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How the complexity of food webs depends on environmental variables is a long‐standing ecological question. It is unclear though how food‐chain length should vary with adaptive evolution of the constitutive species. Here we model the evolution of species colonisation rates and its consequences on occupancies and food‐chain length in metacommunities. When colonisation rates can evolve, longer food‐chains can persist. Extinction, perturbation and habitat loss all affect evolutionarily stable colonisation rates, but the strength of the competition‐colonisation trade‐off has a major role: weaker trade‐offs yield longer chains. Although such eco‐evo dynamics partly alleviates the spatial constraint on food‐chain length, it is no magic bullet: the highest, most vulnerable, trophic levels are also those that least benefit from evolution. We provide qualitative predictions regarding how trait evolution affects the response of communities to disturbance and habitat loss. This highlights the importance of eco‐evolutionary dynamics at metacommunity level in determining food‐chain length. [ABSTRACT FROM AUTHOR]

Mountain headwater streams have emerged as important climate refuges for native cold‐water species due to their slow climate velocities and extreme physical conditions that inhibit non‐native invasions. Species persisting in refuges often do so as fragmented, relict populations from broader historical distributions that are subject to ongoing habitat reductions and increasing isolation as climate change progresses. Key for conservation planning is determining where remaining populations will persist and how habitat restoration strategies can improve biological resilience to enhance the long‐term prospects for species of concern. Studying bull trout, a headwater species in the northwestern USA, we developed habitat occupancy models using a data set of population occurrence in 991 natal habitat patches with a suite of novel geospatial covariates derived from high‐resolution hydroclimatic scenarios and other sources representing watershed and instream habitat conditions, patch geometry, disturbance, and biological interactions. The best model correctly predicted bull trout occupancy status in 82.6% of the patches and included effects for: patch size estimated as habitat volume, extent of within‐patch reaches <9°C mean August temperature, distance to nearest occupied patch, road density, invasive brook trout prevalence, patch slope, and frequency of high winter flows. The model was used to assess 16 scenarios of bull trout occurrence within the study streams that represented a range of restoration strategies under three climatic conditions (baseline, moderate change, and extreme change). Results suggested that regional improvements in bull trout status were difficult to achieve in realistic restoration strategies due to the pervasive nature of climate change and the limited extent of restoration actions given their high costs. However, occurrence probabilities in a subset of patches were highly responsive to restoration actions, suggesting that targeted investments to improve the resilience of some populations may be contextually beneficial. A possible strategy, therefore, is focusing effort on responsive populations near more robust population strongholds, thereby contributing to local enclaves where dispersal among populations further enhances resilience. Equally important, strongholds constituted a small numerical percentage of patches (5%–21%), yet encompassed the large majority of occupied habitat by volume (72%–89%) and their protection could have significant conservation benefits for bull trout. [ABSTRACT FROM AUTHOR]

If the deterministic threats of fragmentation can be stopped or reversed, species may find opportunities to disperse between patches and reduce the risk of extinction. In order to realise these opportunities and apply them to conservation programmes, it is necessary to understand the dynamics involved and to estimate which capacity is high enough to sustain a population at the landscape level. In a regional population consisting of several subpopulations, the incidence function model (IFM) is a stochastic, spatially-realistic patch occupancy model which can be applied using few parameters. With this model one can simulate and manipulate a patch network for a species. In IFM, the extinction probability is assumed to be proportional to local population size which in turn is assumed to be proportional to the local patch area. Although, the basic area of patches is of importance, influence from the geometric shape of patches may be equally or more important to determine potential incidence of a species in a particular patch. Basic area measurements might overestimate the probability of occupancy and/or capacity of a certain patch network to sustain a metapopulation. One applicable method to use in dealing with regional dynamics in fragmented landscapes is metapopulation capacity; derived from metapopulation theory, this method can be used to rank different patch networks. In our study, we examine if there is any difference in occupancy level and capacity between four different area scenarios. This allows us to determine if the basic area measurement of patches can result in a biased estimation of population viability in a specific landscape. It is concluded that perimeter-area related measures of patch size combined with capacity could be a more important measure for estimation of population dynamics and impact of landscape changes compared to basic area measurement and occupancy levels. [ABSTRACT FROM AUTHOR]

1. A recent and controversial topic in landscape ecology is whether populations of species respond to habitat fragmentation in a general fashion. Empirical research has provided mixed support, resulting in controversy about the use of general rules in landscape management. Rather than simply assessing post hoc whether individual species follow such rules, a priori testing could shed light on their accuracy and utility for predicting species response to landscape change. 2. We aim to create an a priori model that predicts the presence or absence of multiple species in habitat patches. Our goal is to balance general theory with relevant species life-history traits to obtain high prediction accuracy. To increase the utility of this work, we aim to use accessible methods that can be applied using readily available inexpensive resources. 3. The classification tree patch-occupancy model we create for birds is based on habitat suitability, minimum area requirements, dispersal potential of each species and overall landscape connectivity. 4. To test our model we apply it to the South East Queensland region, Australia, for 17 bird species with varying dispersal potential and habitat specialization. We test the accuracy of our predictions using presence–absence information for 55 vegetation patches. 5. Overall we achieve Cohen’s kappa of 0·33, or ‘fair’ agreement between the model predictions and test data sets, and generally a very high level of absence prediction accuracy. Habitat specialization appeared to influence the accuracy of the model for different species. 6. We also compare the a priori model to the statistically derived model for each species. Although this ‘optimal model’ generally differed from our original predictive model, the process revealed ways in which it could be improved for future attempts. 7. Synthesis and applications. Our study demonstrates that ecological generalizations alongside basic resources (a vegetation map and some species-specific information) can provide conservative accuracy for predicting species occupancy in remnant vegetation patches. We show that the process of testing and developing models based on general rules could provide basic tools for conservation managers to understand the impact of current or planned landscape change on wildlife populations. [ABSTRACT FROM AUTHOR]

Patch occupancy models provide a simple phenomenological approach to evaluating ecological questions on a metapopulation scale. In this study, I use and modify a patch occupancy model to evaluate the effects of synchronous extinctions correlated with flooding and patch-size-dependent migration on the regional dynamics of a neotropical beetle, Cephaloleia fenestrata. Various methods have been used to evaluate patch occupancy models. Previous authors most commonly have evaluated patch occupancy models by logistic regression of incidence functions. Likelihoods produced from regression methods, however, neglect autocorrelation in spatial occupancy patterns even though spatial auto-correlation is common in ecological data. In this study, I used a Monte Carlo method of model evaluation, which accounts for spatial autocorrelation. Results suggest that patches undergo synchronous extinctions correlated with flooding and this affects regional dynamics of C. fenestrata. Immigration probability positively correlated with patch size, and emigration probability negatively correlated with patch size, also affecting C. fenestrata regional dynamics. The patch occupancy pattern was positively spatially autocorrelated at only two of the sites, but was nearly significant at another. The logistic regression method was a reasonable alternative to the Monte Carlo method for model evaluation. Other model evaluation methods (fits to model development data, proportion of occupied patches, and turnover rates) were inconsistent (at best) quantitative measures of a model's fit to independent data. When used to select among competing models, however, the fits to model development data were reasonably good indicators of fits to independent data. Given the increased complexity involved in using the Monte Carlo method, the simpler logistic regression method may be a preferable alternative, especially when spatial autocorrelation is minimal. [ABSTRACT FROM AUTHOR]