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  1. Abstract

    How species richness scales spatially is a foundational concept of community ecology, but how biotic interactions scale spatially is poorly known. Previous studies have proposed interactions-area relationships (IARs) based on two competing relationships for how the number of interactions scale with the number of species, the ‘link-species scaling law’ and the ‘constant connectance hypothesis.’ The link-species scaling law posits that the number of interactions per species remains constant as the size of the network increases. The constant connectance hypothesis says that the proportion of realized interactions remains constant with network size. While few tests of these IARs exist, evidence for the original interactions-species relationships are mixed. We propose a novel IAR and test it against the two existing IARs. We first present a general theory for how interactions scale spatially and the mathematical relationship between the IAR and the species richness-area curve. We then provide a new mathematical formulation of the IAR, accounting for connectance varying with area. Employing data from three mutualistic networks (i.e. a network which specifies interconnected and mutually-beneficial interactions between two groups of species), we evaluate three competing models of how interactions scale spatially: two previously published IAR models and our proposed IAR. We find the new IAR described by our theory-based equation fits the empirical datasets equally as well as the previously proposed IAR based on the link-species scaling law in one out of three cases and better than the previously-proposed models in two out of three cases. Our novel IAR improves upon previous models and quantifies mutualist interactions across space, which is paramount to understanding biodiversity and preventing its loss.

     
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  2. Outdoor recreation benefits local economies, environmental education, and public health and wellbeing, but it can also adversely affect local ecosystems. Human presence in natural areas alters feeding and reproductive behaviors, physiology, and population structure in many wildlife species, often resulting in cascading effects through entire ecological communities. As outdoor recreation gains popularity, existing trails are becoming overcrowded and new trails are being built to accommodate increasing use. Many recreation impact studies have investigated effects of the presence or absence of humans while few have investigated recreation effects on wildlife using a gradient of disturbance intensity. We used camera traps to quantify trail use by humans and mid- to large-sized mammals in an area of intense outdoor recreation–the Upper East River Valley, Colorado, USA. We selected five trails with different types and intensities of human use and deployed six cameras on each trail for five weeks during a COVID-enhanced 2020 summer tourism season. We used occupancy models to estimate detectability and habitat use of the three most common mammal species in the study area and determined which human activities affect the habitat use patterns of each species. Human activities affected each species differently. Mule deer (Odocoileus hemionus) tended to use areas with more vehicles, more predators, and greater distances from the trailhead, and they were more likely to be detected where there were more bikers. Coyotes (Canis latrans) and red foxes (Vulpes vulpes) were most likely to use areas where their prey species occurred, and foxes were more likely to be detected where the vegetation was shorter. Humans and their recreational activities differentially influence different species. More generally, these results reinforce that it is unlikely that a single management policy is suitable for all species and management should thus be tailored for each target species.

     
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    Free, publicly-accessible full text available November 10, 2024
  3. Males, Jamie (Ed.)
    Mountains hold much of the world’s taxonomic diversity, but global climate change threatens this diversity by altering the distributions of montane species. While numerous studies have documented upslope shifts in elevational ranges, these patterns are highly variable across geographic regions and taxa. This variation in how species’ range shifts are manifesting along elevational gradients likely reflects the diversity of mechanisms that determines elevational ranges and modulates movements, and stands in contrast to latitudinal gradients, where range shifts show less variability and appear more predictable. Here, we review observed elevational range shifts in a single taxonomic group–birds–a group that has received substantial research attention and thus provides a useful context for exploring variability in range shifts while controlling for the mechanisms that drive range shifts across broader taxonomic groups. We then explore the abiotic and biotic factors that are known to define elevational ranges, as well as the constraints that may prevent birds from shifting. Across the literature, temperature is generally invoked as the prime driver of range shifts while the role of precipitation is more neglected. However, temperature is less likely to act directly on elevational ranges, instead mediating biotic factors such as habitat and food availability, predator activity, and parasite prevalence, which could in turn modulate range shifts. Dispersal ability places an intrinsic constraint on elevational range shifts, exacerbated by habitat fragmentation. While current research provides strong evidence for the importance of various drivers of elevational ranges and shifts, testing the relative importance of these factors and achieving a more holistic view of elevational gradients will require integration of expanding datasets, novel technologies, and innovative techniques. 
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  4. Changes in phenology in response to ongoing climate change have been observed in numerous taxa around the world. Differing rates of phenological shifts across trophic levels have led to concerns that ecological interactions may become increasingly decoupled in time, with potential negative consequences for populations. Despite widespread evidence of phenological change and a broad body of supporting theory, large-scale multitaxa evidence for demographic consequences of phenological asynchrony remains elusive. Using data from a continental-scale bird-banding program, we assess the impact of phenological dynamics on avian breeding productivity in 41 species of migratory and resident North American birds breeding in and around forested areas. We find strong evidence for a phenological optimum where breeding productivity decreases in years with both particularly early or late phenology and when breeding occurs early or late relative to local vegetation phenology. Moreover, we demonstrate that landbird breeding phenology did not keep pace with shifts in the timing of vegetation green-up over a recent 18-y period, even though avian breeding phenology has tracked green-up with greater sensitivity than arrival for migratory species. Species whose breeding phenology more closely tracked green-up tend to migrate shorter distances (or are resident over the entire year) and breed earlier in the season. These results showcase the broadest-scale evidence yet of the demographic impacts of phenological change. Future climate change–associated phenological shifts will likely result in a decrease in breeding productivity for most species, given that bird breeding phenology is failing to keep pace with climate change. 
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  5. Abstract

    Rare birds known as “accidentals” or “vagrants” have long captivated birdwatchers and puzzled biologists, but the drivers of these rare occurrences remain elusive. Errors in orientation or navigation are considered one potential driver: migratory birds use the Earth’s magnetic field—sensed using specialized magnetoreceptor structures—to traverse long distances over often unfamiliar terrain. Disruption to these magnetoreceptors or to the magnetic field itself could potentially cause errors leading to vagrancy. Using data from 2 million captures of 152 landbird species in North America over 60 years, we demonstrate a strong association between disruption to the Earth’s magnetic field and avian vagrancy during fall migration. Furthermore, we find that increased solar activity—a disruptor of the avian magnetoreceptor—generally counteracts this effect, potentially mitigating misorientation by disabling the ability for birds to use the magnetic field to orient. Our results link a hypothesized cause of misorientation to the phenomenon of avian vagrancy, further demonstrating the importance of magnetoreception among the orientation mechanisms of migratory birds. Geomagnetic disturbance may have important downstream ecological consequences, as vagrants may experience increased mortality rates or facilitate range expansions of avian populations and the organisms they disperse.

     
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  6. Abstract

    Rapid advances in the field of movement ecology have led to increasing insight into both the population‐level abundance patterns and individual‐level behaviour of migratory species. Despite this progress, research questions that require scaling individual‐level understanding of the behaviour of migrating organisms to the population level remain difficult to investigate.

    To bridge this gap, we introduce a generalizable framework for training full‐annual cycle individual‐based models of migratory movements by combining information from tracking studies and species occurrence records. Focusing on migratory birds, we call this method: Models of Individual Movement of Avian Species (MIMAS). We implement MIMAS to design individual‐based models of avian migration that are trained using previously published weekly occurrence maps and fit via Approximate Bayesian Computation.

    MIMAS models leverage individual‐ and population‐level information to faithfully represent continental‐scale migration patterns. Models can be trained successfully for species even when little existing individual‐level data is available for parameterization by relying on population‐level information. In contrast to existing mathematical models of migration, MIMAS explicitly represents and estimates behavioural attributes of migrants. MIMAS can additionally be used to simulate movement over consecutive migration seasons, and models can be easily updated or validated as new empirical data on migratory behaviours becomes available.

    MIMAS can be applied to a variety of research questions that require representing individual movement at large scales. We demonstrate three applied uses for MIMAS: estimating population‐specific migratory phenology, predicting the spatial patterns and magnitude of ectoparasite dispersal by migrants, and simulating the spread of a pathogen across the annual cycle of a migrant species. Currently, MIMAS can easily be used to build models for hundreds of migratory landbird species but can also be adapted in the future to build models of other types of migratory animals.

     
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  7. Abstract

    Correlative species distribution models are widely used to quantify past shifts in ranges or communities, and to predict future outcomes under ongoing global change. Practitioners confront a wide range of potentially plausible models for ecological dynamics, but most specific applications only consider a narrow set. Here, we clarify that certain model structures can embed restrictive assumptions about key sources of forecast uncertainty into an analysis. To evaluate forecast uncertainties and our ability to explain community change, we fit and compared 39 candidate multi‐ or joint species occupancy models to avian incidence data collected at 320 sites across California during the early 20th century and resurveyed a century later. We found massive (>20,000 LOOIC) differences in within‐time information criterion across models. Poorer fitting models omitting multivariate random effects predicted less variation in species richness changes and smaller contemporary communities, with considerable variation in predicted spatial patterns in richness changes across models. The top models suggested avian environmental associations changed across time, contemporary avian occupancy was influenced by previous site‐specific occupancy states, and that both latent site variables and species associations with these variables also varied over time. Collectively, our results recapitulate that simplified model assumptions not only impact predictive fit but may mask important sources of forecast uncertainty and mischaracterize the current state of system understanding when seeking to describe or project community responses to global change. We recommend that researchers seeking to make long‐term forecasts prioritize characterizing forecast uncertainty over seeking to present a single best guess. To do so reliably, we urge practitioners to employ models capable of characterizing the key sources of forecast uncertainty, where predictors, parameters and random effects may vary over time or further interact with previous occurrence states.

     
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