Higher body condition with infection by Haemoproteus parasites in Bananaquits (Coereba flaveola).

Parasite transmission is a heterogenous process in host-parasite interactions. This heterogeneity is particularly apparent in vector-borne parasite transmission where the vector adds an additional level of complexity. Haemosporidian parasites, a widespread protist, cause a malaria-like disease in birds globally, but we still have much to learn about the consequences of infection to hosts' health. In the Caribbean, where malarial parasites are endemic, studying host-parasites interactions may give us important insights about energetic trade-offs involved in malarial parasites infections in birds. In this study, we tested the consequences of Haemoproteus infection on the Bananaquit, a resident species of Puerto Rico. We also tested for potential sources of individual heterogeneity in the consequences of infection such as host age and sex. To quantify the consequences of infection to hosts' health we compared three complementary body condition indices between infected and uninfected individuals. Our results showed that Bananaquits infected by Haemoproteus had higher body condition than uninfected individuals. This result was consistent among the three body condition indices. Still, we found no clear evidence that this effect was mediated by host age or sex. We discuss a set of non-mutually exclusive hypotheses that may explain this pattern including metabolic syndrome, immunological responses leading to host tolerance or resistance to infection, and potential changes in consumption rates. Overall, our results suggest that other mechanisms, may drive the consequences of avian malarial infection.

Geographic differences in body size distributions underlie food web connectance of tropical forest mammals.

Understanding variation in food web structure over large spatial scales is an emerging research agenda in food web ecology. The density of predator-prey links in a food web (i.e., connectance) is a key measure of network complexity that describes the mean proportional dietary breadth of species within a food web. Connectance is a critical component of food web robustness to species loss: food webs with lower connectance have been shown to be more susceptible to secondary extinctions. Identifying geographic variation in food web connectance and its drivers may provide insight into community robustness to species loss. We investigated the food web connectance of ground-dwelling tropical forest mammal communities in multiple biogeographic regions to test for differences among regions in food web connectance and to test three potential drivers: primary productivity, contemporary anthropogenic pressure, and variation in mammal body mass distributions reflective of historical extinctions. Mammal communities from fifteen protected forests throughout the Neo-, Afro-, and Asian tropics were identified from systematic camera trap arrays. Predator-prey interaction data were collected from published literature, and we calculated connectance for each community as the number of observed predator-prey links relative to the number of possible predator-prey links. We used generalized linear models to test for differences among regions and to identify the site level characteristics that best predicted connectance. We found that mammal food web connectance varied significantly among continents and that body size range was the only significant predictor. More possible predator-prey links were observed in communities with smaller ranges in body size and therefore sites with smaller body size ranges had higher mean proportional dietary breadth. Specifically, mammal communities in the Neotropics and in Madagascar had significantly higher connectance than mammal communities in Africa. This geographic variation in contemporary mammalian food web structure may be the product of historical extinctions in the Late Quaternary, which led to greater losses of large-bodied species in the Neotropics and Madagascar thus contributing to higher average proportional dietary breadth among the remaining smaller bodied species in these regions.

Recolonization of secondary forests by a locally extinct Caribbean anole through the lens of range expansion theory.

Disturbance and recovery dynamics are characteristic features of many ecosystems. Disturbance dynamics are widely studied in ecology and conservation biology. Still, we know less about the ecological processes that drive ecosystem recovery. The ecological processes that mediate ecosystem recovery stand at the intersection of many theoretical frameworks. Range expansion theory is one of these complementary frameworks that can provide unique insights into the population-level processes that mediate ecosystem recovery, particularly fauna recolonization. Although the biodiversity patterns that follow the fauna recolonization of recovering forests have been well described in the literature, the ecological processes at the population level that drive these patterns remain conspicuously unknown. In this study, we tested three fundamental predictions of range expansion theory during the recolonization of recovering forests in Puerto Rico by a shade specialist anole, Anolis gundlachi. Range expansion theory predicts that individuals at the early stages of recolonization (i.e., younger forests) would have a high prevalence of dispersive traits, experience less density dependence, and suffer less parasitism. To test these predictions, we conducted a chronosequence study applying space-for-time substitution where we compared phenotypic traits (i.e., body size, body condition, and relative limb size), population density, population growth rates, and Plasmodium parasitism rates among lizard populations living in young (<30 years), mid (~40-70 years), and old-growth forests (>75 years). Lizard populations in younger forests had lower densities, higher population growth rates, and lower rates of Plasmodium parasitism compared with old-growth forests. Still, while we found that individuals had larger body sizes, and longer forelimbs in young forests in one site, this result was not consistent among sites. This suggests a potential trade-off between the traits that provide a dispersal advantage during the initial stages of recolonization and those that are advantageous to establish in novel environmental conditions. Overall, our study emphasizes the suitability of range expansion theory to describe fauna recolonization but also highlights that the ecological processes that drive recolonization are time-dependent, complex, and nuanced.

No evidence of predicted phenotypic changes after hurricane disturbance in a shade-specialist Caribbean anole.

Extreme climatic events (ECEs) such as hurricanes have been hypothesized to be a major driving force of natural selection. Recent studies argue that, following strong hurricane disturbance, Anolis lizards in the Caribbean undergo selection for traits such as longer forelimbs or smaller body sizes that improve their clinging ability to their substrates increasing their chances of surviving hurricane wind gusts. Some authors challenge the generalization of this hypothesis arguing that other mechanisms may explain these phenotypic changes or that they may not necessarily be generalizable across systems. To address this issue, we compared body size and relative forelimb length of Anolis gundlachi, a trunk-ground anole living in closed-canopy forests in Puerto Rico, before, four months after, and 15 months after Hurricanes Irma and Maria in 2017. Overall, our results show no clear evidence of a temporal decrease in body size or increase forelimb length (relative to body size) challenging the generalizability of the clinging ability hypothesis. Understanding how animals adapt to ECE is an emerging field. Still, we are quickly learning that this process is complex and nuanced.

Modulation of motor behavior by the mesencephalic locomotor region.

The mesencephalic locomotor region (MLR) serves as an interface between higher-order motor systems and lower motor neurons. The excitatory module of the MLR is composed of the pedunculopontine nucleus (PPN) and the cuneiform nucleus (CnF), and their activation has been proposed to elicit different modalities of movement. However, how the differences in connectivity and physiological properties explain their contributions to motor activity is not well known. Here we report that CnF glutamatergic neurons are more electrophysiologically homogeneous than PPN neurons and have mostly short-range connectivity, whereas PPN glutamatergic neurons are heterogeneous and maintain long-range connections, most notably with the basal ganglia. Optogenetic activation of CnF neurons produces short-lasting muscle activation, driving involuntary motor activity. In contrast, PPN neuron activation produces long-lasting increases in muscle tone that reduce motor activity and disrupt gait. Our results highlight biophysical and functional attributes among MLR neurons that support their differential contribution to motor behavior.

Teaching quantitative ecology online: An evidence-based prescription of best practices.

Quantitative skills are becoming central to the undergraduate and graduate curriculum in ecology and evolutionary biology. While previous studies acknowledge that students perceive their quantitative training to be inadequate, there is little guidance on best practices. Moreover, with the recent COVID-19 sudden transition to online learning, there is even less guidance on how to effectively teach quantitative ecology online. Here, I synthesize a prescription of pedagogical best practices for teaching quantitative ecology online based on a broad review of the literature on multiple quantitative disciplines. These best practices include the following: (1) design and implement the class to meet learning goals using online strategies specifically; (2) create an open, inclusive, and welcoming online environment that promotes a sense of learning community; (3) acknowledge the diversity of talents and learning strategies; (4) use real-world examples and assessments; (5) account for gaps in knowledge; (6) emphasize the modeling cycle process; (7) focus on developing ideas rather than tools or procedures; (8) if needed, introduce computational tools thoroughly before combining them with mathematical or statistical concepts; (9) evaluate the course constantly; and (10) put your heart and soul into the class. I hope these practices help fellow instructors of quantitative ecology facing similar challenges in providing our students with the knowledge and skills needed to meet the challenges of the future.

Towards a unified framework for connectivity that disentangles movement and mortality in space and time.

Predicting connectivity, or how landscapes alter movement, is essential for understanding the scope for species persistence with environmental change. Although it is well known that movement is risky, connectivity modelling often conflates behavioural responses to the matrix through which animals disperse with mortality risk. We derive new connectivity models using random walk theory, based on the concept of spatial absorbing Markov chains. These models decompose the role of matrix on movement behaviour and mortality risk, can incorporate species distribution to predict the amount of flow, and provide both short- and long-term analytical solutions for multiple connectivity metrics. We validate the framework using data on movement of an insect herbivore in 15 experimental landscapes. Our results demonstrate that disentangling the roles of movement behaviour and mortality risk is fundamental to accurately interpreting landscape connectivity, and that spatial absorbing Markov chains provide a generalisable and powerful framework with which to do so.

Virulence-driven trade-offs in disease transmission: A meta-analysis.

The virulence-transmission trade-off hypothesis proposed more than 30 years ago is the cornerstone in the study of host-parasite co-evolution. This hypothesis rests on the premise that virulence is an unavoidable and increasing cost because the parasite uses host resources to replicate. This cost associated with replication ultimately results in a deceleration in transmission rate because increasing within-host replication increases host mortality. Empirical tests of predictions of the hypothesis have found mixed support, which cast doubt about its overall generalizability. To quantitatively address this issue, we conducted a meta-analysis of 29 empirical studies, after reviewing over 6000 published papers, addressing the four core relationships between (1) virulence and recovery rate, (2) within-host replication rate and virulence, (3) within-host replication and transmission rate, and (4) virulence and transmission rate. We found strong support for an increasing relationship between replication and virulence, and replication and transmission. Yet, it is still uncertain if these relationships generally decelerate due to high within-study variability. There was insufficient data to quantitatively test the other two core relationships predicted by the theory. Overall, the results suggest that the current empirical evidence provides partial support for the trade-off hypothesis, but more work remains to be done.

The drivers and consequences of unstable Plasmodium dynamics: a long-term study of three malaria parasite species infecting a tropical lizard.

Understanding the consequences of environmental fluctuations for parasite dynamics requires a long-term view stretching over many transmission cycles. Here we studied the dynamics of three malaria parasites (Plasmodium azurophilum, P. leucocytica and P. floridense) infecting the lizard Anolis gundlachi, in the rainforest of Puerto Rico. In this malaria-anole system we evaluated temporal fluctuations in individual probability of infection, the environmental drivers of observed variation and consequences for host body condition and Plasmodium parasites assemblage. We conducted a total of 15 surveys including 10 from 1990 to 2002 and five from 2015 to 2017. During the early years, a lizard's probability of infection by all Plasmodium species appeared stable despite disturbances ranging from two hurricanes to short droughts. Over a longer timescale, probability of infection and overall prevalence varied significantly, following non-linear relationships with temperature and rainfall such that highest prevalence is expected at intermediate climate measures. A perplexing result was that host body condition was maximized at intermediate levels of rainfall and/or temperature (when risk of infection was highest), yet we found no significant decreases in body condition due to infection. Plasmodium parasite species composition varied through time with a reduction and near local extinction of P. floridense. Our results emphasize the need for long-term studies to reveal host-parasite dynamics, their drivers and consequences.

Spatial heterogeneity, host movement and mosquito-borne disease transmission.

Mosquito-borne diseases are a global health priority disproportionately affecting low-income populations in tropical and sub-tropical countries. These pathogens live in mosquitoes and hosts that interact in spatially heterogeneous environments where hosts move between regions of varying transmission intensity. Although there is increasing interest in the implications of spatial processes for mosquito-borne disease dynamics, most of our understanding derives from models that assume spatially homogeneous transmission. Spatial variation in contact rates can influence transmission and the risk of epidemics, yet the interaction between spatial heterogeneity and movement of hosts remains relatively unexplored. Here we explore, analytically and through numerical simulations, how human mobility connects spatially heterogeneous mosquito populations, thereby influencing disease persistence (determined by the basic reproduction number R0), prevalence and their relationship. We show that, when local transmission rates are highly heterogeneous, R0 declines asymptotically as human mobility increases, but infection prevalence peaks at low to intermediate rates of movement and decreases asymptotically after this peak. Movement can reduce heterogeneity in exposure to mosquito biting. As a result, if biting intensity is high but uneven, infection prevalence increases with mobility despite reductions in R0. This increase in prevalence decreases with further increase in mobility because individuals do not spend enough time in high transmission patches, hence decreasing the number of new infections and overall prevalence. These results provide a better basis for understanding the interplay between spatial transmission heterogeneity and human mobility, and their combined influence on prevalence and R0.

Spatial asymmetries in connectivity influence colonization-extinction dynamics.

Movement has broad implications for many areas of biology, including evolution, community and population ecology. Movement is crucial in metapopulation ecology because it facilitates colonization and reduces the likelihood of local extinction via rescue effects. Most metapopulation modeling approaches describe connectivity using pair-wise Euclidean distances resulting in the simplifying assumption of a symmetric connectivity pattern. Yet, assuming symmetric connectivity when populations show net asymmetric movement patterns may result in biased estimates of colonization and extinction, and may alter interpretations of the dynamics and conclusions regarding the viability of metapopulations. Here, we use a 10-year time series on a wind-dispersed orchid Lepanthes rupestris that anchors its roots in patches of moss growing on trees or boulders along streams, to test for the role of connectivity asymmetries in explaining the colonization-extinction dynamics of this orchid in a network of 975 patches. We expected that wind direction could highly alter dispersal direction in this orchid. To account for this potential asymmetry, we modified the connectivity measure traditionally used in metapopulation models to allow for asymmetric effective distances between patches and subsequently estimated colonization and extinction probabilities using a dynamic occupancy modeling approach. Asymmetric movement was prevalent in the L. rupestris metapopulation and incorporating potential dispersal asymmetries resulted in higher colonization estimates in larger patches and more accurate models. Accounting for dispersal asymmetries may reveal connectivity effects where they were previously assumed to be negligible and may provide more reliable conclusions regarding the role of connectivity in patch dynamics.

The matrix alters the role of path redundancy on patch colonization rates.

Landscape connectivity is central to many problems in ecology and conservation. Recently, the role of path redundancies on movement of organisms has been emphasized for understanding connectivity, because increasing the number of potential paths (i.e., redundancy) is predicted to increase movement rates, which can alter predictions for foraging theory and population dynamics. Nonetheless, experiments that test for the effects of path redundancies on connectivity remain scarce. We tested for the role of path redundancies on the movements of a habitat specialist, Chelinidea vittiger, using experimental arenas that altered path redundancy by varying the amount and configuration of stepping stones across a gradient of matrix resistance. We found that stepping-stone redundancies increased colonization rates to target patches, but the effects differed depending on the configuration of redundancy and the structure of the matrix. In addition, matrix effects were better explained through the use of effective distance measures that incorporate redundancy in the matrix than those that ignore redundancy. Our results provide experimental evidence that measures that ignore redundancies may be inadequate for capturing functional connectivity, illustrate the ways in which redundancies alter colonization rates, and emphasize how habitat configuration and matrix structure can interact to guide movement of individuals across landscapes.

Place-based attributes predict community membership in a mobile phone communication network.

Social networks can be organized into communities of closely connected nodes, a property known as modularity. Because diseases, information, and behaviors spread faster within communities than between communities, understanding modularity has broad implications for public policy, epidemiology and the social sciences. Explanations for community formation in social networks often incorporate the attributes of individual people, such as gender, ethnicity or shared activities. High modularity is also a property of large-scale social networks, where each node represents a population of individuals at a location, such as call flow between mobile phone towers. However, whether or not place-based attributes, including land cover and economic activity, can predict community membership for network nodes in large-scale networks remains unknown. We describe the pattern of modularity in a mobile phone communication network in the Dominican Republic, and use a linear discriminant analysis (LDA) to determine whether geographic context can explain community membership. Our results demonstrate that place-based attributes, including sugar cane production, urbanization, distance to the nearest airport, and wealth, correctly predicted community membership for over 70% of mobile phone towers. We observed a strongly positive correlation (r = 0.97) between the modularity score and the predictive ability of the LDA, suggesting that place-based attributes can accurately represent the processes driving modularity. In the absence of social network data, the methods we present can be used to predict community membership over large scales using solely place-based attributes.

Social network models predict movement and connectivity in ecological landscapes.

Network analysis is on the rise across scientific disciplines because of its ability to reveal complex, and often emergent, patterns and dynamics. Nonetheless, a growing concern in network analysis is the use of limited data for constructing networks. This concern is strikingly relevant to ecology and conservation biology, where network analysis is used to infer connectivity across landscapes. In this context, movement among patches is the crucial parameter for interpreting connectivity but because of the difficulty of collecting reliable movement data, most network analysis proceeds with only indirect information on movement across landscapes rather than using observed movement to construct networks. Statistical models developed for social networks provide promising alternatives for landscape network construction because they can leverage limited movement information to predict linkages. Using two mark-recapture datasets on individual movement and connectivity across landscapes, we test whether commonly used network constructions for interpreting connectivity can predict actual linkages and network structure, and we contrast these approaches to social network models. We find that currently applied network constructions for assessing connectivity consistently, and substantially, overpredict actual connectivity, resulting in considerable overestimation of metapopulation lifetime. Furthermore, social network models provide accurate predictions of network structure, and can do so with remarkably limited data on movement. Social network models offer a flexible and powerful way for not only understanding the factors influencing connectivity but also for providing more reliable estimates of connectivity and metapopulation persistence in the face of limited data.