Morphospace analysis leads to an evo-devo model of digit patterning.

Biological forms occupy a constrained portion of theoretical morphospaces. Developmental models accounting for empirical morphospaces are necessary to achieve a better understanding of this phenomenon. We analyzed the phalangeal formulas (PFs) in lizards and relatives' hands by comparing them with a set of simulated PFs that compose a theoretical morphospace. We detected that: (1) the empirical morphospace is severely limited in size, (2) the PFs comply with two properties of phalangeal count per digit, namely the ordering rule (DI ≤ DII ≤ DIII ≤ DIV ≥ DV), and the contiguity relationship (neighbor digits differ on average in one phalanx), (3) the totality of the PFs can be categorized into four categories of hands aligned along a feasibility gradient. We also reconstructed the evolution of PFs and found a stepwise trajectory from the plesiomorphic PF towards reduced conditions. Finally, we propose a developmental model as the generative mechanism behind the PFs. It is consistent with the bulk of evidence managed and involves an ordered digit primordia initialization timed with periodic signals of joint formation coming from digit tips. Our approach is also useful to address the study of other meristic sequences in nature such as dental, floral, and branchial formulas.

Demonstration Using Field Collections that Argentina Fall Armyworm Populations Exhibit Strain-specific Host Plant Preferences.

Spodoptera frugiperda, the fall armyworm, is a major economic pest throughout the Western Hemisphere of corn (maize), cotton, sorghum, and a variety of agricultural grasses and vegetable crops. Studies in the United States, the Caribbean, and Brazil demonstrated the existence of two subpopulations (previously designated "host strains") that differ in their choice of plant host. Specifically, the corn strain is preferentially found in corn and sorghum, while the rice strain is dominant in rice, turf grass, and alfalfa. However, inconsistent results were reported in surveys of fall armyworm in Argentina, with some indicating that the host plant preferences of the two strains might be compromised or even nonexistent. If correct, this would complicate efforts to control this pest by considerably expanding the range of habitats that would have to be considered as potential sources for fall armyworm infestations in specific crops. A reexamination of Argentine fall armyworm, this time with field collections rather than the laboratory colonies used in previous studies, confirmed the existence of the two strains and their host preferences. Specifically, the corn strain was consistently the majority population infesting corn and was usually so in sorghum, while the rice strain was predominant in pasture/turf grasses and alfalfa. The one outlier was a collection from rice, which had a corn strain majority. Overall, the data were generally consistent with strain behaviors observed in other areas of the Western Hemisphere.

Interplay between postcranial morphology and locomotor types in Neotropical sigmodontine rodents.

Sigmodontine rats are one of the most diverse components of the Neotropical mammal fauna. They exhibit a wide ecological diversity and a variety of locomotor types that allow them to occupy different environments. To explore the relationship between morphology and locomotor types, we analyzed traits of the postcranial osteology (axial and appendicular skeletons) of 329 specimens belonging to 51 species and 29 genera of sigmodontines exhibiting different locomotor types. In this work, postcranial skeletal characters of these rats are considered in an ecomorphological study for the first time. Statistical analyses showed that of the 34 osteological characters considered, 15 were related to the locomotor types studied, except for ambulatory. However, character mapping showed that climbing and jumping sigmodontines are the only taxa exhibiting clear adaptations in their postcranial osteology, which are highly consistent with the tendencies described in many other mammal taxa. Climbing, digging and swimming rats presented statistically differences in traits associated with their vertebral column and limbs, whereas jumping rats showed modifications associated with all the skeletal regions. Our data suggest that sigmodontine rats retain an all-purpose morphology that allows them to use a variety of habitats. This versatility is particularly important when considering the lack of specialization of sigmodontines for a specific locomotor mode. Another possible interpretation is that our dataset probably did not consider relevant information about these groups and should be increased with other types of characters (e.g. characters from the external morphology, myology, etc.).

Towards a dynamic analysis of weighted networks in biogeography.

An improvement to the Network Analysis Method (NAM) in Biogeography based on weighted inference and dynamic exploration of sympatry networks is proposed. Intricate distributions of species result in a reticulated structure of spatial associations. Species are geographically connected through sympatry links forming an overall natural network in biogeography. Spatial records are the signals that provide evidence to infer these sympatry links in the network. Punctual data are independent of a priori area determination. NAM is oriented to detect groups of species embedded into the global network that are internally sustained by sympatric cohesiveness but weakly connected (or disconnected) to outgroup entities. These groups, called units of co-occurrence (UCs), are segregated through the iterative removal of intermediary species according to their betweenness scores. Instances of analysis of the original NAM are improved through the following changes and extensions: (i) inference of weighted sympatry networks using new measures sensitive to the strength of overlap and topological resemblance between set of points; (ii) construction of a basal network discriminating major from minor sympatry associations; (iii) evaluation of the entire process of iterative removal of intermediary species for the selection of UCs found on different subnetworks; (iv) network partitioning based on the intrinsic cohesiveness of the UCs; (v) production of a graphical tool (cleavogram) depicting the structural changes of the network along the removal process. Improvements are tested using real and hypothetical data sets. Resolution of patterns is notably increased due to a more accurate recognition of allopatric patterns and the possibility of segregating spatially overlapped UCs. As in original NAM, spatial expressions of UCs are building blocks for biogeography supported by strictly endemic and connected species through sympatry paths.

Sympatry inference and network analysis in biogeography.

A new approach for biogeography to find patterns of sympatry, based on network analysis, is proposed. Biogeographic analysis focuses basically on sympatry patterns of species. Sympatry is a network (= relational) datum, but it has never been analyzed before using relational tools such as Network Analysis. Our approach to biogeographic analysis consists of two parts: first the sympatry inference and second the network analysis method (NAM). The sympatry inference method was designed to propose sympatry hypothesis, constructing a basal sympatry network based on punctual data, independent of a priori distributional area determination. In this way, two or more species are considered sympatric when there is interpenetration and relative proximity among their records of occurrence. In nature, groups of species presenting within-group sympatry and between-group allopatry constitute natural units (units of co-occurrence). These allopatric units are usually connected by intermediary species. The network analysis method (NAM) that we propose here is based on the identification and removal of intermediary species to segregate units of co-occurrence, using the betweenness measure and the clustering coefficient. The species ranges of the units of co-occurrence obtained are transferred to a map, being considered as candidates to areas of endemism. The new approach was implemented on three different real complex data sets (one of them a classic example previously used in biogeography) resulting in (1) independence of predefined spatial units; (2) definition of co-occurrence patterns from the sympatry network structure, not from species range similarities; (3) higher stability in results despite scale changes; (4) identification of candidates to areas of endemism supported by strictly endemic species; (5) identification of intermediary species with particular biological attributes.