Range-wide sources of variation in reproductive rates of northern spotted owls.

We conducted a range-wide investigation of the dynamics of site-level reproductive rate of northern spotted owls using survey data from 11 study areas across the subspecies geographic range collected during 1993-2018. Our analytical approach accounted for imperfect detection of owl pairs and misclassification of successful reproduction (i.e., at least one young fledged) and contributed further insights into northern spotted owl population ecology and dynamics. Both nondetection and state misclassification were important, especially because factors affecting these sources of error also affected focal ecological parameters. Annual probabilities of site occupancy were greatest at sites with successful reproduction in the previous year and lowest for sites not occupied by a pair in the previous year. Site-specific occupancy transition probabilities declined over time and were negatively affected by barred owl presence. Overall, the site-specific probability of successful reproduction showed substantial year-to-year fluctuations and was similar for occupied sites that did or did not experience successful reproduction the previous year. Site-specific probabilities for successful reproduction were very small for sites that were unoccupied the previous year. Barred owl presence negatively affected the probability of successful reproduction by northern spotted owls in Washington and California, as predicted, but the effect in Oregon was mixed. The proportions of sites occupied by northern spotted owl pairs showed steep, near-monotonic declines over the study period, with all study areas showing the lowest observed levels of occupancy to date. If trends continue it is likely that northern spotted owls will become extirpated throughout large portions of their range in the coming decades.

An evidence-based approach to assessing the effectiveness of training regimen on athlete performance: Youth soccer as a case study.

Athletic performance data are modeled in an effort to better understand the relationship between both hours spent training and a measurement of "commitment" to that training, and improvements in performance. Both increased training time and greater commitment were predicted to produce larger increases in performance improvement, and commitment was predicted to be the more important determinant of improvement. The performance of 108 soccer players (ages 9-18) was quantified over a 10-week training program. Hours spent training ranged from 16 to 90 during the course of the study, while commitment scores ranged from 0.55 to 2.00, based on a scale from 0.00 to 2.40. A model selection approach was used to discriminate among models specifying relationships between training hours and improvement, and commitment and improvement. Despite considerable variability in the data, results provided strong evidence for an increase in performance improvement with both training hours and commitment score. The best models for hours and commitment were directly compared by computing an evidence ratio of 5799, indicating much stronger evidence favoring the model based on commitment. Results of analyses such as these go beyond anecdotal experience in an effort to establish a formal evidentiary basis for athletic training programs.

Synergistic interventions to control COVID-19: Mass testing and isolation mitigates reliance on distancing.

Stay-at-home orders and shutdowns of non-essential businesses are powerful, but socially costly, tools to control the pandemic spread of SARS-CoV-2. Mass testing strategies, which rely on widely administered frequent and rapid diagnostics to identify and isolate infected individuals, could be a potentially less disruptive management strategy, particularly where vaccine access is limited. In this paper, we assess the extent to which mass testing and isolation strategies can reduce reliance on socially costly non-pharmaceutical interventions, such as distancing and shutdowns. We develop a multi-compartmental model of SARS-CoV-2 transmission incorporating both preventative non-pharmaceutical interventions (NPIs) and testing and isolation to evaluate their combined effect on public health outcomes. Our model is designed to be a policy-guiding tool that captures important realities of the testing system, including constraints on test administration and non-random testing allocation. We show how strategic changes in the characteristics of the testing system, including test administration, test delays, and test sensitivity, can reduce reliance on preventative NPIs without compromising public health outcomes in the future. The lowest NPI levels are possible only when many tests are administered and test delays are short, given limited immunity in the population. Reducing reliance on NPIs is highly dependent on the ability of a testing program to identify and isolate unreported, asymptomatic infections. Changes in NPIs, including the intensity of lockdowns and stay at home orders, should be coordinated with increases in testing to ensure epidemic control; otherwise small additional lifting of these NPIs can lead to dramatic increases in infections, hospitalizations and deaths. Importantly, our results can be used to guide ramp-up of testing capacity in outbreak settings, allow for the flexible design of combined interventions based on social context, and inform future cost-benefit analyses to identify efficient pandemic management strategies.

What is the effect of poaching activity on wildlife species?

Poaching is a pervasive threat to wildlife, yet quantifying the direct effect of poaching on wildlife is rarely possible because both wildlife and threat data are infrequently collected concurrently. In this study, we used poaching data collected through the Management Information System (MIST) and wildlife camera trap data collected by the Tropical Ecology Assessment and Monitoring (TEAM) network from 2014 to 2017 in Volcanoes National Park, Rwanda. We implemented co-occurrence multi-season occupancy models that accounted for imperfect detection to investigate the effect of poaching on initial occupancy, colonization, and extinction of five mammal species. Specifically, we focused on two species of conservation concern (mountain gorilla [Gorilla beringei beringei] and golden monkey [Cercopithecus mitis kandti]), and three species targeted by poachers (black-fronted duiker [Cephalophus nigrifrons], bushbuck [Tragelaphus scriptus], and African buffalo [Syncerus caffer]). We found that the probability of local extinction was highest in sites with poaching activity for golden monkey and bushbuck. In addition, the probability of initial occupancy for golden monkey was highest in sites without poaching activity. We only found weak evidence of effects of poaching on parameters governing the occupancy dynamics of the other species. All species showed evidence of poaching presence affecting the probability of detection of the wildlife species. This is the first study to our knowledge to combine direct threat observations from ranger-based monitoring data with camera trap wildlife observations to quantify the effect of poaching on wildlife. Given the widespread collection of ranger-based monitoring and camera trap data, our approach is broadly applicable to numerous protected areas and has the potential to significantly improve conservation management. Specifically, the relationship between poaching activity and wildlife population dynamics can be combined with information on the relationship between ranger patrols and poaching activity to develop models useful for making wise decisions about ranger patrol deployment.

Invader removal triggers competitive release in a threatened avian predator.

Changes in the distribution and abundance of invasive species can have far-reaching ecological consequences. Programs to control invaders are common but gauging the effectiveness of such programs using carefully controlled, large-scale field experiments is rare, especially at higher trophic levels. Experimental manipulations coupled with long-term demographic monitoring can reveal the mechanistic underpinnings of interspecific competition among apex predators and suggest mitigation options for invasive species. We used a large-scale before-after control-impact removal experiment to investigate the effects of an invasive competitor, the barred owl (Strix varia), on the population dynamics of an iconic old-forest native species, the northern spotted owl (Strix occidentalis caurina). Removal of barred owls had a strong, positive effect on survival of sympatric spotted owls and a weaker but positive effect on spotted owl dispersal and recruitment. After removals, the estimated mean annual rate of population change for spotted owls stabilized in areas with removals (0.2% decline per year), but continued to decline sharply in areas without removals (12.1% decline per year). The results demonstrated that the most substantial changes in population dynamics of northern spotted owls over the past two decades were associated with the invasion, population expansion, and subsequent removal of barred owls. Our study provides experimental evidence of the demographic consequences of competitive release, where a threatened avian predator was freed from restrictions imposed on its population dynamics with the removal of a competitively dominant invasive species.

Strategic testing approaches for targeted disease monitoring can be used to inform pandemic decision-making.

More than 1.6 million Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) tests were administered daily in the United States at the peak of the epidemic, with a significant focus on individual treatment. Here, we show that objective-driven, strategic sampling designs and analyses can maximize information gain at the population level, which is necessary to increase situational awareness and predict, prepare for, and respond to a pandemic, while also continuing to inform individual treatment. By focusing on specific objectives such as individual treatment or disease prediction and control (e.g., via the collection of population-level statistics to inform lockdown measures or vaccine rollout) and drawing from the literature on capture-recapture methods to deal with nonrandom sampling and testing errors, we illustrate how public health objectives can be achieved even with limited test availability when testing programs are designed a priori to meet those objectives.

Partitioning global change: Assessing the relative importance of changes in climate and land cover for changes in avian distribution.

Understanding the relative impact of climate change and land cover change on changes in avian distribution has implications for the future course of avian distributions and appropriate management strategies. Due to the dynamic nature of climate change, our goal was to investigate the processes that shape species distributions, rather than the current distributional patterns. To this end, we analyzed changes in the distribution of Eastern Wood Pewees (Contopus virens) and Red-eyed Vireos (Vireo olivaceus) from 1997 to 2012 using Breeding Bird Survey data and dynamic correlated-detection occupancy models. We estimated the local colonization and extinction rates of these species in relation to changes in climate (hours of extreme temperature) and changes in land cover (amount of nesting habitat). We fit six nested models to partition the deviance explained by spatial and temporal components of land cover and climate. We isolated the temporal components of environmental variables because this is the essence of global change. For both species, model fit was significantly improved when we modeled vital rates as a function of spatial variation in climate and land cover. Model fit improved only marginally when we added temporal variation in climate and land cover to the model. Temporal variation in climate explained more deviance than temporal variation in land cover, although both combined only explained 20% (Eastern Wood Pewee) and 6% (Red-eyed Vireo) of temporal variation in vital rates. Our results showing a significant correlation between initial occupancy and environmental covariates are consistent with biological expectation and previous studies. The weak correlation between vital rates and temporal changes in covariates indicated that we have yet to identify the most relevant components of global change influencing the distributions of these species and, more importantly, that spatially significant covariates are not necessarily driving temporal shifts in avian distributions.

Accumulating evidence in ecology: Once is not enough.

Many published studies in ecological science are viewed as stand-alone investigations that purport to provide new insights into how ecological systems behave based on single analyses. But it is rare for results of single studies to provide definitive results, as evidenced in current discussions of the "reproducibility crisis" in science. The key step in science is the comparison of hypothesis-based predictions with observations, where the predictions are typically generated by hypothesis-specific models. Repeating this step allows us to gain confidence in the predictive ability of a model, and its corresponding hypothesis, and thus to accumulate evidence and eventually knowledge. This accumulation may occur via an ad hoc approach, via meta-analyses, or via a more systematic approach based on the anticipated evolution of an information state. We argue the merits of this latter approach, provide an example, and discuss implications for designing sequences of studies focused on a particular question. We conclude by discussing current data collection programs that are preadapted to use this approach and argue that expanded use would increase the rate of learning in ecology, as well as our confidence in what is learned.

When habitat matters: Habitat preferences can modulate co-occurrence patterns of similar sympatric species.

Disentangling the role of competition in regulating the distribution of sympatric species can be difficult because species can have different habitat preferences or time use that introduce non-random patterns that are not related to interspecific interactions. We adopted a multi-step approach to systematically incorporate habitat preferences while investigating the co-occurrence of two presumed competitors, morphologically similar, and closely related ground-dwelling birds: the brown tinamou (Crypturellus obsoletus) and the tataupa tinamou (C. tataupa). First, we used single-species occupancy models to identify the main landscape characteristics affecting site occupancy, while accounting for detection probability. We then used these factors to control for the effect of habitat while investigating species co-occurrence. In addition, we investigated species present-time partitioning by measuring the degree of overlap in their activity time. Both species were strictly diurnal and their activity time highly overlapped (i.e., the species are not present-time partitioning). The distribution of the two species varied across the landscape, and they seemed to occupy opposite portions of the study area, but co-occurrence models and species interaction factors suggested that the tinamous have independent occupancy and detection. In addition, co-occurrence models that accounted for habitat performed better than models without habitat covariates. The observed co-occurrence pattern is more likely related to habitat preferences, wherein species segregated by elevation. These results provide evidence that habitat characteristics can play a bigger role than interspecific interactions in regulating co-existence of some species. Therefore, exploring habitat preferences while analyzing co-occurrence patterns is essential, in addition to being a feasible approach to achieve more accurate estimation of parameters reflecting species interactions. Occupancy models can be a valuable tool in such modeling.

Landscape Use and Co-Occurrence Patterns of Neotropical Spotted Cats.

Small felids influence ecosystem dynamics through prey and plant population changes. Although most of these species are threatened, they are accorded one of the lowest research efforts of all felids, and we lack basic information about them. Many felids occur in sympatry, where intraguild competition is frequent. Therefore, assessing the role of interspecific interactions along with the relative importance of landscape characteristics is necessary to understand how these species co-occur in space. Here, we selected three morphologically similar and closely related species of small Neotropical cats to evaluate the roles of interspecific interactions, geomorphometry, environmental, and anthropogenic landscape characteristics on their habitat use. We collected data with camera trapping and scat sampling in a large protected Atlantic forest remnant (35,000 ha). Throughout occupancy modeling we investigated whether these species occur together more or less frequently than would be expected by chance, while dealing with imperfect detection and incorporating possible habitat preferences into the models. We used occupancy as a measure of their habitat use. Although intraguild competition can be an important determinant of carnivore assemblages, in our system, we did not find evidence that one species affects the habitat use of the other. Evidence suggested that proximity to the nature reserve (a more protected area) was a more important driver of Neotropical spotted cats' occurrence than interspecific interactions or geomorphometry and environmental landscape characteristics-even though our entire study area is under some type of protection. This suggests that small felids can be sensitive to the area protection status, emphasizing the importance of maintaining and creating reserves and other areas with elevated protection for the proper management and conservation of the group.

Monitoring for the Management of Disease Risk in Animal Translocation Programmes.

Monitoring is best viewed as a component of some larger programme focused on science or conservation. The value of monitoring is determined by the extent to which it informs the parent process. Animal translocation programmes are typically designed to augment or establish viable animal populations without changing the local community in any detrimental way. Such programmes seek to minimize disease risk to local wild animals, to translocated animals, and in some cases to humans. Disease monitoring can inform translocation decisions by (1) providing information for state-dependent decisions, (2) assessing progress towards programme objectives, and (3) permitting learning in order to make better decisions in the future. Here we discuss specific decisions that can be informed by both pre-release and post-release disease monitoring programmes. We specify state variables and vital rates needed to inform these decisions. We then discuss monitoring data and analytic methods that can be used to estimate these state variables and vital rates. Our discussion is necessarily general, but hopefully provides a basis for tailoring disease monitoring approaches to specific translocation programmes.

State-Dependent Resource Harvesting with Lagged Information about System States.

Markov decision processes (MDPs), which involve a temporal sequence of actions conditioned on the state of the managed system, are increasingly being applied in natural resource management. This study focuses on the modification of a traditional MDP to account for those cases in which an action must be chosen after a significant time lag in observing system state, but just prior to a new observation. In order to calculate an optimal decision policy under these conditions, possible actions must be conditioned on the previous observed system state and action taken. We show how to solve these problems when the state transition structure is known and when it is uncertain. Our focus is on the latter case, and we show how actions must be conditioned not only on the previous system state and action, but on the probabilities associated with alternative models of system dynamics. To demonstrate this framework, we calculated and simulated optimal, adaptive policies for MDPs with lagged states for the problem of deciding annual harvest regulations for mallards (Anas platyrhynchos) in the United States. In this particular example, changes in harvest policy induced by the use of lagged information about system state were sufficient to maintain expected management performance (e.g. population size, harvest) even in the face of an uncertain system state at the time of a decision.

Estimating indices of range shifts in birds using dynamic models when detection is imperfect.

There is intense interest in basic and applied ecology about the effect of global change on current and future species distributions. Projections based on widely used static modeling methods implicitly assume that species are in equilibrium with the environment and that detection during surveys is perfect. We used multiseason correlated detection occupancy models, which avoid these assumptions, to relate climate data to distributional shifts of Louisiana Waterthrush in the North American Breeding Bird Survey (BBS) data. We summarized these shifts with indices of range size and position and compared them to the same indices obtained using more basic modeling approaches. Detection rates during point counts in BBS surveys were low, and models that ignored imperfect detection severely underestimated the proportion of area occupied and slightly overestimated mean latitude. Static models indicated Louisiana Waterthrush distribution was most closely associated with moderate temperatures, while dynamic occupancy models indicated that initial occupancy was associated with diurnal temperature ranges and colonization of sites was associated with moderate precipitation. Overall, the proportion of area occupied and mean latitude changed little during the 1997-2013 study period. Near-term forecasts of species distribution generated by dynamic models were more similar to subsequently observed distributions than forecasts from static models. Occupancy models incorporating a finite mixture model on detection - a new extension to correlated detection occupancy models - were better supported and may reduce bias associated with detection heterogeneity. We argue that replacing phenomenological static models with more mechanistic dynamic models can improve projections of future species distributions. In turn, better projections can improve biodiversity forecasts, management decisions, and understanding of global change biology.

Modeling false positive detections in species occurrence data under different study designs.

The occurrence of false positive detections in presence-absence data, even when they occur infrequently, can lead to severe bias when estimating species occupancy patterns. Building upon previous efforts to account for this source of observational error, we established a general framework to model false positives in occupancy studies and extend existing modeling approaches to encompass a broader range of sampling designs. Specifically, we identified three common sampling designs that are likely to cover most scenarios encountered by researchers. The different designs all included ambiguous detections, as well as some known-truth data, but their modeling differed in the level of the model hierarchy at which the known-truth information was incorporated (site level or observation level). For each model, we provide the likelihood, as well as R and BUGS code needed for implementation. We also establish a clear terminology and provide guidance to help choosing the most appropriate design and modeling approach.

To predict the niche, model colonization and extinction.

Ecologists frequently try to predict the future geographic distributions of species. Most studies assume that the current distribution of a species reflects its environmental requirements (i.e., the species' niche). However, the current distributions of many species are unlikely to be at equilibrium with the current distribution of environmental conditions, both because of ongoing invasions and because the distribution of suitable environmental conditions is always changing. This mismatch between the equilibrium assumptions inherent in many analyses and the disequilibrium conditions in the real world leads to inaccurate predictions of species' geographic distributions and suggests the need for theory and analytical tools that avoid equilibrium assumptions. Here, we develop a general theory of environmental associations during periods of transient dynamics. We show that time-invariant relationships between environmental conditions and rates of local colonization and extinction can produce substantial temporal variation in occupancy-environment relationships. We then estimate occupancy-environment relationships during three avian invasions. Changes in occupancy-environment relationships over time differ among species but are predicted by dynamic occupancy models. Since estimates of the occupancy-environment relationships themselves are frequently poor predictors of future occupancy patterns, research should increasingly focus on characterizing how rates of local colonization and extinction vary with environmental conditions.

Mechanistic understanding of human-wildlife conflict through a novel application of dynamic occupancy models.

Crop and livestock depredation by wildlife is a primary driver of human-wildlife conflict, a problem that threatens the coexistence of people and wildlife globally. Understanding mechanisms that underlie depredation patterns holds the key to mitigating conflicts across time and space. However, most studies do not consider imperfect detection and reporting of conflicts, which may lead to incorrect inference regarding its spatiotemporal drivers. We applied dynamic occupancy models to elephant crop depredation data from India between 2005 and 2011 to estimate crop depredation occurrence and model its underlying dynamics as a function of spatiotemporal covariates while accounting for imperfect detection of conflicts. The probability of detecting conflicts was consistently <1.0 and was negatively influenced by distance to roads and elevation gradient, averaging 0.08-0.56 across primary periods (distinct agricultural seasons within each year). The probability of crop depredation occurrence ranged from 0.29 (SE 0.09) to 0.96 (SE 0.04). The probability that sites raided by elephants in primary period t would not be raided in primary period t + 1 varied with elevation gradient in different seasons and was influenced negatively by mean rainfall and village density and positively by distance to forests. Negative effects of rainfall variation and distance to forests best explained variation in the probability that sites not raided by elephants in primary period t would be raided in primary period t + 1. With our novel application of occupancy models, we teased apart the spatiotemporal drivers of conflicts from factors that influence how they are observed, thereby allowing more reliable inference on mechanisms underlying observed conflict patterns. We found that factors associated with increased crop accessibility and availability (e.g., distance to forests and rainfall patterns) were key drivers of elephant crop depredation dynamics. Such an understanding is essential for rigorous prediction of future conflicts, a critical requirement for effective conflict management in the context of increasing human-wildlife interactions.

Entendimiento Mecánico del Conflicto Humano - Animales Silvestre a través de la Novedosa Aplicación de los Modelos Dinámicos de Ocupación Resumen La depredación de cultivos y ganado por parte de animales silvestres es un conductor principal del conflicto humano - animales silvestres, un problema que amenaza la coexistencia de la gente y la vida silvestre a nivel global. Entender los mecanismos que son la base de los patrones de depredación es la clave para mitigar los conflictos a lo largo del tiempo y el espacio. Sin embargo, la mayoría de los estudios no consideran la detección imperfecta y el reporte de conflictos, lo que puede llevar a la interferencia incorrecta con respecto a los conductores espacio-temporales. Aplicamos modelos dinámicos de ocupación a datos de depredación de cultivos por elefantes en India desde 2005 y hasta 2011 para estimar la incidencia de depredación de cultivos y modelar sus dinámicas como una función de covarianzas espacio-temporales mientras representan la detección imperfecta de los conflictos. La probabilidad de detectar conflictos fue constantemente <1.0 y estuvo influenciada negativamente por la distancia a las carreteras y el gradiente de elevación, promediando 0.08 - 0.56 en los periodos primarios (temporadas agrícolas distintas dentro de cada año). La probabilidad de la incidencia de depredación de cultivos varió desde 0.29 (SE 0.09) hasta 0.96 (SE 0.04). La probabilidad de que los sitios saqueados por elefantes en un periodo primario t no fueran saqueados en un periodo primario t + 1 varió con el gradiente de elevación en diferentes temporadas y estuvo influenciado negativamente por la precipitación promedio y la densidad de la aldea y positivamente por la distancia al los bosques. Los efectos negativos de la variación en la precipitación y la distancia a los bosques explicaron de mejor manera la variación en la probabilidad de que los sitios no saqueados por elefantes en el periodo primario t serían saqueados en el periodo primario t + 1. Con nuestra novedosa aplicación de los modelos de ocupación, separamos a los conductores espacio-temporales de los factores que influyen en cómo son observados, permitiendo así la inferencia más fiable de los mecanismos que son la base de los patrones observados de los conflictos. Encontramos que los factores asociados con el incremento en la disponibilidad y accesibilidad de los cultivos (p. ej.: la distancia a los bosques y los patrones de precipitación) fueron conductores clave en las dinámicas de depredación de cultivos de los elefantes. Tal entendimiento es esencial para una predicción rigurosa de conflictos futuros, un requerimiento crítico para el manejo efectivo de conflictos en el contexto de las crecientes interacción humano - animales silvestres.

The roles of competition and habitat in the dynamics of populations and species distributions.

The role of competition in structuring biotic communities at fine spatial scales is well known from detailed process-based studies. Our understanding of competition's importance at broader scales is less resolved and mainly based on static species distribution maps. Here, we bridge this gap by examining the joint occupancy dynamics of an invading species (Barred Owl, Strix varia) and a resident species (Northern Spotted Owl, Strix occidentalis caurina) in a 1000-km study area over a 22-year period. Past studies of these competitors have focused on the dynamics of one species at a time, hindering efforts to parse out the roles of habitat and competition and to forecast the future of the resident species. In addition, while these studies accounted for the imperfect detection of the focal species, no multi-season analysis of these species has accounted for the imperfect detection of the secondary species, potentially biasing inference. We analyzed survey data using models that combine the general multistate-multi-season occupancy modeling framework with autologistic modeling, allowing us to account for important aspects of our study system. We found that local extinction probability increases for each species when the other is present; however, the effect of the invader on the resident is greater. Although the species prefer different habitats, these habitats are highly correlated at the patch scale, and the impacts of invader on the resident are greatest in patches that would otherwise be optimal. As a consequence, competition leads to a weaker relationship between habitat and Northern Spotted Owl occupancy. Colonization and extinction rates of the invader are closely related to neighborhood occupancy, and over the first half of the study the availability of colonists limited the rate of population growth. Competition is likely to exclude the resident species, both through its immediate effects on local extinction and by indirectly lowering colonization rates as Northern Spotted Owl occupancy declines. Our analysis suggests that dispersal limitation affects both the invasion dynamics and the scale at which the effects of competition are observed. We also provide predictions regarding the potential costs and benefits of managing Barred Owl populations at different target levels.

Using imputation and mixture model approaches to integrate multi-state capture-recapture models with assignment information.

In this article, we first extend the superpopulation capture-recapture model to multiple states (locations or populations) for two age groups., Wen et al., (2011; 2013) developed a new approach combining capture-recapture data with population assignment information to estimate the relative contributions of in situ births and immigrants to the growth of a single study population. Here, we first generalize Wen et al., (2011; 2013) approach to a system composed of multiple study populations (multi-state) with two age groups, where an imputation approach is employed to account for the uncertainty inherent in the population assignment information. Then we develop a different, individual-level mixture model approach to integrate the individual-level population assignment information with the capture-recapture data. Our simulation and real data analyses show that the fusion of population assignment information with capture-recapture data allows us to estimate the origination-specific recruitment of new animals to the system and the dispersal process between populations within the system. Compared to a standard capture-recapture model, our new models improve the estimation of demographic parameters, including survival probability, origination-specific entry probability, and especially the probability of movement between populations, yielding higher accuracy and precision.

Determinants of extinction-colonization dynamics in Mediterranean butterflies: the role of landscape, climate and local habitat features.

Many species are found today in the form of fragmented populations occupying patches of remnant habitat in human-altered landscapes. The persistence of these population networks requires a balance between extinction and colonization events assumed to be primarily related to patch area and isolation, but the contribution of factors such as the characteristics of patch and matrix habitats, the species' traits (habitat specialization and dispersal capabilities) and variation in climatic conditions have seldom been evaluated simultaneously. The identification of environmental variables associated with patch occupancy and turnover may be especially useful to enhance the persistence of multiple species under current global change. However, for robust inference on occupancy and related parameters, we must account for detection errors, a commonly overlooked problem that leads to biased estimates and misleading conclusions about population dynamics. Here, we provide direct empirical evidence of the effects of different environmental variables on the extinction and colonization rates of a rich butterfly community in the western Mediterranean. The analysis was based on a 17-year data set containing detection/nondetection data on 73 butterfly species for 26 sites in north-eastern Spain. Using multiseason occupancy models, which take into account species' detectability, we were able to obtain robust estimates of local extinction and colonization probabilities for each species and test the potential effects of site covariates such as the area of suitable habitat, topographic variability, landscape permeability around the site and climatic variability in aridity conditions. Results revealed a general pattern across species with local habitat composition and landscape features as stronger predictors of occupancy dynamics compared with topography and local aridity. Increasing area of suitable habitat in a site strongly decreased local extinction risks and, for a number of species, both higher amounts of suitable habitat and more permeable landscapes increased colonization rates. Nevertheless, increased topographic variability decreased the extinction risk of bad dispersers, a group of species with significantly lower colonization rates. Our models predicted higher sensitivity of the butterfly assemblages to deterministic changes in habitat features rather than to stochastic weather patterns, with some relationships being clearly dependent on the species' traits.