Corresponding author: Tiina Särkinen (
Academic editor: L. Penev
A new species of
Se describe una nueva especie de
The tropical Andean hotspot is one of the most species rich but data poor areas of the world (
Many more species remain to be discovered, however, especially in Peru and Ecuador, where the number of new discoveries per year shows no sign of diminishing (
In an effort to speed up the process of cataloguing species diversity and recording accurate distributions, an approach referred to as Model-Based Sampling (MBS) has been developed (
Here we present a case study of MBS from
We examined 26 herbarium specimens in the herbaria listed in the text. These were combined with our field observations from Peru in the identification and description of the new taxon (see Taxonomy below). All specimens are cited in the text and full data is provided in the supplemental file and on
Following the MBS approach by
We first ran MAXENT based on the four observed collections from 2012 from southern Peru to identify potential suitable habitat areas for the target species (Model 1). The model was run with default settings (allowing for transformations of the covariates with the default thresholds for conversion, removing duplicate presence records, maximum number of background points = 10,000, maximum number of iterations = 500; convergence threshold = 0.00001; fit regulization parameter = 1; default prevalence = 0.5). To evaluate model performance, we ran it with cross-validation, where the occurrence data is randomly split into two equal-sized groups and one of them is then used for creating the model whilst the other is used for validating the model. We chose cross-validation approach because it uses all of the data for validation, unlike a single training/test split, and is hence more suitable when working with small numbers of occurrence points across a complex landscape (
The model was run with 11 bioclimatic variables at 30 arc second spatial resolution (c. 1 km2) (
Principal components analysis (PCA) results of the climatic variables (
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47% | 70% |
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Mean Diurnal Range (BIO2) | 0.085 | -0.117 |
Isothermality (BIO3) | -0.095 | -0.001 |
Temperature Seasonality (BIO4) | 0.044 | 0.175 |
Max Temperature of Warmest Month (BIO5) | -0.277 | 0.121 |
Min Temperature of Coldest Month (BIO6) | -0.304 | 0.160 |
Temperature Annual Range (BIO7) | 0.097 | -0.094 |
Mean Temperature of Driest Quarter (BIO9) | -0.300 | 0.148 |
Precipitation Seasonality (BIO15) | 0.070 | 0.346 |
Precipitation of Wettest Quarter (BIO16) | -0.224 | -0.254 |
Precipitation of Driest Quarter (BIO17) | -0.235 | -0.278 |
Precipitation of Warmest Quarter (BIO18) | -0.109 | -0.253 |
The Model 1 output was ground-truthed with additional field work. To target areas where potential new populations of
We ran a second model after the second field season, where all new localities identified through field work and herbarium visits and loans were included. Model 2 was run using a total of 26 records, of which four were from our first field trip in 2012, 17 were from our second field trip in 2013, and five from herbarium records (SI File 1 Occurrence data). The same 11 climatic predictors and MAXENT parameters were used as in Model 1 (see above). The model was trained using Peru as the study extent, and results were projected to an area that covered the whole of Ecuador and northern Bolivia (-81.0, 65.6, -19.5, 0). A final potential distribution map for
We evaluated the relative success of our SDM model predictions based on the mean area under curve (AUC) values of the receiver operating characteristic (ROC) curve of the cross-validation replicates. AUC values close to 1 indicate optimal performance, whilst values close to 0.5 indicate performance equal to random. Both models yielded AUC values > 0.98 indicating good model performance (
Model performance values for the two models run to detect suitable habitat areas for
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Model 1 | 4 | 0.987 | 0.009 |
Model 2 | 26 | 0.984 | 0.014 |
The results of Model 1, using only the first four records from 2012, showed highly suitable climatic conditions in northern and central Peru in the Departments of Cajamarca, La Libertad, Ancash and Huánuco, as well as in northernmost Piura and Loja, El Oro and Azuay provinces of Ecuador (
Potential habitat distribution map of
Model 2 was run with all collection data from 2012 and 2013, including all herbarium collections (
Distribution map of
Previous studies have clearly demonstrated how the use of SDM can dramatically increase detection rates of rare species in the field (
Here we describe a new species and provide both an observed distribution map as well as a modelled distribution range for the species. With increasing ease of SDM through publicly supported online portals such as BioVel (
Modelled distribution maps have large benefits over observed distribution maps. Modelled maps, although still incomplete, can be argued to provide a more realistic picture of the actual species’ distribution area. This is because modelled maps are less biased by collection densities, and although nowhere near complete, provide a step towards representing species distributions in a more realistic manner. Such maps will also aid in targeting field collecting efforts and provide additional information for planning conservation areas compared to traditional maps.
Whilst advocating the publication of modelled distributions for new species, we fully acknowledge that species distributions are not guided by simple factors such as climate alone. Many factors govern range size, including dispersal limitation, competitive exclusion, habitat destruction, urbanisation and agriculture, as well as species interactions. These complex factors are often dismissed in simplistic SDMs where only bioclimatic predictors are included. Simple SDMs can, however, be used as a starting point for evaluating rare species (e.g.,
In the case of
Our null hypothesis was that MBS approach cannot be used in such a highly variable landscape as the Andes with as few records as we had available. Our expectations were low for two reasons. Firstly, the climate data available for the Andes through WorldClim suffers from high uncertainty because only a few weather stations were used to interpolate the data (
Results from our case study indicate that both assumptions might not be correct. The high AUC scores shown by our models indicate that informative models can be run with as little data as used here and with climate predictors alone. The climate data appears to be of high enough quality to reveal broad patterns that can be used to identify suitable habitats across poorly explored regions. Variation in climate, and the associated elevational gradients, seem to explain large parts of plant distribution patterns in the Andes (
Another question is the minimum number of occurrence records required for building accurate distribution models. While it is well established that more data produce better, more accurate models (
The new species described here belongs to
Like
Herb with woody base, 20–50 cm tall, the individual stems to 1 m long and sprawling. Stems terete or somewhat angled with ridges, pubescent with simple uniseriate 1–4-celled trichomes often clustered along the stem angles; new growth densely pubescent with appressed 1–4-celled simple uniseriate trichomes 0.2–0.8 mm long. Sympodial units difoliate, not geminate. Leaves simple, 4.5–12(–15) cm long, 1.8–8 cm wide, ovate to elliptic; adaxial surface sparsely pubescent with more or less appressed 1-4-celled translucent simple uniseriate trichomes, these denser along the veins; abaxial surface more densely pubescent with simple uniseriate trichomes like those of the upper surface; primary veins 5–8 pairs; base acute and decurrent on the petiole; margins entire or occasionally with shallow lobes in the basal third; apex acute; petiole 0.5–2.5(–5) cm long, occasionally narrowly winged, sparsely pubescent with simple uniseriate trichomes like those of the stems and leaves. Inflorescences lateral and intermodal, 1–2.5 cm long, simple or once-branched, with 3–5(9) flowers, sparsely pubescent with appressed 1–2-celled simple uniseriate trichomes; peduncle 0.4–1.6 cm long, if the inflorescence branched then the peduncle rachis 0.4–0.6 cm long; pedicels 0.6–0.7 cm long, ca. 0.3 mm in diameter at the base and apex, straight and spreading, articulated at the base; pedicel scars spaced ca. 1 mm apart. Buds globose, the corolla only exserted from the calyx tube just before anthesis. Flowers 5-merous, all perfect; calyx tube ca. 1 mm long, the lobes 0.5–0.7 mm long with rounded apices, sparsely pubescent with 1–4-celled translucent simple uniseriate trichomes; corolla 5–6 mm in diameter, stellate, white with a yellow central portion near the base, lobed slightly less than halfway to the base, the lobes ca. 1.5 mm long, 2 mm wide, strongly reflexed at anthesis, later spreading, densely pubescent abaxially with 1–4-celled simple uniseriate trichomes, these usually shorter than the trichomes of the stems and leaves; filament tube minute, pubescent with tangled uniseriate trichomes adaxially; free portion of the filaments ca. 1 mm long, pubescent like the tube; anthers ellipsoid, yellow, ca. 1.5 mm long, 0.7–0.8 mm wide; ovary conical, glabrous; style 3–4 mm long, exserted (0.5)1–2 mm beyond the anther cone, densely pubescent with 2–3-celled simple uniseriate trichomes at the base; stigma globose and capitate, minutely papillate, bright green in live plants. Fruit a globose berry, 4–9 mm in diameter, green at maturity or green and turning purplish black when ripe, the surface not markedly shiny, lacking stone cells aggregates; fruiting pedicels 4–7 mm long, ca. 1 mm in diameter at the base, spreading and becoming somewhat more woody in fruit, usually remaining on the plant after fruit drops; fruiting calyx lobes spreading or appressed to the berry, not reflexed. Seeds 35–45 per berry, 1.2–1.5 mm long, 0.9–1 mm wide, flattened-reniform, yellowish, the surfaces minutely pitted, the testal cells pentagonal in outline.
Illustration of
Photos of
Endemic to Peru in the upper zones of seasonally dry tropical forests or in mid-elevation montane forests, usually above 2,000 m elevation, with only some overlap between the closely related
Flowering January–July, fruiting March–July.
The name
The IUCN threat status of
Most of the collections of
We thank Maria Baden, Emilio Perales, Diana Percy, Erica McAlister, and Andrew Matthews for assistance in the field, Asunción Cano and Betty Millán for assistance with permits in Peru, Severo Baldeón, Eric Rodríguez, Isidoro Sánchez Vega and the staff of the USM, HUT and CPUN herbaria for assistance during herbarium visits, and the Peruvian authorities of the Ministerio de Agricultura, Dirección General Forestal y de Fauna Silvestre, for granting our collection permit No. 084-2012-AG-DGFFS-DGEFFS under which this study was executed. We thank the herbaria mentioned in the text for specimen loans. This work was supported by NSF grant DEB-0316614 “PBI
Occurrence records of