1650/CONDOR-16-150.1
Received: 21 August 2016; Accepted: 1 April 2017; Published: 28 June 2017
JOURNAL ARTICLE
10 PAGES


SHARE
ARTICLE IMPACT
Back to Top