Data analysis

Species richness and complementarity within the CA network

To obtain a potential richness map, we overlapped the suitable range maps of the 122 target-mammals and summed for each cell of the country’s grid the species predicted to have suitable climatic conditions therein. The map of Mozambique’s CA was also intersected with the coun-try’s grid. Grid cells overlapping with CA were considered “protected cells”, and grid cells outside the CA network were classified as “non-protected cells”.

To assess the number of species within the existing CA network, we overlaid the CA map with the potential richness map. For each conservation area and the complete set of “protected cells”, we extracted the potential diversity and identity of species therein. We calculated the average potential richness and standard deviation for both the “protected cells” and the “protected cells”. Statistical differences in total species richness between “protected and non-protected cells were tested with non-parametric Kruskal Wallis tests.

Complementarity between the existing CA was assessed as high or low redundancy in species diversity, by calculating similarities in species composition among the conservation areas. The assessment was based on a cluster analysis using the Jaccard similarity coefficient.

The Jaccard coefficient measures spatial turnover by comparing all pair sites, clustering similar sites until a complete dendrogram is constructed (Magurran,2004).

Representativeness of the CA network and protection targets

The extent of the suitable range for each species was measured as the number of the coun-try’s grid cells overlapping with the species’ suitable range. In addition, for each species, we determined their “protected range” as the extent of their suitable range within the “protected cells”. Here, we determined the representativeness of the CA network, for each species, as the proportion of the protected range in relation to the suitable range.

To assess if a species is adequately protected, we followed thresholds proposed byRodrigues et al.(2004). Thresholds established based on the proportion of range covered by CA networks have been used extensively (Butchart et al.,2015;Gonz´alez-Maya et al.,2015, e.g.). A species with more restricted ranges should have a more significant percentage of its range protected, i.e.

within conservation areas. Accordingly, a 100% protection target was set for species with ranges under 1000 km2, and a 10% protection target was set for species with ranges above 250000 km2.

A linear decline in the target was established for ranges between these extremes (Rodrigues et al., 2004). Species presenting a “protected range” lower than these protection targets set a priori were identified as “under-protected species”. Additionally, species not represented in any conservation area were considered “gap species” (Rodrigues et al.,2004).

Range size was previously identified as an important predictor of extinction risk of terrestrial mammals (Crooks et al., 2017;Pimm et al.,2014). Species with small ranges tend to be more vulnerable to adverse natural events and anthropogenic activities (Gaston, 2003; Rodrigues et al., 2004). Accordingly, we also considered species with restricted ranges within Mozam-bique to be priority species for conservation. The 122 species were grouped by quartiles over the size of their suitable range. Accordingly, four groups of species were formed: restricted-range group, with species with suitable ranges within the lowest quartile; restricted-to-moderate range group, with species within the second quartile; moderate-to-wide range group, with species within the third quartile; and wide-range species, for species within the fourth quartile. Po-tential richness maps were also created for the set of “under-protected species” and the set of

“restricted-range species”. For both sets of species, we calculated the average potential rich-ness and standard deviation of the “protected cells” and the “non-protected cells”. We tested for statistical differences between protected and non-protected cells with non-parametric Kruskal Wallis tests.

To examine the overall congruence of the number of species between the maps of total species richness, of “under-protected species” richness and of “restricted-range species”

rich-ness, we used a modified t-test that can be used for the correlation of spatial variables (Spatial-Pack package, R environment;Vallejos et al.,2018).

Species conservation under climate change and human pressure

Climate change can shift a species’ suitable climatic conditions to places where the species would be less adequately protected or exposed to greater human pressure. For this reason, we determined species richness changes under future climatic conditions. A map of suitability changes was produced by comparing, for each species, their current and future suitable ranges and quantifying the potential number of species gained or lost in each of the country’s grid cells, assuming no dispersal. In addition, based on the suitable future ranges, we measured the extent and representativeness of the existing CA network for protecting species and their suitable future ranges, as in the previous section.

“Human pressure” in Mozambique was quantified by averaging the values of HF and pop-ulation densities across the entire country, inside the conservation areas, within the species’

suitable ranges, and within the species’ protected range, for current and future projections.

Priority zones for conservation

Priority zones to improve mammal conservation were projected from non-protected areas with high richness and high species rarity, as well as with low human pressure and climate change impact.

First, we determined “Centres of protected high richness” by selecting the 25% of non-protected cells with the highest number of “under-non-protected” species, and “Centers of rarity” by selecting the 25% of the non-protected cells with the highest number of restricted-range species.

We then merged these Centres’ cells and selected the 30 cells with both low human pressure and low change in climate suitability (i.e., lower potential loss of species). We only considered cells with HF values below 7 (Venter et al.,2016) and with values of HPD predictions for 2020 below the current country’s average (37.73 hab./km2;The World Bank,2017).

Thirdly, we created 0.3º width buffers around these top 30 cells using the “gBuffer” function available in the R package “rGeos” (Bivend et al.,2017). Intersecting buffers were merged, and the resulting spatial areas were considered to represent “priority conservation zones”. Climate conditions and human pressure, under current and future projections, were measured (mean and standard deviation) in these priority zones for conservation.

To evaluate the effectiveness of the proposed priority zones, we estimated the gain from the hypothetical creation of one to all priority zones in the country. For each hypothetical scenario of creating an “X” number of new conservation areas, we randomly extracted “X”

zones from the set of “priority conservation zones” and repeated this process 2000 times to obtain all possible combinations of “priority conservation zones”. Next, for each combination of “priority conservation zones” selected, we calculated the potential gain in species protected range and the number of species that would be considered protected, given the protection targets established in “Data analysis” - Section 4.2.3. Finally, we ranked the “priority conservation zones” considering the total number of restricted-range species, under-protected species, and the overall number of species represented.

4.3 Results