Objectives: The central question is: do corridors provide connectivity for wildlife in the real world? Using 20 landscapes in Europe and the Americas, we will deploy a natural experiment approach [23], whereby each landscape contains three types of habitat configuration: a large intact natural area, isolated patches, and patches connected by a corridor, (Fig. 1, 2). Using long-term gene flow as a robust measure of isolation impact and corridor success, and focusing on mammals, which are strongly affected by fragmentation [24], we will answer the following questions.
Q1. What characteristics of corridors determine their success? We will identify the corridor characteristics that most strongly influence success (gene flow) for a range of mammals. We hypothesize a strong non-linear decline in success with increasing corridor length, reflecting the skewed distribution of dispersal distances within species. We also expect success will drop steeply as corridor width falls below a threshold, with the threshold determined by species traits. This will be the first study of the success of large corridors for multiple species, using a robust response variable.
Q2. What types of species benefit most from corridors? We will analyze how species differ in the degree to which corridors enhance gene flow, and relate these to species’ traits. We hypothesize that species that are bigger, are habitat specialists, or have greater dispersal abilities, relative brain size or reproductive rate will benefit more from corridors. This will allow generalization to a wide range of mammal species not covered in this project.
Q3. Which connectivity models best predict corridor success? We hypothesize that more complex models will perform best as they represent better the details of animal ecology, corridor characteristics, and landscape influences. We expect accuracy gains for more complex models to be substantial, allowing us to explore the most effective corridor designs for a range of species types.
 

Methodology and approach 
Natural experiments involve overlaying a robust statistical design on a system that has had manipulations which are out of the researcher’s control, but which can be used as experimental treatments. They fall between true manipulative experiments and observational studies. As they can cover larger scales than the former and are more rigorous than the latter, they have been central to key insights in ecology and other sciences [23]. We will study ~4 focal species in each of ~20 landscapes in Europe, the Americas (Table 1), and perhaps elsewhere. As summarized in Fig. 2, each landscape contains three types of habitat configuration, which will be our treatments: isolated habitat patches, >1 pair of patches connected by a corridor, and a large intact natural area (Fig. 1). The landscapes are ideal [11] replicates for the natural experiment because (1) corridor widths vary from 0.16 to 2.5km and lengths from 2 to 25km; (2) the configurations have been in place for ≥40 years, so genetic patterns will reflect landscape structure; (3) to address recommendations that fragmentation studies should control for habitat area [13], we will match sizes of isolated and connected patches as well as their inter-patch distances; and (4) the focal species span a range of traits. With previous funding, Gregory visited 93 potential landscapes in 37 countries, confirmed the presence of suitable focal species, and met with potential partners, identifying 86 landscapes with de facto corridors. Globally, few corridors have been implemented, all <20 yrs ago, which is too short a time for genetic patterns to reflect the influence of the corridor [25]. Our de facto corridors comprise old, remnant habitat. Importantly, the matrix in each landscape comprises predominantly agricultural and urban land, presenting a strong contrast to the natural habitat areas, which will influence mammal dispersal decisions and behaviors, and the costs incurred. We are selecting landscapes (Table 1) to include even representation of corridor widths, lengths, and road types, to work efficiently close to our home institutions, and to benefit from committed local partners who will provide essential help. 

Mammal species. From species suggested by local partners (Table 1), we will select ~4 mammals in each landscape that occur in the focal habitat, avoid the matrix, and represent a variety of traits putatively related to corridor use and benefits. These comprise two orders with quite different characteristics: rodents and carnivores. Although the constrained taxonomy limits generality, it allows inferences to be drawn that are not confounded by large phylogenetic differences. Trait information will be drawn from available databases [e.g., 26-29]. Traits relevant to our hypotheses include body size, diet type, habitat breadth, age at sexual maturity (a measure of reproductive rate), brain mass, and dispersal ability (estimated from other traits [12]). 

Measuring effective movement. We will use genetic similarity to measure relative gene flow in each corridor. Although genetic equilibrium probably never fully occurs in any human-altered landscape, genetic differentiation is evident in as few as three generations after perturbations [30], and our habitat configurations have been stable for >20 generations. We will use live traps, hair snares, and faecal sampling to collect tissue samples of each focal species in each habitat patch (Fig.2: A,B,M,N) and in locations in the intact area that are the same distance apart as the patches in the other configurations (Fig.2: Y,Z). We will also sample at key locations in the corridor (e.g. near roads or villages) to identify barriers to gene flow. We will identify samples to species or species group (hair and faeces) to select those for DNA extraction. We will amplify each DNA sample at ≥50 SNPs (single nucleotide polymorphisms) and at 6-10 microsatellite markers (to control for marker bias). We probably need ≈30 individuals per patch which, after post-hoc screening for close relatives, should yield >20 samples. We will take a mobile sequencing lab (MinIon) to each field site, allowing us to ascertain promptly when we have an adequate sample size to detect differentiation, thus optimizing our sampling efficiency. Gregory has extensive experience in all these methods [31]. Libraries of 2-31 SNPs are available for most focal species, and we will develop additional SNPs [32] as needed to reach 50-100 SNPs; the exact number will be based on the power of the selected panel to resolve genetic similarity across the habitat configuration, with an emphasis on surveying more individuals as opposed to more markers [33]. 

The Corridor Success Index. CSI [15], measures gene flow provided by the corridor for each species in each landscape. The CSI equation (Fig. 2) rescales the genetic similarity between populations in the corridor-connected patches. CSI is the amount by which gene flow through the corridor exceeds gene flow across the matrix, standardised to the gene flow across the intact habitat configuration. A value near 0 indicates the species gains no benefit from the corridor (no greater gene flow than between isolated patches), a value close to 1 shows gene flow in the corridor similar to intact habitat, and intermediate values indicate the degree to which the corridor enhances gene flow. We hypothesize that even for species that are likely to be able to cross the matrix to some extent, the corridor will still enhance connectivity, and so CSI will be positive, albeit near 0.  

We will use random forests (RF) models to identify which (Q1) corridor characteristics (length, mean width, road density, years since fragmentation) and (Q2) species traits (body size, diet, etc.) promote CSI. A categorical “landscape” variable will account for dependence among observations within each landscape in a Mixed Effect RF approach [34]. Similarly, a categorical “species” variable will account for non-independence where the same species are sampled in different landscapes. RF does not assume any parametric relationship between response variables and predictors or that residuals are independently and identically distributed. Furthermore, RF’s variable importance (VI) values are robust to multicollinearity and RF can model relationships even when predictors greatly outnumber observations [35]. We will examine interactions among the important predictors using partial dependence plots [36]. In addition, for each corridor/species combination with a low CSI, we will use a Hierarchical Bayesian formulation [37] of boundary detection analysis with BoundarySEER software to identify segments of the corridor with the greatest discontinuity in gene frequencies. Post hoc permutation procedures will measure congruence of these segments with putative barriers such as a road. Because RF does not formally test hypotheses, we will use a 2-stage process to test our hypotheses about the effect of corridor characteristics and species traits, e.g., that large-bodied species benefit more from corridors: (1) Calculate each predictor’s Conditional Variable Importance (CVI)38 (i.e., VI after accounting for multicollinearity), rank predictors in order of CVI, and create reduced RF models by sequential backwards elimination, stopping when mean squared error starts to increase39. If a predictor (e.g., body mass) is not retained in the final model, the hypothesis will be rejected (body mass has no independent influence on CSI). (2) If the predictor is retained, a 1-tailed t-test will determine if, say, large-bodied species have higher CSI than small-bodied ones.

Table 1. Landscapes and Focal Species. Habitat configuration has been stable ≥20 generations of the focal species. In each landscape, the matrix is dominated by human land uses. Width is at the narrowest portion of each corridor; mean width of each corridor is 50% to 200% wider.​