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Turnover in species composition between sites, or beta diversity, is a critical component of species diversity that is typically influenced by geography, environment, and biotic interactions. Quantifying turnover is particularly challenging, however, in multi-host, multi-parasite assemblages where undersampling is unavoidable, resulting in inflated estimates of turnover and uncertainty about its spatial scale. We developed and implemented a framework using null models to test for community turnover in avian haemosporidian communities of three sky islands in the southwestern United States. We screened 776 birds for haemosporidian parasites from three genera (Parahaemoproteus, Plasmodium, and Leucocytozoon) by amplifying and sequencing a mitochondrial DNA barcode. We detected infections in 280 birds (36.1%), sequenced 357 infections, and found a total of 99 parasite haplotypes. When compared to communities simulated from a regional pool, we observed more unique, single-mountain haplotypes and fewer haplotypes shared among three mountain ranges than expected, indicating that haemosporidian communities differ to some degree among adjacent mountain ranges. These results were robust even after pruning datasets to include only identical sets of host species, and they were consistent for two of the three haemosporidian genera. The two more distant mountain ranges were more similar to each other than the one located centrally, suggesting that the differences we detected were due to stochastic colonization–extirpation dynamics. These results demonstrate that avian haemosporidian communities of temperate-zone forests differ on relatively fine spatial scales between adjacent sky islands. Null models are essential tools for testing the spatial scale of turnover in complex, undersampled, and poorly known systems.

Molecular docking prediction, used to design new drugs and identify biological function, can be represented as a motion planning problem in high dimensional space. This complex task can benefit from human guidance, i.e., interactive simulations combining visual and haptic feedback from atomic forces [4]. Using human-generated data to find solutions to complex problems has been used in protein folding with FoldIt [3], showing that crowdsourced data collection is viable to help solve difficult problems in biology. However, existing interactive molecular docking systems that typically use haptic devices employ devices that are not widely available to the public, limiting the potential to crowdsource solutions [1]. This user study tested 4 input devices to find potentially bound ligand-receptor states and biologically feasible paths via motion planning. On a PC laptop, players used one of the following: a 6 degree-of-freedom (DOF) haptic feedback device, a 3 DOF haptic feedback device, a game controller with vibration feedback, and a mouse and keyboard, with our in-house interactive rigid body molecular docking program, DockAnywhere [1, 2]. Players tried to dock a known inhibitor of HIV Protease (PDB ID 1AJX). The goal is to move the ligand around the receptor to find the lowest potential energy state possible. Players get feedback as a positive integer score based on the potential energy and, for haptic devices, a force feedback calculated from atomic forces. During gameplay, ligand positions and orientations are recorded as it is moved through the environment. In total, 439,832 states were collected from 32 players (8 per device). These states were divided into 4 sets, one per device. and Motion planning queries found a feasible path to the goal state in all sets, with the mouse showing more pronounced energy barriers. We found that exploration varied among different devices, with players on haptic devices exploring the interaction energy landscape more uniformly. However, all 4 devices yielded low energy ligand states with comparable values and similar closest distance to the binding site. In summary, force feedback showed no clear improvement to finding low potential energy states or getting closer to the known binding state. However, haptic devices appear to enable a more thorough exploration of the state space, creating more samples and generating smoother paths. The nature of this guidance should be the subject of future investigation.

Turnover in species composition between sites, or beta diversity, is a critical component of species diversity that is typically influenced by geography, environment, and biotic interactions. Quantifying turnover is particularly challenging, however, in multi-host, multi-parasite assemblages where undersampling is unavoidable, resulting in inflated estimates of turnover and uncertainty about its spatial scale. We developed and implemented a framework using null models to test for community turnover in avian haemosporidian communities of three sky islands in the southwestern United States. We screened 776 birds for haemosporidian parasites from three genera (Parahaemoproteus, Plasmodium, and Leucocytozoon) by amplifying and sequencing a mitochondrial DNA barcode. We detected infections in 280 birds (36.1%), sequenced 357 infections, and found a total of 99 parasite haplotypes. When compared to communities simulated from a regional pool, we observed more unique, single-mountain haplotypes and fewer haplotypes shared among three mountain ranges than expected, indicating that haemosporidian communities differ to some degree among adjacent mountain ranges. These results were robust even after pruning datasets to include only identical sets of host species, and they were consistent for two of the three haemosporidian genera. The two more distant mountain ranges were more similar to each other than the one located centrally, suggesting that the differences we detected were due to stochastic colonization-extirpation dynamics. These results demonstrate that avian haemosporidian communities of temperate-zone forests differ on relatively fine spatial scales associated with adjacent sky-islands. Null models are essential tools for detecting turnover in complex, undersampled, and poorly known systems.

Turnover in species composition between sites, or beta diversity, is a critical component of species diversity that is typically influenced by geography, environment, and biotic interactions. Quantifying turnover is particularly challenging, however, in multi-host, multi-parasite assemblages where undersampling is unavoidable, resulting in inflated estimates of turnover and uncertainty about its spatial scale. We developed and implemented a framework using null models to test for community turnover in avian haemosporidian communities of three sky islands in the southwestern United States. We screened 776 birds for haemosporidian parasites from three genera (Parahaemoproteus, Plasmodium, and Leucocytozoon) by amplifying and sequencing a mitochondrial DNA barcode. We detected infections in 280 birds (36.1%), sequenced 357 infections, and found a total of 99 parasite haplotypes. When compared to communities simulated from a regional pool, we observed more unique, single-mountain haplotypes and fewer haplotypes shared among three mountain ranges than expected, indicating that haemosporidian communities differ to some degree among adjacent mountain ranges. These results were robust even after pruning datasets to include only identical sets of host species, and they were consistent for two of the three haemosporidian genera. The two more distant mountain ranges were more similar to each other than the one located centrally, suggesting that the differences we detected were due to stochastic colonization-extirpation dynamics. These results demonstrate that avian haemosporidian communities of temperate-zone forests differ on relatively fine spatial scales between adjacent sky islands. Null models are essential tools for testing the spatial scale of turnover in complex, undersampled, and poorly known systems.

Avian malaria parasites (genera Haemoproteus, Plasmodium, and Leucocytozoon) affect bird demography, distribution limits, and community structure, yet most bird communities and populations remain unsurveyed. We conducted a community-level survey of these vector-transmitted parasites in New Mexico, USA, to describe the diversity, abundance, and host associations. We focused on the breeding-bird community in the transition zone between piñon-juniper woodland and ponderosa pine forests (elevational range: 2150–2460 meters). We screened 186 birds representing 49 species using both standard PCR and microscopy techniques to detect infections of all three avian malaria genera. The combined infection rate was 36.6%, with the highest infection rate for Haemoproteus (20.9%), followed by Leucocytozoon (13.4%), then Plasmodium (8.0%). We sequenced mtDNA for 77 infections representing 43 haplotypes (25 Haemoproteus, 12 Leucocytozoon, 6 Plasmodium). When compared to all previously known lineages in the MalAvi and GenBank databases, 65% (28) of the haplotypes that we recovered were novel. We found evidence for host specificity at the avian clade and species level, but this specificity was variable among parasite genera. Haemoproteus and Leucocytozoon were each restricted to three avian host-clades or groups (out of six), while Plasmodium occurred in all groups except non-passerines. We found striking variation in infection rate among host species, with nearly universal infection among vireos and no infection among nuthatches. Using rarefaction and extrapolation, we estimated the total avian malaria diversity to be 70 haplotypes (95% CI: 43–98); thus, we may have already sampled ~60% of the diversity of avian malaria in New Mexico pine forests. It is possible that future studies will find higher diversity in microhabitats or host species that are under-sampled or unsampled in the present study. Fortunately, this study is fully extendable via voucher specimens, frozen tissues, blood smears, parasite images, and documentation provided in open-access databases (MalAvi, Genbank, and ARCTOS).