We urgently need to predict species responses to climate change to minimize future biodiversity loss and ensure we do not waste limited resources on ineffective conservation strategies. Currently, most predictions of species responses to climate change ignore the potential for evolution. However, evolution can alter species ecological responses, and different aspects of evolution and ecology can interact to produce complex ecoevolutionary dynamics under climate change. Here we review how evolution could alter ecological responses to climate change on species warm and cool range margins, where evolution could be especially important. We discuss different aspects of evolution in isolation, and then synthesize results to consider how multiple evolutionary processes might interact and affect conservation strategies. On species cool range margins, the evolution of dispersal could increase range expansion rates and allow species to adapt to novel conditions in their new range. However, low genetic variation and genetic drift in small range-front populations could also slow or halt range expansions. Together, these eco-evolutionary effects could cause a three-step, stop-and-go expansion pattern for many species. On warm range margins, isolation among populations could maintain high genetic variation that facilitates evolution to novel climates and allows species to persist longer than expected without evolution. This 'evolutionary extinction debt' could then prevent other species from shifting their ranges. However, as climate change increases isolation among populations, increasing dispersal mortality could select for decreased dispersal and cause rapid range contractions. Some of these eco-evolutionary dynamics could explain why many species are not responding to climate change as predicted. We conclude by suggesting that resurveying historical studies that measured trait frequencies, the strength of selection, or heritabilities could be an efficient way to increase our eco-evolutionary knowledge in climate change biology. ) and M. C. Urban (https://orcid.org/0000-0003-3962-4091),

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Accurately predicting biological impacts of climate change is necessary to guide policy. However, the resolution of climate data could be affecting the accuracy of climate change impact assessments. Here, we review the spatial and temporal resolution of climate data used in impact assessments and demonstrate that these resolutions are often too coarse relative to biologically relevant scales. We then develop a framework that partitions climate into three important components: trend, variance, and autocorrelation. We apply this framework to map different global climate regimes and identify where coarse climate data is most and least likely to reduce the accuracy of impact assessments. We show that impact assessments for many large mammals and birds use climate data with a spatial resolution similar to the biologically relevant area encompassing population dynamics. Conversely, impact assessments for many small mammals, herpetofauna, and plants use climate data with a spatial resolution that is orders of magnitude larger than the area encompassing population dynamics. Most impact assessments also use climate data with a coarse temporal resolution. We suggest that climate data with a coarse spatial resolution is likely to reduce the accuracy of impact assessments the most in climates with high spatial trend and variance (e.g., much of western North and South America) and the least in climates with low spatial trend and variance (e.g., the Great Plains of the USA). Climate data with a coarse temporal resolution is likely to reduce the accuracy of impact assessments the most in the northern half of the northern hemisphere where temporal climatic variance is high. Our framework provides one way to identify where improving the resolution of climate data will have the largest impact on the accuracy of biological predictions under climate change.

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Large flood events were part of the historical disturbance regime within the lower basin of most large river systems around the world. Large flood events are now rare in the lower basins of most large river systems due to flood control structures. Endemic organisms that are adapted to this historical disturbance regime have become less abundant due to these dramatic changes in the hydrology and the resultant changes in vegetation structure. The Yuma Clapper Rail is a federally endangered bird that breeds in emergent marshes within the lower Colorado River basin in the southwestern United States and northwestern Mexico. We evaluated whether prescribed fire could be used as a surrogate disturbance event to help restore historical conditions for the benefit of Yuma Clapper Rails and four sympatric marsh-dependent birds. We conducted call-broadcast surveys for marsh birds within burned and unburned (control) plots both pre- and post-burn. Fire increased the numbers of Yuma Clapper Rails and Virginia Rails, and did not affect the numbers of Black Rails, Soras, and Least Bitterns. We found no evidence that detection probability of any of the five species differed between burn and control plots. Our results suggest that prescribed fire can be used to set back succession of emergent marshlands and help mimic the natural disturbance regime in the lower Colorado River basin. Hence, prescribed fire can be used to help increase Yuma Clapper Rail populations without adversely affecting sympatric species. Implementing a coordinated long-term fire management plan within marshes of the lower Colorado River may allow regulatory agencies to remove the Yuma Clapper Rail from the endangered species list.

Standardized protocols that include the use of call-broadcast have recently been proposed for marsh birds in North America. We used data from point-count surveys collected across North America over eight years to evaluate the extent to which each of 13 focal marsh bird species responded to conspecific and heterospecific call-broadcast relative to passive survey methods. Surveyors detected more individuals during the 1-minute of conspecific callbroadcast compared to each of the five 1-minute passive segments and all of the 1-minute heterospecific callbroadcast segments for all species. Surveyors also detected more individuals during most of the 1-minute heterospecific call-broadcast segments compared to the 1-minute passive segments. Most birds responded to conspecific callbroadcast quickly (within 1 min), but we found some evidence for a lag time in birds' response to conspecific calls. The percent increase in the number of birds detected as a result of conspecific call-broadcast (relative to passive survey methods) varied between 14% (American bitterns [Botaurus lentiginosus]) to 632% (purple gallinules [Porphyrula martinica]). We recommend the use of point-count surveys that include both passive and call-broadcast segments for numerous marsh birds when collecting data to assess occupancy, evaluate the effects of wetland management practices, and estimate population trends of marsh birds.

1. Understanding patterns of diversity is central to ecology and conservation, yet estimates of diversity are often biased by imperfect detection. In recent years, multi-species occupancy models (MSOM) have been developed as a statistical tool to account for species-specific heterogeneity in detection while estimating true measures of diversity. Although the power of these models has been tested in various ways, their ability to estimate gamma diversity-or true community size, N is a largely unrecognized feature that needs rigorous evaluation.2. We use both simulations and an empirical dataset to evaluate the bias, precision, accuracy and coverage of estimates of N from MSOM compared to the widely applied iChao2 non-parametric estimator. We simulated 5,600 datasets across seven scenarios of varying average occupancy and detectability covariates, as well as varying numbers of sites, replicates and true community size. Additionally, we use a real dataset of surveys over 9 years (where species accumulation reached an asymptote, indicating true N), to estimate N from each annual survey.3. Simulations showed that both MSOM and iChao2 estimators are generally accurate (i.e. unbiased and precise) except under unideal scenarios where mean species occupancy is low. In such scenarios, MSOM frequently overestimated N. Across all scenarios, MSOM estimates were less certain than iChao2, but this led to over-confident iChao2 estimates that showed poor coverage. Results from the real dataset largely confirmed the simulation findings, with MSOM estimates showing greater accuracy and coverage than iChao2. Community ecologists have a wide choice of analytical methods, and both iChao2and MSOM estimates of N are substantially preferable to raw species counts. The simplicity of non-parametric estimators has obvious advantages, but our results show that in many cases, MSOM may provide superior estimates that also account more accurately for uncertainty. Both methods can show strong bias when average occupancy is very low, and practitioners should show caution when using estimates derived from either method under such conditions. K E Y W O R D SChao estimator, community richness, data augmentation, gamma diversity, imperfect detection, multi-species occupancy models, occupancy modelling S U PP O RTI N G I N FO R M ATI O NAdditional supporting information may be found online in the Supporting Information section.

Context The most common methods to estimate detection probability during avian point-count surveys involve recording a distance between the survey point and individual birds detected during the survey period. Accurately measuring or estimating distance is an important assumption of these methods; however, this assumption is rarely tested in the context of aural avian point-count surveys. Aims We expand on recent bird-simulation studies to document the error associated with estimating distance to calling birds in a wetland ecosystem. Methods We used two approaches to estimate the error associated with five surveyor’s distance estimates between the survey point and calling birds, and to determine the factors that affect a surveyor’s ability to estimate distance. Key results We observed biased and imprecise distance estimates when estimating distance to simulated birds in a point-count scenario (error = –9 m, s.d.error = 47 m) and when estimating distances to real birds during field trials (error = 39 m, s.d.error = 79 m). The amount of bias and precision in distance estimates differed among surveyors; surveyors with more training and experience were less biased and more precise when estimating distance to both real and simulated birds. Three environmental factors were important in explaining the error associated with distance estimates, including the measured distance from the bird to the surveyor, the volume of the call and the species of bird. Surveyors tended to make large overestimations to birds close to the survey point, which is an especially serious error in distance sampling. Conclusions Our results suggest that distance-estimation error is prevalent, but surveyor training may be the easiest way to reduce distance-estimation error. Implications The present study has demonstrated how relatively simple field trials can be used to estimate the error associated with distance estimates used to estimate detection probability during avian point-count surveys. Evaluating distance-estimation errors will allow investigators to better evaluate the accuracy of avian density and trend estimates. Moreover, investigators who evaluate distance-estimation errors could employ recently developed models to incorporate distance-estimation error into analyses. We encourage further development of such models, including the inclusion of such models into distance-analysis software.