Robert A. Briers

Incorporating connectivity into reserve selection procedures

Methods for selecting sites to be included in reserve networks generally neglect the spatial location of sites, often resulting in highly fragmented networks. This restricts the possibility of dispersal between sites, which for many species may be essential for long-term persistence. Here I describe iterative reserve selection algorithms which incorporate considerations of reserve connectivity and evaluate their performance using a data set for macroinvertebrates in ponds. Methods where spatial criteria were only invoked when ties between sites occurred did not perform significantly better than a simple greedy algorithm in terms of reserve connectivity. An algorithm based on a composite measure of species added and changes in reserve connectivity produced a reserve network with higher connectivity, but needed more sites to represent all species. A trade-off between connectivity and efficiency may be inevitable, but the costs in terms of efficiency may be justified if long-term persistence of species is more likely.

Incorporating spatial criteria in optimum reserve network selection

Considering the spatial location of sites that are to be selected for inclusion in a protected reserve network may be necessary to facilitate dispersal and long–term persistence of species in the selected sites. This paper presents an integer programming (IP) approach to the reserve network selection problem where spatial considerations based on intersite distances are taken into account when selecting reserve sites. The objective is to reduce the fragmentation of preserved sites and design a compact reserve network. Two IP formulations are developed which minimize the sum of pairwise distances and the maximum intersite distance between all sites in the reserve network, respectively, while representing all species under consideration. This approach is applied to a pond invertebrate dataset consisting of 131 sites containing 256 species in Oxfordshire, UK. The results show that significant reductions in reserve fragmentation can be achieved, compared with spatially unrestricted optimum reserve selection, at the expense of a small loss in reserve efficiency.

Selection of a minimum-boundary reserve network using integer programming

In the conservation literature, heuristic procedures have been employed to incorporate spatial considerations in reserve network selection with the presumption that computationally convenient optimization models would be too difficult or impossible to formulate. This paper extends the standard set-covering formulation to incorporate a particular spatial selection criterion, namely reducing the reserve boundary to the extent possible, when selecting a reserve network that represents a set of target species at least once. Applying the model to a dataset on the occurrence of breeding birds in Berkshire, UK, demonstrated that the technique resulted in significant reductions in reserve boundary length relative to solutions produced by the standard set–covering formulation. Computational results showed that moderately large reserve network selection problems could be solved without issue. Alternative solutions may be produced to explore trade–offs between boundary length, number of sites required or alternative criteria.

Optimal Selection of a Connected Reserve Network

Spatial considerations are important in conservation reserve design. A particularly important spatial requirement is the connectivity of selected sites. Direct connections between reserve sites increase the likelihood of species persistence by allowing dispersal and colonization of other areas within the network without species having to leave the reserve. The conventional set-covering and maximal-covering formulations of the reserve selection problem assume that species representation is the only criterion in site selection. This approach usually results in a small but highly fragmented reserve, which may not be desirable. We present a linear integer programming framework incorporating spatial contiguity as an additional site selection criterion. An empirical application to a data set on the occurrence of breeding birds in Berkshire, United Kingdom, demonstrates that site connectivity requires a significantly larger reserve. Incorporation of spatial criteria increases the computational complexity of the problem. To overcome this, we use a two-stage procedure where the original sites are aggregated first and an optimum solution is determined for the aggregate sites. Then, site selection is restricted to original sites included in the aggregate solution and a connected reserve is determined. In this particular application the above procedure generated a significantly more efficient reserve than a heuristic selection.

Flight activity of adult stoneflies in relation to weather

Abstract. 1. Dispersal of adult aquatic insects between streams may have important consequences for local and regional population dynamics, but little is known about how dispersal is affected by weather conditions.

Population turnover and habitat dynamics in Notonecta (Hemiptera: Notonectidae) metapopulations

Abstract Simple metapopulation models assume that local populations occur in patches of uniform quality habitat separated by non-habitat. However field metapopulations tend to show considerable spatial and temporal variation in patch quality, and hence probability of occupancy. This may have implications for the adequacy of simple metapopulation models in describing and predicting regional population dynamics of natural systems. This study investigated the effects of habitat characteristics on landscape-scale occupancy dynamics of two species of backswimmer (Notonecta, Hemiptera: Notonectidae) in small freshwater ponds. The results demonstrated clear links between habitat, pond occupancy and population turnover, particularly local extinction. There were considerable changes in the habitat of individual ponds between years, but local changes were not spatially correlated and the frequency distribution of habitat conditions at the landscape level remained similar in different years. Stable occupancy levels of Notonecta species appears to result from a balance of the rates of creation and loss of suitable habitat due to spatially uncorrelated habitat change. Systems such as this, where turnover is driven by habitat dynamics, demonstrate the potential value of incorporating the dynamics of habitat change into metapopulation models. Such developments are likely to improve predictions of landscape-scale occupancy dynamics, whilst also allowing patch-level predictions of occupancy, based on local habitat conditions.

Quantifying two-dimensional dichromatic patterns using a photographic technique: case study on the shore crab (Carcinus maenas L.)

Contrasting patterns of pigmentation, such as those associated with crypsis and aposematism, are common in many taxa. In order to determine why patterning varies among individuals or populations, it is important to quantify how these patches of pigment are arranged. Here we present a simple technique for measuring areas of pigmentation as well as their spatial distribution, and demonstrate its application to the study of substrate-associated patterning in shore crabs (Carcinus maenas L.). The results, based on a virtual grid laid over digital images of crab carapaces, allow for correlations to be made among sample populations. The technique, or variations of it, can be applied to any situation where two-dimensional dichromatic patterns need to be quantified.

Making predictions of mangrove deforestation: a comparison of two methods in Kenya

Deforestation of mangroves is of global concern given their importance for carbon storage, biogeochemical cycling and the provision of other ecosystem services, but the links between rates of loss and potential drivers or risk factors are rarely evaluated. Here, we identified key drivers of mangrove loss in Kenya and compared two different approaches to predicting risk. Risk factors tested included various possible predictors of anthropogenic deforestation, related to population, suitability for land use change and accessibility. Two approaches were taken to modelling risk; a quantitative statistical approach and a qualitative categorical ranking approach. A quantitative model linking rates of loss to risk factors was constructed based on generalized least squares regression and using mangrove loss data from 1992 to 2000. Population density, soil type and proximity to roads were the most important predictors. In order to validate this model it was used to generate a map of losses of Kenyan mangroves predicted to have occurred between 2000 and 2010. The qualitative categorical model was constructed using data from the same selection of variables, with the coincidence of different risk factors in particular mangrove areas used in an additive manner to create a relative risk index which was then mapped. Quantitative predictions of loss were significantly correlated with the actual loss of mangroves between 2000 and 2010 and the categorical risk index values were also highly correlated with the quantitative predictions. Hence, in this case the relatively simple categorical modelling approach was of similar predictive value to the more complex quantitative model of mangrove deforestation. The advantages and disadvantages of each approach are discussed, and the implications for mangroves are outlined.