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Abstract The recognition of areas of endemism (AEs) is important for conservation biology and biogeographical regionalization. Our objective was to quantitatively identify AEs and distributional congruence patterns of native rodents at the tropical/temperate transition in the central Andes. We analysed 6200 geo-referenced distributional records of 80 species in north-western Argentina using NDM/VNDM software. We found 20 AEs defined by 22 endemic species (27% of the total rodent fauna) and 34 patterns of distributional congruence in non-endemic rodents. Geographical range congruence follows two main patterns running parallel along the Andes.

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One is related to the humid eastern slopes of the Andes (Argentinean Yungas forest) and the other to the high Andes (Argentinean Puna plateau). Endemism was mainly restricted to the southernmost part of the Yungas forest and adjacent dryer valleys (Monte desert). Species diversity was highest in the northern sector of the Argentinean Yungas forest, where several species reach their southern distributional range. Jr Typing Tutor Crack Key. This incongruence among hotspots of diversity and endemism has also been also noted in diversity studies at continental and global scales. Our results provide a starting point for conservation planning in the southernmost Central Andes, which combines the taper of tropical diversity and range-restricted species endemic to the tropical–temperate transition. © 2014 The Linnean Society of London, Biological Journal of the Linnean Society, 2014, 112, 163–179.

Introduction Identification of areas of endemism (AEs) is important for both biogeography and conservation biology. In biogeography, the concept of endemism, which is the restricted distribution of a taxon to a particular geographical area and nowhere else, is key for biogeographical regionalization (;; ). The congruent geographical distribution of at least two endemic taxa defines an area of endemism, which is the basic unit in evolutionary biogeographical analyses and constitutes the first step for biogeographical regionalizations (;; ).

In conservation biology, AEs are important because they contain a set of species not occurring in any other area and therefore they represent essential conservation targets, particularly if occurring in rather small geographical areas (;; ). Despite the long-standing definition of AEs as the overlapping geographical distribution of endemic species (), non-spatially explicit methods have traditionally been applied to recognize them, such as parsimony analysis of endemicity (PAE) () or cluster analysis (). In this paper we adopt an approach that considers explicitly the spatial aspects of species distribution to identify AEs based on an optimality criterion (; ).

Basically, this method takes into account the distributional congruence among different species by dividing the study region into grid cells and assigning endemicity values to each species according to how well their distribution matches a given set of cells (= area). The values of all species that contribute to an area are summed to obtain the endemicity value (score) of the area. The areas with higher scores are preferred. Ideally, the taxa to be used in such an analysis of endemism should be those that are maximally endemic, i.e. Those whose geographical ranges are smaller than the study area. In this sense, rodents seem to be useful to analyse patterns of endemism and biographical regionalization in small geographical areas because a large proportion of mammals with restricted distributions are rodents, independently of their taxonomic level (genera/species) or spatial scale (;; ).

However, while tailored to identify distributional congruence of endemic species (i.e. AEs), the optimality criterion implemented in the method of can be used to recognize prominent patterns of geographical distribution shared by several species even when non-endemic, especially when applied on small geographical areas ().

The concordant distribution of non-endemic species may be determined either by species belonging to greater AEs or by species whose distributions are or have been affected by particular combinations of ecological or climatic factors within the geographical context of the study area. Whether the factors restricting the species ranges are a combination of present-day ecological and physical phenomena or a consequence of a history of vicariance and speciation, it is not a prerequisite for the detection of the pattern, which is, in turn, the first step in the elucidation of the process generating them. Traditionally, biogeographical divisions in north-western Argentina have been based on taxa (mainly plant species) characteristic to each unit (e g.;; ). Quantitative evaluations of species distributional congruence are very recent compared with narrative biogeographical regionalizations that have been made for decades by different naturalists.

Quantitative approaches allow the assessment of previously proposed biogeographical regionalization and even identify previously undetected AEs. Quantitative analyses of distributional congruence in northern Argentina have included selected taxa of plants, insects, reptiles, birds, and mammals, and have corroborated some conventional biogeographical divisions (;;;; ). However, rodents have never been included in such quantitative analyses of endemism. Beyond the convenience of choosing rodents to analyse patterns of endemicity in small geographical areas, there are additional reasons why they are useful to analyse distributional patterns. For instance, they are present in all type of biomes from tropical moist forest to gelid highland deserts, but at the same time, they constitute quite conspicuous assemblages as a consequence of adaptation to environments (; ). Moreover, rodents are the most diverse order of mammals containing 42% of known species (), thus frequently being the most important part of mammal assemblages concerning number of species.

Here we analyse distributional records of rodent species from north-western Argentina to recognize areas of endemism and the species that characterize them. Also, we quantitatively identify patterns of geographical distribution shared by several native, but non endemic to the study area, species and relate concordant distributional patterns to traditional biogeographical divisions within the study region. Materials and Methods Study area The study area comprises an expanse of 470 184 km 2 between parallels 22 and 30°S and meridians 66 and 68°W in the southern part of the central Andes (). The tropical eastern slopes of the Andes are recurrently recognized as a continental and global hotspot of diversity and endemism for several taxa (e.g.;;; ). The study region is located where the high diversity and endemism of the eastern Andes slopes gradually fade southward.

The geographical location of north-western Argentina (NWA) in the interface between tropical and subtropical latitudes and the Andean orography on the western half of the area determine great climatic contrasts and a mosaic of different biomes, ranging from tropical rain forest to high-altitude deserts, in a rather small geographical area (; ). A, geographical location of north-western Argentina (NWA) with a simplified scheme of the principal biomes present in the study region ( sensu; ).

B, political provinces of NWA and localities of rodent occurrence. The biogeographical description outlined below follows and. The eastern lowlands are dominated by semi-arid woodlands belonging to the Chaco biogeographical province. Along the eastern slopes of the Andes occurs, as a narrow strip and on a marked elevational gradient, the montane rainforest of the Yungas biogeographical province (). In the western rain shadow valleys occurs the xeric scrub of the Monte biogeographical province and on the surrounding arid mountain slopes the Prepuna biogeographical province. Finally, according to, highland desert of mountaintops can be divided into the Puna plateau, known as the Puna biogeographical province, and the biogeographical province of the High Andes in the highest and steep peaks [which considered as part of the Puna]. Data set The taxonomy used herein follows that outlined in.

All records are based on specimens and were obtained from the major Argentinean museum collections (see ). For some rodents, which are not well represented by museum specimens, we used literature records to complete our database. Literature records were included for Abrocoma cinerea (Thomas), Chinchilla brevicaudata (Waterhouse), Coendou bicolor (Tschudi), C. Prehensilis (Linnaeus), Ctenomys opimus Wagner, Dasyprocta punctata (Gray), Hydrochoerus hydrochaeris (Linnaeus), Lagidium viscacia (Molina), Lagostomus maximus (Desmarest), Microcavia shiptoni (Thomas), Myocastor coypus (Molina), Octodontomys gliroides (Gervais & d'Orbigny) and Pediolagus salinicola (Burmeister) (;;;;;; ) ( ). Each vouchered specimen was identified to specific level by the authors. Localities were taken from museum tags or from collector catalogues.

All localities were corroborated and geo-referenced using gazetteers, maps, or satellite images and field trips. Elevational ranges of species were inferred by considering the lowest and highest elevation records for each species. To analyse distributional patterns of species outside the study area, we extrapolate the whole geographical distributional range of each species using data from taxonomic reviews and maps published by the International Union for Conservation of Nature: Red List of Threatened Species (). We divided each species distribution into three classes: endemic to the study area, reaching its southern or northern distributional edge in the study area, and widely distributed. Distributional analysis To identify AEs and patterns of distributional congruence of rodent species in NWA, we used the method proposed by and. This method implements an optimality criterion that explicitly considers the spatial location of the species in the study region.

The study region is divided into cells and species distributions are used to identify, through an index of endemicity, among all the possible combinations of cells (= areas), those that constitute an AE. For each combination of cells, a score between 0 and 1 is calculated for all species.

The species score depends on the fit of the individual species to the given combination of cells (; ). The endemicity score of a given area (combination of cells) is the sum of the individual species scores.

Only the areas with highest scores are retained. This optimality criterion is implemented in the computer program NDM and its viewer VNDM (; available at ). The optimality criterion implemented in NDM was tailored to identify AEs based on its classic definition as places where two or more taxa are found together and nowhere else (). However, the method can also be applied to discover patterns of distributional congruence of species whose ranges exceed the study region size (). The spatial congruence of several species restricted to some parts of the study area suggests the occurrence of causal factors affecting their distributions.

The distributional congruence of species not endemic to the study area may be representing parts of larger AEs, local co-occurrence by particular combinations of ecological or climatic factors, or an artefact due to unevenness of sampling localities. It is beyond the scope of this study to identify the causal factors of the distributional concordances identified. Thus, we will consider two area categories, as follows: AEs (defined only or mostly by sympatric endemic species) and local patterns of distributional congruence (LPDCs, defined only or mostly by sympatric species not endemic to the study area).

We analysed our matrix of geo-referenced data using grid cells of four different sizes, 1°, 0.75°, 0.5° and 0.25° per side, and rectangular cells of 0.75 × 0.25° latitude–longitude. The use of several grid sizes and shapes increases the chance of finding different areas given the topographical complexity of the study area; moreover, using several grid sizes provides some kind of measure of support for a particular area of endemism: those areas which survive changes in grid size can be considered more strongly and clearly supported by the data (;; ).

Additionally, grid sizes and shapes used in this analysis were already used for the study area, facilitating comparisons with previous studies (e.g.;;;; ). In preliminary analyses of our data set, we found that by increasing cell size, species that are not actually sympatric may be included in a given area. Incorporation of many species as endemic to an area just based on their marginal records was a frequent imprecision when using coarse grid cells. These species generally presented low individual values of endemicity, and could obscure the true pattern of species composition represented by that set of cells.

Therefore, for the coarser grid cells (square cells of 1° and 0.75° per side) we set minimum species scores as 0.5. That is, only those species that contribute to an area with an individual value of endemicity equal to or greater than 0.5 finally contribute to that area. For the finer grid cells we did not set any minimum species scores. Contrary to coarse cells, if cells are too small the number of artificially empty cells increases and reduces the number of sympatric species, preventing correct detection of AEs. The practical problem with fine grids is that available data are usually incomplete, including many gaps. To counteract this, we established filling values, which minimize the number of empty cells (;; ).

Therefore, we analysed our matrix considering different filling values for assumed and inferred presences for smaller cells (square cells of 0.5° and 0.25° per side). An assumed presence implies that, even if a species has not been recorded in a cell, its presence is assumed due to its proximity to records in neighbouring cells; an inferred presence is implemented by the method itself when a species is absent from one cell but present in surrounding cells (). For a brief explanation on how the algorithm works see. Grid origin was arbitrarily defined and located at 70°W and 20°S. We carried out the distributional analysis through an heuristic (not exhaustive) search using the default NDM parameters: searching for groups of cells by adding/eliminating one cell at a time and saving groups defined by two or more endemic species with scores higher or equal to 2.0. When candidate areas shared more than 50% of their defining species, only the area with the highest score was retained. We obtained consensus areas by combining candidate areas that shared at least 50% of their defining species using the loose consensus rule in VNDM (see ).

The concordant distribution patterns were evaluated in the context of traditional biogeographical divisions by plotting the distribution of the defining species upon the terrestrial eco-regions as defined by and. The results were mapped using the program Global Mapper v11.02. Results The analysis of 6116 geo-referenced records of 80 rodent species (35 genera and 11 families) from NWA resulted in 65 consensus areas. Two large areas almost equivalent to the study were not considered in the following characterization. The remaining 63 areas represent either AEs in the strict sense or LPDCs characterized by 61 species of 30 genera. Twenty-two species (27.5% of all study area rodent fauna) are endemic to the study area, of which 19 species defined AEs ( ). Twenty consensus areas are defined mostly by endemic species, thus constituting AEs in the strict scene.

All of these areas are located in the southern part of the study area, either on the eastern moist Andean slopes and lowlands or on the western dry valleys. Thirty-four consensus areas are determined by species whose distributions exceed the study area, and thus we consider them as LPDCs. These areas fall into two main patterns running parallel to the Andes (i.e. Orientated north–south), one on the eastern Andean slopes and the other on the western highlands; both have smaller areas nested within them.

The remaining nine consensus areas are grouped into two patterns, one in the northern and the other in the southern part of the study area. These patterns, running transverse to the Andes (i.e.

East–west), lump the previously identified consensus areas on eastern moist Andean slopes and the western dry valley and highlands. The defining species are also combinations of the lowland and highland species groups previously identified, and thus we consider these to be spurious areas (SAs). Local patterns of distributional congruence Widespread Eastern Andean Slopes (‘Argentinean Yungas’) Ten consensus areas were equivalent to this pattern, comparable to the Argentinean Yungas biogeographical province (; ). Of these ten LPDCs, five were continuous areas () and five were disjunct areas. The disjunction in all cases was located in the same geographical region, at the Central sector of the Argentinean Yungas (). The continuous areas were obtained with only one grid size and different filling values, while disjunct areas were obtained with three grid sizes and different filling values (). Cell size 1° × 1° 0.75° × 0.75° 0.5° × 0.5° 0.25° × 0.25° 0.75° × 0.25° Area/Filling value (A I) 00 00 00 00 00 00 10 30 30 50 50 70 70 90 00 00 10 30 30 50 50 70 70 90 00 00 Total 1 Yungas 1D 1D 2D 1C 1C 1C 1D, 2C – – – – – – 10 2 Northern Yungas 1 2 4 2 1 1 3 – – – – 1 2 17 3 Southern Yungas – 1 2 1 1 1 1 2 2 2 1 2 2 18 4 Monte 1 – – – – 1 – – – – – – – 2 5 Puna 4 2 – – – – – – – – – – – 6 6 High Andes – – – – – – – – – – – 1 – 1 Spurious Area 1 1 – – 1 – 1 1 – – 1 1 – – 6 Spurious Area 2 2 – – – – – 1 – – – – – – 3 Total 10 6 8 5 3 5 9 2 2 3 2 4 4 63.

Cell size 1° × 1° 0.75° × 0.75° 0.5° × 0.5° 0.25° × 0.25° 0.75° × 0.25° Area/Filling value (A I) 00 00 00 00 00 00 10 30 30 50 50 70 70 90 00 00 10 30 30 50 50 70 70 90 00 00 Total 1 Yungas 1D 1D 2D 1C 1C 1C 1D, 2C – – – – – – 10 2 Northern Yungas 1 2 4 2 1 1 3 – – – – 1 2 17 3 Southern Yungas – 1 2 1 1 1 1 2 2 2 1 2 2 18 4 Monte 1 – – – – 1 – – – – – – – 2 5 Puna 4 2 – – – – – – – – – – – 6 6 High Andes – – – – – – – – – – – 1 – 1 Spurious Area 1 1 – – 1 – 1 1 – – 1 1 – – 6 Spurious Area 2 2 – – – – – 1 – – – – – – 3 Total 10 6 8 5 3 5 9 2 2 3 2 4 4 63. Widespread Eastern Andean Slopes (‘Argentinean Yungas’). A, occurrence localities of the defining species of a distributional pattern comparable to the entire Yungas of Argentina as a continuous area.

C, elevational range of all Argentinean Yungas-defining species. Widespread Eastern Andean Slopes (‘Argentinean Yungas’). A, occurrence localities of the defining species of a distributional pattern comparable to the entire Yungas of Argentina as a continuous area.

B, occurrence localities of the defining species of a distributional pattern comparable to the entire Yungas of Argentina as a disjunctive area (note that all species, except A. Edax, defining the continuous pattern also support the disjunct pattern). C, elevational range of all Argentinean Yungas-defining species. Thirteen species characterize these LPDCs. Six species characterize both continuous and disjunct areas [ Akodon caenosus Thomas, A. Simulator Thomas, Necromys lactens (Thomas), Oligoryzomys destructor (Tschudi), O.

Flavescens (Waterhouse), and Phyllotis osilae (JA Allen)], one species characterizes only continuous areas ( Andinomys edax Thomas) and six species characterize only disjunct areas [ Cavia tschudii Fitzinger, Euryoryzomys legatus Thomas, Graomys domorum (Thomas), Holochilus chacarius Thomas, Necromys lasiurus (Lund), and Oxymycterus paramensis Thomas]. There are no endemic species characterizing these areas, 11 species reach their southern distributional edge in the study area, and two are widely distributed (). Of areas (of 10) Individual score Distributional range Altitudinal range (m) A. Caenosus 6 0.000–0.882 SL 400–3100 A.

Simulator 3 0.000–0.764 SL 400–3000 A. Edax 1 0.000–0.685 SL 1000–4500 C. Tschudii 4 0.526–0.766 SL 371–2652 E. Legatus 1 0.000–0.795 SL 407–1270 G. Domorum 3 0.000–0.746 SL 1000–1200 H. Chacarius 2 0.000–0.687 WD 300–700 N. Lactens 4 0.000–0.806 SL 1400–3200 N.

Lasiurus 4 0.636–1.000 SL 1700–1900 O. Destructor 5 0.551–0.814 SL 400–2800 O.

Cf flavescens 6 0.000–0.862 WD 400–2800 O. Paramensis 4 0.417–0.825 SL 1400–3000 P. Osilae 5 0.000–0.907 SL 1400–3200. Of areas (of 10) Individual score Distributional range Altitudinal range (m) A.

Caenosus 6 0.000–0.882 SL 400–3100 A. Simulator 3 0.000–0.764 SL 400–3000 A. Edax 1 0.000–0.685 SL 1000–4500 C. Tschudii 4 0.526–0.766 SL 371–2652 E. Legatus 1 0.000–0.795 SL 407–1270 G. Domorum 3 0.000–0.746 SL 1000–1200 H. Chacarius 2 0.000–0.687 WD 300–700 N.

Lactens 4 0.000–0.806 SL 1400–3200 N. Lasiurus 4 0.636–1.000 SL 1700–1900 O. Destructor 5 0.551–0.814 SL 400–2800 O. Cf flavescens 6 0.000–0.862 WD 400–2800 O. Paramensis 4 0.417–0.825 SL 1400–3000 P. Osilae 5 0.000–0.907 SL 1400–3200. Of areas (of 10) Individual score Distributional range Altitudinal range (m) A.

Cinerea 2 0.000–0.786 WD 3800–5000 A. Andina 2 0.000–0.844 WD 3500–4300 A. Albiventer 1 0.818 SL 2400–5000 A.

Leucolimnaeus 1 0.750 E 3100–3500 A. Edax 2 0.000–0.711 SL 1000–4500 C. Lepidus 2 0.000–0.750 SL 2600–5000 C. Opimus 3 0.000–0.909 WD 2500–5000 E. Hirtipes 4 0.536–0.833 SL 2408–4343 E. Moreni 3 0.000–0.682 WD 1200–2300 E. Puerulus 3 0.000–0.833 SL 3450–4343 L.

Viscacia 3 0.000–0.900 WD 2500–5100 M. Shiptoni 3 0.000–0.750 E 3000–4000. Of areas (of 10) Individual score Distributional range Altitudinal range (m) A. Cinerea 2 0.000–0.786 WD 3800–5000 A. Andina 2 0.000–0.844 WD 3500–4300 A.

Albiventer 1 0.818 SL 2400–5000 A. Leucolimnaeus 1 0.750 E 3100–3500 A. Edax 2 0.000–0.711 SL 1000–4500 C. Lepidus 2 0.000–0.750 SL 2600–5000 C. Opimus 3 0.000–0.909 WD 2500–5000 E.

Hirtipes 4 0.536–0.833 SL 2408–4343 E. Moreni 3 0.000–0.682 WD 1200–2300 E.

Puerulus 3 0.000–0.833 SL 3450–4343 L. Viscacia 3 0.000–0.900 WD 2500–5100 M. Shiptoni 3 0.000–0.750 E 3000–4000.

Of areas (of 17) Individual score Distributional range Altitudinal range (m) A. Boliviensis 9 0.000–0.906 SL 2400–4200 A. Budini 10 0.000–0.979 SL 1500–2600 A. Fumeus 10 0.000–1.000 SL 670–3500 A. Sylvanus 3 0.000–0.800 E 700–2400 A. Toba 4 0.000–0.727 WD 255–935 C.

Bicolor 5 0.000–0.825 SL 350–1700 C. Prehensilis 1 0.000–0.750 SL 355–1100 C. Sylvanus 11 0.000–1.000 E 304–1720 D. Punctata 10 0.000–0.907 SL 300–1500 E.

Legatus 7 0.000–1.000 SL 407–1270 G. Domorum 2 0.000–0.766 SL 1000–1200 H.

Hydrochaeris 4 0.000–0.716 WD 255–900 O. Chacoensis 4 0.000–0.909 WD 255–1270 O. Paramensis 1 0.000–0.714 SL 700–3000 P.

Caprinus 5 0.000–0.790 SL 2100–4500 R. Austrinus 8 0.000–0.900 SL 300–2600 S. Ignitus 9 0.000–1.000 SL 472–1590 T. Primus 6 0.000–0.875 SL 1000–1500 T. Wolffsohni 8 0.000–0.844 SL 1180–23360. Of areas (of 17) Individual score Distributional range Altitudinal range (m) A.

Boliviensis 9 0.000–0.906 SL 2400–4200 A. Budini 10 0.000–0.979 SL 1500–2600 A. Fumeus 10 0.000–1.000 SL 670–3500 A.

Sylvanus 3 0.000–0.800 E 700–2400 A. Toba 4 0.000–0.727 WD 255–935 C. Bicolor 5 0.000–0.825 SL 350–1700 C. Prehensilis 1 0.000–0.750 SL 355–1100 C. Sylvanus 11 0.000–1.000 E 304–1720 D. Punctata 10 0.000–0.907 SL 300–1500 E. Legatus 7 0.000–1.000 SL 407–1270 G.

Domorum 2 0.000–0.766 SL 1000–1200 H. Hydrochaeris 4 0.000–0.716 WD 255–900 O. Chacoensis 4 0.000–0.909 WD 255–1270 O. Paramensis 1 0.000–0.714 SL 700–3000 P. Caprinus 5 0.000–0.790 SL 2100–4500 R. Austrinus 8 0.000–0.900 SL 300–2600 S.

Ignitus 9 0.000–1.000 SL 472–1590 T. Primus 6 0.000–0.875 SL 1000–1500 T. Wolffsohni 8 0.000–0.844 SL 1180–23360.

Of areas (of 18) Individual score Distributional range Altitudinal range (m) A. Illutea 6 0.000–0.938 E 540–2800 A. Aliquantulus 5 0.271–0.875 E 1700–1900 C. Latro 12 0.000–0.833 E 600–1100 C.

Saltarius 1 0.600 E 1600 C. Scagliai 7 0.000–0.700 E 1886–2739 C. Tuconax 12 0.000–0.900 E 361–3100 C. Tucumanus 12 0.000–0.875 E 400–600 C. Viperinus 17 0.000–1.000 E 700–2300 O. Wayku 11 0.000–0.800 E 800–2800 P.

Alisosiensis 6 0.000–0.944 E 1200–2200 P. Anitae 2 0.000–0.731 E 2300–2400 P.

Aureus 1 0.000–0.271 E 735 R. Auritus 12 0.000–0.972 NL 2400–3100 S. Delicatus 1 0.000–0.271 NL 300–500.

Saltarius 1 0.600 E 1600 C. Scagliai 7 0.000–0.700 E 1886–2739 C. Tuconax 12 0.000–0.900 E 361–3100 C. Tucumanus 12 0.000–0.875 E 400–600 C. Viperinus 17 0.000–1.000 E 700–2300 O.

Wayku 11 0.000–0.800 E 800–2800 P. Alisosiensis 6 0.000–0.944 E 1200–2200 P. Anitae 2 0.000–0.731 E 2300–2400 P.

Aureus 1 0.000–0.271 E 735 R. Auritus 12 0.000–0.972 NL 2400–3100 S. Delicatus 1 0.000–0.271 NL 300–500.

Spurious areas North-eastern Andean Slopes merged with Northern-western High Andes (‘SA 1’) Six areas equivalent to this pattern were obtained, with three different cell sizes, in the north of the study area (). These areas were characterized by 15 species, which can be divided in two sets: a highland group of species [ Abrothrix jelskii (Thomas), Akodon albiventer Thomas, A. Boliviensis Meyen, Auliscomys sublimis (Thomas), Ctenomys budini Thomas, Neotomys ebriosus Thomas, Octodontomys gliroides, and Phyllotis caprinus Pearson] and a lowland or sylvan set [ A.

Budini (Thomas), A. Fumeus Thomas, A. Sylvanus Thomas, Coendou bicolor, Sciurus ignitus (Gray), Tapecomys primus Anderson & Yates, and T. Wolffsohni (Thomas)] ().

Appendix Rodent species included in the analysis. The taxonomy used herein mainly follows. An asterisk indicates those species for which published record points were incorporated (see Materials and Methods). Endemic species are marked with a plus sign (+) for defining species and (++) for endemic but not defining AEs; not endemic characterizing species are marked with a numeral sign (#). Order Rodentia Suborder Sciurognathi Family Sciuridae – Subfamily Sciurinae – Sciurus ignitus # Family Cricetidae – Subfamily Sigmodontinae – Tribe Akodontini – A. Albiventer # – A. Aliquantulus + – A.

Boliviensis # – A. Budini # – A. Caenosus # – A.

Fumeus # – A. Leucolimnaeus + – A.

Simulator – A. Spegazzinii – A. Sylvanus ++ – A.

Toba # – Necromys lactens # – N. Lasiurus # – Oxymycterus paramensis # – O. Wayku ++ Family Cricetidae – Subfamily Sigmodontinae – Tribe Oryzomyini – Euryoryzomys legatus # – Holochilus chacarius – Oligoryzomys chacoensis # – O.

Destructor# – O. Flavescens # Family Cricetidae – Subfamily Sigmodontinae – Tribe Phyllotini – Andalgalomys olrogi # – Auliscomys sublimis # – C. Fecundus – C. Lepidus # – C. Musculinus – Eligmodontia bolsonensis + – E. Hirtipes # – E.

Moreni # – E. Puerulus # – Graomys chacoensis – G. Domorum # – G.

Griseoflavus – Phyllotis alisosiensis + – P. Anitae + – P. Caprinus # – P. Xanthopygus – Salinomys delicatus # – Tapecomys primus # – T. Wolffsohni # Family Cricetidae – Subfamily Sigmodontinae – Tribe Reithrodontini – Reithrodon auritus # Family Cricetidae – Subfamily Sigmodontinae – Tribe Thomasomyini – Rhipidomys austrinus # Family Cricetidae – Subfamily Sigmodontinae – Tribe Abrothricini – Abrothrix andina # – A. Illutea + – A. Amore A Prima Svista ITA. Jelskii # Family Cricetidae – Subfamily Sigmodontinae – Incertae sedis – Andinomys edax # – Neotomys ebriosus # Suborder Histricognathi Family Erethizontidae – Subfamily Erethizontinae – Coendou bicolor * # – C.

Prehensilis * # Family Chinchillidae – Subfamily Chinchillinae – Chinchilla brevicaudata * – Lagidium viscacia * # Family Chinchillidae – Subfamily Lagostominae – Lagostomus maximus * Family Caviidae – Subfamily Caviinae – Cavia tschudii – Galea musteloides – Microcavia australis – M. Shiptoni * + Family Caviidae – Subfamily Dolichotinae – Pediolagus salinicola * Family Hydrochoeridae – Subfamily Hydrochoerinae – Hydrochoerus hydrochaeris * # Family Dasyproctidae – Dasyprocta punctata * # Superfamilia Octodontoidea – Family Ctenomyidae – Ctenomys budini + – C. Coludo ++ – C.

Knighti + – C. Occultus ++ – C. Opimus * # – C. Saltarius + – C. Scagliai + – C. Sylvanus + – C. Tuconax + – C.

Tucumanus + – C. Viperinus + Family Octodontidae – Octodontomys gliroides * – Pipanacoctomys aureus + Family Abrocomidae – Abrocoma cinerea * # Family Myocastoridae – Myocastor coypus *.

• • • Biogeography is the study of the distribution of and in and through. Organisms and biological often vary in a regular fashion along geographic gradients of,, and habitat. Is the branch of biogeography that studies the distribution of plants. Is the branch that studies distribution of animals. Knowledge of spatial variation in the numbers and types of organisms is as vital to us today as it was to our early human, as we adapt to heterogeneous but geographically predictable.

Biogeography is an integrative field of inquiry that unites concepts and information from,,, and. Modern biogeographic research combines information and ideas from many fields, from the physiological and ecological constraints on organismal to and phenomena operating at global spatial scales and time frames. The short-term interactions within a habitat and species of organisms describe the ecological application of biogeography.

Historical biogeography describes the long-term, evolutionary periods of time for broader classifications of organisms. Early scientists, beginning with, contributed to the development of biogeography as a science. Beginning in the mid-18th century, Europeans explored the world and discovered the of life. The scientific theory of biogeography grows out of the work of (1769–1859), (1804–1881), (1806–1893), (1823–1913), (1829–1913) and other biologists and explorers.

Contents • • • • • • • • • • • • • • Introduction [ ] The patterns of species distribution across geographical areas can usually be explained through a combination of historical factors such as:,,, and. Through observing the geographic distribution of species, we can see associated variations in, river routes, habitat, and.

Additionally, this science considers the geographic constraints of areas and isolation, as well as the available ecosystem energy supplies. Over periods of changes, biogeography includes the study of plant and animal species in: their past and/or present living; their interim living sites; and/or their survival locales. As writer David Quammen put it, '.biogeography does more than ask Which species? It also asks Why? And, what is sometimes more crucial, Why not?'

Modern biogeography often employs the use of (GIS), to understand the factors affecting organism distribution, and to predict future trends in organism distribution. Often mathematical models and GIS are employed to solve ecological problems that have a spatial aspect to them. Biogeography is most keenly observed on the world's. These habitats are often much more manageable areas of study because they are more condensed than larger ecosystems on the mainland.

Islands are also ideal locations because they allow scientists to look at habitats that new have only recently colonized and can observe how they disperse throughout the island and change it. They can then apply their understanding to similar but more complex mainland habitats. Islands are very diverse in their, ranging from the tropical to arctic climates. This diversity in habitat allows for a wide range of species study in different parts of the world. One scientist who recognized the importance of these geographic locations was, who remarked in his journal 'The Zoology of Archipelagoes will be well worth examination'. Two chapters in were devoted to geographical distribution.

History [ ] 18th century [ ] The first discoveries that contributed to the development of biogeography as a science began in the mid-18th century, as Europeans explored the world and discovered the biodiversity of life. During the 18th century most views on the world were shaped around religion and for many natural theologists, the bible., in the mid-18th century, initiated the ways to classify organisms through his exploration of undiscovered territories. When he noticed that species were not as perpetual as he believed, he developed the Mountain Explanation to explain the distribution of biodiversity.

When Noah’s ark landed on Mount Ararat and the waters receded, the animals dispersed throughout different elevations on the mountain. This showed different species in different climates proving species were not constant. Linnaeus’ findings set a basis for ecological biogeography. Through his strong beliefs in Christianity, he was inspired to classify the living world, which then gave way to additional accounts of secular views on geographical distribution. He argued that the structure of an animal was very closely related to its physical surroundings. This was important to a George Louis Buffon’s rival theory of distribution. Distribution of four Permian and Triassic fossil groups used as biogeographic evidence for continental drift, and land bridging Moving on to the 20th century, introduced the Theory of in 1912, though it was not widely accepted until the 1960s.

This theory was revolutionary because it changed the way that everyone thought about species and their distribution around the globe. The theory explained how continents were formerly joined together in one large landmass,, and slowly drifted apart due to the movement of the plates below Earth’s surface.

The evidence for this theory is in the geological similarities between varying locations around the globe, fossil comparisons from different continents, and the jigsaw puzzle shape of the landmasses on Earth. Though Wegener did not know the mechanism of this concept of Continental Drift, this contribution to the study of biogeography was significant in the way that it shed light on the importance of environmental and geographic similarities or differences as a result of climate and other pressures on the planet. Importantly, late in his career Wegener recognised that testing his theory required measurement of continental movement rather than inference from fossils species distributions. The publication of by and in 1967 showed that the species richness of an area could be predicted in terms of such factors as habitat area, immigration rate and extinction rate.

This added to the long-standing interest in. The application of island biogeography theory to spurred the development of the fields of and. Classic biogeography has been expanded by the development of, creating a new discipline known as. This development allowed scientists to test theories about the origin and dispersal of populations, such as. For example, while classic biogeographers were able to speculate about the origins of species in the, phylogeography allows them to test theories of relatedness between these populations and putative source populations in and.

Biogeography continues as a point of study for many life sciences and geography students worldwide, however it may be under different broader titles within institutions such as ecology or evolutionary biology. In recent years, one of the most important and consequential developments in biogeography has been to show how multiple organisms, including mammals like monkeys and reptiles like lizards, overcame barriers such as large oceans that many biogeographers formerly believed were impossible to cross.

Biogeographic regions of Europe Modern applications [ ] Biogeography now incorporates many different fields including but not limited to physical geography, geology, botany and plant biology, zoology, and general biology. A biogeographer’s main focus is on what environmental factors and what the influence of humans do to the distribution of the specific species of study. In terms of applications of biogeography as a science today, technological advances have allowed satellite imaging and processing of the Earth.

Two main types of satellite imaging that are important within modern biogeography are Global Production Efficiency Model (GLO-PEM) and Geographic Information Systems (GIS). GLO-PEM uses satellite-imaging gives “repetitive, spatially contiguous, and time specific observations of vegetation.” These observations are on a global scale.

GIS can show certain processes on the earth’s surface like whale locations, sea surface temperatures, and bathymetry. Current scientists also use coral reefs to delve into the history of biogeography through the fossilized reefs. Paleobiogeography [ ] Paleobiogeography goes one step further to include data and considerations of.

Using molecular analyses and corroborated by, it has been possible to demonstrate that evolved first in the region of or the adjacent (which at that time lay somewhat further north and had a temperate climate). From there, they spread to the other continents and Southeast Asia – the part of then closest to their origin of dispersal – in the late, before achieving a global distribution in the early. Not knowing that at the time of dispersal, the Indian Ocean was much narrower than it is today, and that South America was closer to the Antarctic, one would be hard pressed to explain the presence of many 'ancient' lineages of perching birds in Africa, as well as the mainly South American distribution of the. Paleobiogeography also helps constrain hypotheses on the timing of biogeographic events such as and, and provides unique information on the formation of regional biotas.

For example, data from species-level phylogenetic and biogeographic studies tell us that the fish fauna accumulated in increments over a period of tens of millions of years, principally by means of allopatric speciation, and in an arena extending over most of the area of tropical South America (Albert & Reis 2011). In other words, unlike some of the well-known insular faunas (, Hawaiian drosophilid flies, African rift lake ), the species-rich Amazonian ichthyofauna is not the result of recent. For organisms, landscapes are divided naturally into discrete by, episodically isolated and reunited by processes.

In regions like the (or more generally Greater Amazonia, the Amazon basin, basin, and ) with an exceptionally low (flat) topographic relief, the many waterways have had a highly reticulated history over. In such a context, is an important factor affecting the evolution and distribution of freshwater organisms. Stream capture occurs when an upstream portion of one river drainage is diverted to the downstream portion of an adjacent basin. This can happen as a result of (or ), natural damming created by a, or headward or lateral of the watershed between adjacent basins. Concepts and fields [ ] Biogeography is a synthetic science, related to,,,,, and.